Package: r-bioc-affxparser Architecture: arm64 Version: 1.84.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3492 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-r.oo, r-cran-r.utils, r-bioc-affymetrixdatatestfiles Filename: pool/dists/resolute/main/r-bioc-affxparser_1.84.0-1.ca2604.1_arm64.deb Size: 1004176 MD5sum: ccd6d2a0fd0c1352c33ec19237ae1eee SHA1: 838ecd5018d43aece4a0af930c82e3cc9b7d2b8c SHA256: 1b16f9c415a7655b6d7b542a52438191be4059fadb93fbf16feee6dc3bb7b66a SHA512: 977ad805195b141d69e18cc13b13704dcb7b8c1fe5bd9121711ae2cde6e536e1a45ef30c3c3cb6cfa9338023e1eeaeb9991e48344d98ed03a8998c25def8627a Homepage: https://cran.r-project.org/package=affxparser Description: Bioc Package 'affxparser' (Affymetrix File Parsing SDK) Package for parsing Affymetrix files (CDF, CEL, CHP, BPMAP, BAR). 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It is able to perform "bi-clustering" and simultaneously cluster genes into gene modules and cells into cell subpopulations. It also contains DecontX, a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. A variety of scRNA-seq data visualization functions is also included. 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OpenBabel is an open source cheminformatics toolbox that includes utilities for structure format interconversions, descriptor calculations, compound similarity searching and more. ChemineOB aims to make a subset of these utilities available from within R. For non-developers, ChemineOB is primarily intended to be used from ChemmineR as an add-on package rather than used directly. 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Its latest version contains functions for efficient processing of large numbers of molecules, physicochemical/structural property predictions, structural similarity searching, classification and clustering of compound libraries with a wide spectrum of algorithms. In addition, it offers visualization functions for compound clustering results and chemical structures. Package: r-bioc-chipseq Architecture: arm64 Version: 1.62.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3590 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomicranges, r-bioc-shortread, r-cran-lattice Suggests: r-bioc-bsgenome, r-bioc-genomicfeatures, r-bioc-txdb.mmusculus.ucsc.mm9.knowngene, r-bioc-bsgenome.mmusculus.ucsc.mm9, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/resolute/main/r-bioc-chipseq_1.62.0-1.ca2604.1_arm64.deb Size: 2590890 MD5sum: 2a6b702437dbc72318657fe4deaac7c2 SHA1: b1632edb303b8ca3690ef6e30eadba4e1e139350 SHA256: 3648c26c60686bbd99dea114ab20163b4a1505fdd7ff1ffadc25c186d6cc42a6 SHA512: e2427cb3b797311d10775fcba96360782771627ca6d46b77d8366567b834344779c48a323275e3a24281a336d6da7adf8002ec3afa12eb4746488dcd67b152b7 Homepage: https://cran.r-project.org/package=chipseq Description: Bioc Package 'chipseq' (chipseq: A package for analyzing chipseq data) Tools for helping process short read data for chipseq experiments. Package: r-bioc-chromvar Architecture: arm64 Version: 1.34.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1760 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1248716 MD5sum: 3799a9169f0c8324950c3461b9936815 SHA1: d1b95076d4f4835e338b9d2b5b608695514f6481 SHA256: 2157c08a55b1996028173bdc2e8105c907cefee3d837872c7ccf34a2c4045544 SHA512: 6eb597e2863876c5295b6b0e8617a2d347ffdad301ddf484591c900a82c811561560485c0af625cd7a04efb3ddf4bc8fba08f64c41d5577db0e21898e5358a90 Homepage: https://cran.r-project.org/package=chromVAR Description: Bioc Package 'chromVAR' (Chromatin Variation Across Regions) Determine variation in chromatin accessibility across sets of annotations or peaks. Designed primarily for single-cell or sparse chromatin accessibility data, e.g. from scATAC-seq or sparse bulk ATAC or DNAse-seq experiments. Package: r-bioc-cigarillo Architecture: arm64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 866 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-biostrings Suggests: r-bioc-rsamtools, r-bioc-genomicalignments, r-bioc-rnaseqdata.hnrnpc.bam.chr14, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-cigarillo_1.2.0-1.ca2604.1_arm64.deb Size: 339964 MD5sum: 43c937cef712eacd2bfa80c0e128b817 SHA1: 46a5472baf309c0003473fb02f66a7c6b68077bc SHA256: 412254e3795ade6b92ba2fcf2fdad8cc929e23943996825fa542ec8e8336984f SHA512: 1e508eca1707cc6b0a97f1f4dc144490802123a0d01aa6d7da4625d50489a91af1d35399d83489440d5658505e536c4c3ab8fcb143c0367a7a1367cd040336b7 Homepage: https://cran.r-project.org/package=cigarillo Description: Bioc Package 'cigarillo' (Efficient manipulation of CIGAR strings) CIGAR stands for Concise Idiosyncratic Gapped Alignment Report. CIGAR strings are found in the BAM files produced by most aligners and in the AIRR-formatted output produced by IgBLAST. The cigarillo package provides functions to parse and inspect CIGAR strings, trim them, turn them into ranges of positions relative to the "query space" or "reference space", and project positions or sequences from one space to the other. Note that these operations are low-level operations that the user rarely needs to perform directly. More typically, they are performed behind the scene by higher-level functionality implemented in other packages like Bioconductor packages GenomicAlignments and igblastr. Package: r-bioc-clusterexperiment Architecture: arm64 Version: 2.32.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 17868 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_arm64.deb Size: 13094664 MD5sum: 2de5be943c403421e77c9a11221d2199 SHA1: 4959e22a9bfb41a90e762481dd679b360d14780a SHA256: f0493f47b139c0491e09160b60850f61886013c805c440bb5b26adffe565653e SHA512: c4afefb8919133fe206fdcdfd730ca01cb12f7dd3e03a5d7500adc10884168a496b1fc6a09bac2dd5723ff64549e72ff39e78657333866b258a5aa1320554486 Homepage: https://cran.r-project.org/package=clusterExperiment Description: Bioc Package 'clusterExperiment' (Compare Clusterings for Single-Cell Sequencing) Provides functionality for running and comparing many different clusterings of single-cell sequencing data or other large mRNA Expression data sets. Package: r-bioc-csaw Architecture: arm64 Version: 1.46.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2296 Depends: libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), libgcc-s1 (>= 3.0), liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 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_arm64.deb Size: 1186420 MD5sum: 55ba8164a6a824fda85362ca7cde2c55 SHA1: e18fea5db8c4485541f2827e53ae2b9c1fe9741d SHA256: 154bc4367f258515cdf29bf075f65620c3fe60b63b73a0c3d5fac70c52999904 SHA512: 9e581940ed144d1dc777a7a7b12f0f238c1f8d432b386b24dad3e094200e706da76f254e71eddac666b149249ac99e034f112e368d4d2bc17fe8c8df91509f6b Homepage: https://cran.r-project.org/package=csaw Description: Bioc Package 'csaw' (ChIP-Seq Analysis with Windows) Detection of differentially bound regions in ChIP-seq data with sliding windows, with methods for normalization and proper FDR control. Package: r-bioc-cytolib Architecture: arm64 Version: 2.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11003 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_arm64.deb Size: 1377786 MD5sum: ff5e660cbc7052cfc64a6cb79c49b433 SHA1: 2206c5d42f705293ddedfa55ad67b1b9f7dcf06c SHA256: 319771b81c2ef83cc7aa98606c92a60975ae1fdb1dc5713756f4de2773c0b0ac SHA512: a6547e63554fdb9d4d6e4ad64fbc9137102990afee194c867b2ce11ed16b93907161b6a87397d003cd57da31ece053f19377fed997673af1d629771825f6b446 Homepage: https://cran.r-project.org/package=cytolib Description: Bioc Package 'cytolib' (C++ infrastructure for representing and interacting with thegated cytometry data) This package provides the core data structure and API to represent and interact with the gated cytometry data. Package: r-bioc-cytoml Architecture: arm64 Version: 2.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 16839 Depends: libblas3 | libblas.so.3, libc6 (>= 2.43), libcurl4t64 (>= 7.16.2), libgcc-s1 (>= 11), 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_arm64.deb Size: 8769408 MD5sum: 8f05542f53f0a1724ab1487ece3d48db SHA1: 2874cd3a9a5ab2db17d37a0b080c8821c4c76530 SHA256: 9727103b97f266292968b5a6abc6d84408d852052164db9559a1b4bd93312da4 SHA512: 606c3bddae482f9479019bdbe669cd56b958e7ad9c2232018f223336aeff788e3ff55e9373cbb353740c0736650862e1db506f00f5fe28b33a4fa104a1bcb624 Homepage: https://cran.r-project.org/package=CytoML Description: Bioc Package 'CytoML' (A GatingML Interface for Cross Platform Cytometry Data Sharing) Uses platform-specific implemenations of the GatingML2.0 standard to exchange gated cytometry data with other software platforms. Package: r-bioc-dada2 Architecture: arm64 Version: 1.40.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4305 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_arm64.deb Size: 3401256 MD5sum: 99217b35be0078881dd3559810d9e59b SHA1: 4055f18218cff9c266acad8b3201ba66576441c2 SHA256: 6b8dc76376463c537c6ea98764ae729c5cc0fe5d6fdc9949ba11ae24d00e7a60 SHA512: 6315752833b98435507d916f84add03558e14648c6c136f48896fe8dfc69e9c343a31f0af2e165e17ca1a2cd8174b45a91081854b0a96a3076439820f5d44202 Homepage: https://cran.r-project.org/package=dada2 Description: Bioc Package 'dada2' (Accurate, high-resolution sample inference from ampliconsequencing data) The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and species-level assignment to 16S rRNA gene fragments by exact matching. Package: r-bioc-decipher Architecture: arm64 Version: 3.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 20640 Depends: libc6 (>= 2.17), libgomp1 (>= 6), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biostrings, r-cran-dbi, r-bioc-s4vectors, r-bioc-iranges, r-bioc-xvector Suggests: r-cran-rsqlite Filename: pool/dists/resolute/main/r-bioc-decipher_3.8.0-1.ca2604.1_arm64.deb Size: 17698196 MD5sum: 64df6daf91c08cf9e162b93da4a63589 SHA1: f8a99bfccefa2b2c354f2d3b3271faf8c57283a0 SHA256: 66c41ab4f70618de31cba1ab45cfa6d28f6a97c1c28d762c3005dbc9aeebe9f1 SHA512: ba6e9aa223fcdb9cbd746246ef8fa5920e4555a8e4a04f1e8715a74a905f220b3a5a1d5796a1947623751ad55bb2ed3ff5434a3b4b331765fce432f5d36bb65c Homepage: https://cran.r-project.org/package=DECIPHER Description: Bioc Package 'DECIPHER' (Tools for curating, analyzing, and manipulating biologicalsequences) A toolset for deciphering and managing biological sequences. Package: r-bioc-delayedarray Architecture: arm64 Version: 0.36.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3755 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-bioc-biocgenerics, r-bioc-matrixgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-s4arrays, r-bioc-sparsearray Suggests: r-bioc-biocparallel, r-bioc-hdf5array, r-bioc-genefilter, r-bioc-summarizedexperiment, r-bioc-airway, r-cran-lobstr, r-bioc-delayedmatrixstats, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-cran-runit Filename: pool/dists/resolute/main/r-bioc-delayedarray_0.36.0-1.ca2604.1_arm64.deb Size: 2193382 MD5sum: a3fec80e6c6cc427a59eb0c1e5aebeb5 SHA1: 286ef3c31c8de70a7bbfa434cf62ebdff1de2a1e SHA256: 2cfdd277493c0fa4b5e0828c8bd8e1deab5b208521c83ec94edf163480664da3 SHA512: f869975cb48dc7493553ee76d36cc0a89c7b39923b0bb37284689f5b71ac89d85366370afab40f0da6a9744878e86446ff4c02efc2e80a166af130b6dc5ca145 Homepage: https://cran.r-project.org/package=DelayedArray Description: Bioc Package 'DelayedArray' (A unified framework for working transparently with on-disk andin-memory array-like datasets) Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, ordinary arrays and, data frames. Package: r-bioc-densvis Architecture: arm64 Version: 1.22.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2999 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_arm64.deb Size: 1772210 MD5sum: eb19e81553de7e8de9e229c4d17633d3 SHA1: 35a92275469cd6ac52a19602d9f85217dc893848 SHA256: 4f14a596d13bcaca9cb869dc9b731d5afaf217201a65390f2d2c340c5efc9e23 SHA512: 8ca1da1b93536b04e4c841c8a4dcdf38a60d33fc2317dd284fa1bb87fb8e6c810de72acde3caa7a4fc34e389b72efd8c48f55e6d3df98247609811dd95e4ed47 Homepage: https://cran.r-project.org/package=densvis Description: Bioc Package 'densvis' (Density-Preserving Data Visualization via Non-LinearDimensionality Reduction) Implements the density-preserving modification to t-SNE and UMAP described by Narayan et al. (2020) . The non-linear dimensionality reduction techniques t-SNE and UMAP enable users to summarise complex high-dimensional sequencing data such as single cell RNAseq using lower dimensional representations. These lower dimensional representations enable the visualisation of discrete transcriptional states, as well as continuous trajectory (for example, in early development). However, these methods focus on the local neighbourhood structure of the data. In some cases, this results in misleading visualisations, where the density of cells in the low-dimensional embedding does not represent the transcriptional heterogeneity of data in the original high-dimensional space. den-SNE and densMAP aim to enable more accurate visual interpretation of high-dimensional datasets by producing lower-dimensional embeddings that accurately represent the heterogeneity of the original high-dimensional space, enabling the identification of homogeneous and heterogeneous cell states. This accuracy is accomplished by including in the optimisation process a term which considers the local density of points in the original high-dimensional space. This can help to create visualisations that are more representative of heterogeneity in the original high-dimensional space. Package: r-bioc-deseq2 Architecture: arm64 Version: 1.52.0-1.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), 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_arm64.deb Size: 3165506 MD5sum: 463f99731cd7a1f64615a42f805f0276 SHA1: 059bfbb65191ddb8fafa96d30e3a15d0ab15defb SHA256: e2f0033abe27a04e4f1162690e97d0e2427d26a465a8b3c9a82df605e4be9618 SHA512: 630157288dac9e28a138329584b9c2d2f1b25797c9dd748baf4dd13d9362531cde508eb964039202e326893383c3da30133e28027ce2b0ee2009bc2fb0e13e92 Homepage: https://cran.r-project.org/package=DESeq2 Description: Bioc Package 'DESeq2' (Differential gene expression analysis based on the negativebinomial distribution) Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. 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Package: r-bioc-dnacopy Architecture: arm64 Version: 1.86.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/resolute/main/r-bioc-dnacopy_1.86.0-1.ca2604.1_arm64.deb Size: 495760 MD5sum: 3a514d9012051026434703897e12014e SHA1: 252f2dd7c5451d63101e9b935ae279d74f0312c1 SHA256: 53a03e03c01a04430ad01e55d75e0cf6d7507428c89e1320152f94e1b36cee9e SHA512: 752de91f215f0930d397acc007f29a5984e3d49870f7109531d425830aa92996cc7d7a1c69cb90562dcd52255d8f323c762ef4733e1e912b15ed4364459142fa 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-dss Architecture: arm64 Version: 2.60.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3895 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-biocparallel, r-bioc-bsseq Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-edger Filename: pool/dists/resolute/main/r-bioc-dss_2.60.0-1.ca2604.1_arm64.deb Size: 1548088 MD5sum: b1c0630a271336221ff3284b0d90421a SHA1: 9b6ebe6589052c31b6a8132419b9874742596bf8 SHA256: 65983c72ab8d5e19d0f0113e16990505bca243975575c4961d08ea93484ac988 SHA512: 3db039b33310e4425e5ebdf552c22955cb7f69ca8a4c705be1386d31ce91c7f7601776adba9ddd1285af28523d5fc4794a954b2d81433cc584b2713fcd5f62fa Homepage: https://cran.r-project.org/package=DSS Description: Bioc Package 'DSS' (Dispersion shrinkage for sequencing data) DSS is an R library performing differntial analysis for count-based sequencing data. 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Package: r-bioc-eds Architecture: arm64 Version: 1.14.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 803 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 244724 MD5sum: 5ddb59395e16d7568fb87e79925f37ed SHA1: ae7e2ec5f66e39f0432c81de98426d4fe0458c18 SHA256: 3ff8739138b01b4c73f63a205d6c520908d2c5d4061a0a3e029f171f35eea3d6 SHA512: ed12c0e296aad18f377f8b0406e9b985a36e6ef4c9116cab530f6d9fd2391ec12fed0bc5f36b99f8220b5784e7c9856e9d1081cc4453a914a22f297a91dae830 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: arm64 Version: 1.58.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1461 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_arm64.deb Size: 751432 MD5sum: 7dbfff7d6cc54941874f5ba7f8acfae7 SHA1: e43a8bec8e2166605cbd5708bd4f561592502c23 SHA256: 130d1fcd6f30b431da250cbc4be5f0209e9845393cfcd7cb8a7154f21976ac61 SHA512: ca510d94a1e4a9f477256d4e5a04f07224dec898838cbdcb5324e2f975416496d8a074c0c66471cbe0734291c7b9061430a8554039178bbc50508f68da23fbea Homepage: https://cran.r-project.org/package=fastseg Description: Bioc Package 'fastseg' (fastseg - a fast segmentation algorithm) fastseg implements a very fast and efficient segmentation algorithm. It has similar functionality as DNACopy (Olshen and Venkatraman 2004), but is considerably faster and more flexible. fastseg can segment data from DNA microarrays and data from next generation sequencing for example to detect copy number segments. Further it can segment data from RNA microarrays like tiling arrays to identify transcripts. Most generally, it can segment data given as a matrix or as a vector. Various data formats can be used as input to fastseg like expression set objects for microarrays or GRanges for sequencing data. The segmentation criterion of fastseg is based on a statistical test in a Bayesian framework, namely the cyber t-test (Baldi 2001). The speed-up arises from the facts, that sampling is not necessary in for fastseg and that a dynamic programming approach is used for calculation of the segments' first and higher order moments. Package: r-bioc-fgsea Architecture: arm64 Version: 1.38.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9903 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), 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_arm64.deb Size: 5808654 MD5sum: 59f6276c9e40f26ea96e9917c54fa63f SHA1: 40a85d973f48a10c27dff6078f2313f2c7c4b49b SHA256: f73c19372b5bcfd0a138c85d3f5629a2fad8f892111013282ae4f4a6d8e5abc3 SHA512: faa93d3ba8e5601010f1b1ea89894a08f550c14020032ec486f2996929830b72f2e4d05a88ec460b2cc15ca8f32d8cf7dad078d036af6877d10746a6aed53da5 Homepage: https://cran.r-project.org/package=fgsea Description: Bioc Package 'fgsea' (Fast Gene Set Enrichment Analysis) The package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm allows to make more permutations and get more fine grained p-values, which allows to use accurate stantard approaches to multiple hypothesis correction. Package: r-bioc-flowclust Architecture: arm64 Version: 3.50.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2707 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_arm64.deb Size: 1209906 MD5sum: 1419f4bff4e9f8ce1e05233c99b88fea SHA1: c875ccd0842186ee2e6d071ec1aa2812884bc70f SHA256: 51b7f55987325d83a259b29db90d3eb832f266a469dd2e711e3afcff96925aa3 SHA512: 9ca46244c34ed8c9e96bb268895f60d35bc434f7606651be5aa398c4e5a7be42eb8ef0234a5bb312c87e471631d6b2ee68b4c4d32c50d0a14f8fb57dc6c21c1a Homepage: https://cran.r-project.org/package=flowClust Description: Bioc Package 'flowClust' (Clustering for Flow Cytometry) Robust model-based clustering using a t-mixture model with Box-Cox transformation. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. Package: r-bioc-flowcore Architecture: arm64 Version: 2.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 16079 Depends: libblas3 | libblas.so.3, libc6 (>= 2.43), libcurl4t64 (>= 7.16.2), libgcc-s1 (>= 11), 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_arm64.deb Size: 10369290 MD5sum: c4354fc3b2767c4d39908381940a4bb4 SHA1: 9df2e1d69324e4d64f270d89769374b73e33f6c7 SHA256: 3f4a0a6b44ae75f6a8ebbfe58e7605c3dfd58b95ca260b14a13b86d136f64ae9 SHA512: 71a35ea83b258425ba966361f1c760a8e72955cef3ddb75f6d43e66e66190035c4844b37bec38d1a19dbd88815316fe3bd7bb5f3908c2e4ef9ea9ce559bcf998 Homepage: https://cran.r-project.org/package=flowCore Description: Bioc Package 'flowCore' (flowCore: Basic structures for flow cytometry data) Provides S4 data structures and basic functions to deal with flow cytometry data. Package: r-bioc-flowsom Architecture: arm64 Version: 2.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6163 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_arm64.deb Size: 4887028 MD5sum: b4a523dc0ed58aafec58e01d0f2e49c6 SHA1: f836619b42e4b13242107bb7a3cef9c8388f8a34 SHA256: e1adfb1e879febe33f60a8d48b1c802a73a7368952d2559851f19d32f7683969 SHA512: 397b540cff8be1c2aeda15ec824809032ca9fcb4e0f0eb0fac559b956a74dca1e56f7c411f11bf38281a55d1c1f431bb327473b966eefea6050709c9a12ee5c3 Homepage: https://cran.r-project.org/package=FlowSOM Description: Bioc Package 'FlowSOM' (Using self-organizing maps for visualization and interpretationof cytometry data) FlowSOM offers visualization options for cytometry data, by using Self-Organizing Map clustering and Minimal Spanning Trees. Package: r-bioc-flowworkspace Architecture: arm64 Version: 4.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13270 Depends: libblas3 | libblas.so.3, libc6 (>= 2.43), libcurl4t64 (>= 7.16.2), libgcc-s1 (>= 11), 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_arm64.deb Size: 4897080 MD5sum: 95914c3897d27d646c164e722df0885e SHA1: 3bd2eef371a73afa4f79b959b37d6369a2ca9dee SHA256: 585e91ebdadded47a777468623b3655fd6794e88022aeb5a03fb0a688352e399 SHA512: 9813880609fb3880779da02fe156fe4a8d3eded6fa60ce4a2f64512ba533e593bd04baf772373ab5ce3c57cc67774a0573e4880d8906cddfe6fd1d7f958bc30e Homepage: https://cran.r-project.org/package=flowWorkspace Description: Bioc Package 'flowWorkspace' (Infrastructure for representing and interacting with gated andungated cytometry data sets.) This package is designed to facilitate comparison of automated gating methods against manual gating done in flowJo. This package allows you to import basic flowJo workspaces into BioConductor and replicate the gating from flowJo using the flowCore functionality. Gating hierarchies, groups of samples, compensation, and transformation are performed so that the output matches the flowJo analysis. Package: r-bioc-fmcsr Architecture: arm64 Version: 1.54.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1929 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-chemminer, r-cran-runit, r-bioc-biocgenerics Suggests: r-bioc-biocstyle, r-cran-knitr, r-cran-knitcitations, r-cran-knitrbootstrap, r-cran-rmarkdown, r-cran-codetools Filename: pool/dists/resolute/main/r-bioc-fmcsr_1.54.0-1.ca2604.1_arm64.deb Size: 946034 MD5sum: 98c70bb4a55bb46ca2d6b626a61996c8 SHA1: 46d2c359e73e4177d01330eec48710e1b7492f32 SHA256: e8db995649b4ef42a99838684b1531dd9d7fa4a2d40a77cea9fc76f534686519 SHA512: 398424c50ccbf2dc0b1d27baf689f44f16edede6aac122a1bc811c5eb6892ef5828d571f4d1781e03fbaed4db335cee34e2424847797a33ac3ff6df06f114651 Homepage: https://cran.r-project.org/package=fmcsR Description: Bioc Package 'fmcsR' (Mismatch Tolerant Maximum Common Substructure Searching) The fmcsR package introduces an efficient maximum common substructure (MCS) algorithms combined with a novel matching strategy that allows for atom and/or bond mismatches in the substructures shared among two small molecules. The resulting flexible MCSs (FMCSs) are often larger than strict MCSs, resulting in the identification of more common features in their source structures, as well as a higher sensitivity in finding compounds with weak structural similarities. The fmcsR package provides several utilities to use the FMCS algorithm for pairwise compound comparisons, structure similarity searching and clustering. Package: r-bioc-fmrs Architecture: arm64 Version: 1.22.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival Suggests: r-bioc-biocgenerics, r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-bioc-fmrs_1.22.0-1.ca2604.1_arm64.deb Size: 192916 MD5sum: 167f59051151dd66924b26ee2a01eb13 SHA1: 0bd50f683df16b14a30551a0936a69cd85144712 SHA256: 02e261f4f3d03c50f683e03af294027a8795d3e1f8023421f13025243b164821 SHA512: 6af0a3ead79e2356a0fa28514009cb428a4b82c20dde4e09ae1e6243602b5f394c793bc933d05e207278e7ecd09dd6580bf0d27511ad9420a0016bb40f841e65 Homepage: https://cran.r-project.org/package=fmrs Description: Bioc Package 'fmrs' (Variable Selection in Finite Mixture of AFT Regression and FMRModels) The package obtains parameter estimation, i.e., maximum likelihood estimators (MLE), via the Expectation-Maximization (EM) algorithm for the Finite Mixture of Regression (FMR) models with Normal distribution, and MLE for the Finite Mixture of Accelerated Failure Time Regression (FMAFTR) subject to right censoring with Log-Normal and Weibull distributions via the EM algorithm and the Newton-Raphson algorithm (for Weibull distribution). More importantly, the package obtains the maximum penalized likelihood (MPLE) for both FMR and FMAFTR models (collectively called FMRs). A component-wise tuning parameter selection based on a component-wise BIC is implemented in the package. Furthermore, this package provides Ridge Regression and Elastic Net. Package: r-bioc-gcrma Architecture: arm64 Version: 2.84.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 509 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-affy, r-bioc-biobase, r-bioc-affyio, r-bioc-xvector, r-bioc-biostrings, r-cran-biocmanager Suggests: r-bioc-affydata, r-bioc-hgu95av2cdf, r-bioc-hgu95av2probe Filename: pool/dists/resolute/main/r-bioc-gcrma_2.84.0-1.ca2604.1_arm64.deb Size: 396582 MD5sum: 9fc76ad0e938aeb8f5eb074c7f260ea6 SHA1: 275bfb952370550abb112863c799c372a7de9900 SHA256: cc9b151940b7cc63b2cf7ae9d999fadf57ced404ac2eeaf1f2d7408d05dbd384 SHA512: ec553966ee1eb82bae4e1d043b97ffccfc31844a0dae9a06ca99f9fe9117cfbec8ff11a292cbe167c3ec699a94b9d1e62bfb73044db2412ef594f7c71624323c Homepage: https://cran.r-project.org/package=gcrma Description: Bioc Package 'gcrma' (Background Adjustment Using Sequence Information) Background adjustment using sequence information Package: r-bioc-gdsfmt Architecture: arm64 Version: 1.48.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6098 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_arm64.deb Size: 1561070 MD5sum: b310d395817528f0e59360e9b2137cbb SHA1: 18f1d708d2bbb4c7502e5d0f3a91b9cf7cf26c63 SHA256: 4e5961241c422f4e4d9ca52e4979db4da46d82b2f1cc9ac75d740c47fe87873b SHA512: b9148b7e0c554a54e05f93e8f72c2ef35587962f2548abcc2a11e599fda24b76a5077bd7bf418cb5ae1d468afeb165c928bc9ba98ea683c04457932516ac3927 Homepage: https://cran.r-project.org/package=gdsfmt Description: Bioc Package 'gdsfmt' (R Interface to CoreArray Genomic Data Structure (GDS) Files) Provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files. GDS is portable across platforms with hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers the efficient operations specifically designed for integers of less than 8 bits, since a diploid genotype, like single-nucleotide polymorphism (SNP), usually occupies fewer bits than a byte. Data compression and decompression are available with relatively efficient random access. It is also allowed to read a GDS file in parallel with multiple R processes supported by the package parallel. Package: r-bioc-genefilter Architecture: arm64 Version: 1.94.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2556 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-matrixgenerics, r-bioc-annotationdbi, r-bioc-annotate, r-bioc-biobase, r-cran-survival Suggests: r-cran-class, r-bioc-hgu95av2.db, r-bioc-tkwidgets, r-bioc-all, r-bioc-roc, r-cran-rcolorbrewer, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/resolute/main/r-bioc-genefilter_1.94.0-1.ca2604.1_arm64.deb Size: 1231652 MD5sum: 658c96100b61fd5366d9a7552be52480 SHA1: 714c8d7d256c9fcc04aa0f6c7c10d167a859ab90 SHA256: a84250f66369a91941e8c219834d3742b477d6677ab48ecd1c3c941a41d7ca1f SHA512: f6909ce4e4a8ce5493d2a126a617003a321dd7d21baa9cc296e6bbb82f53230c3eedceafb5a21cd3d22ea9ca5b6c8c6ae2d84ab178312b84172e4839e6be5cda Homepage: https://cran.r-project.org/package=genefilter Description: Bioc Package 'genefilter' (genefilter: methods for filtering genes from high-throughputexperiments) Some basic functions for filtering genes. Package: r-bioc-genesis Architecture: arm64 Version: 2.42.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7302 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-biocparallel, r-bioc-gwastools, r-bioc-gdsfmt, r-bioc-genomicranges, r-bioc-iranges, r-bioc-s4vectors, r-bioc-seqarray, r-bioc-seqvartools, r-bioc-snprelate, r-cran-data.table, r-cran-igraph, r-cran-matrix, r-cran-reshape2 Suggests: r-cran-compquadform, r-cran-compoissonreg, r-cran-poibin, r-cran-spatest, r-cran-survey, r-cran-testthat, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-gwasdata, r-cran-dplyr, r-cran-ggplot2, r-cran-ggally, r-cran-rcolorbrewer, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-genomeinfodb Filename: pool/dists/resolute/main/r-bioc-genesis_2.42.0-1.ca2604.1_arm64.deb Size: 3663810 MD5sum: a2b79cc98722fd6efe8d7af1ff9c66cf SHA1: 7414e4fa38691cb535e941a9f2423d79c77ef886 SHA256: 225185028a3d732c013acf7710b197868a869dd8e40930226e97e9c622200585 SHA512: 786ac648bef3040a845562f86556b661569694cbb3282c02be4bac4257ea9cd6fe0d0bcf9648d0853cf119d38fcbfdb152f5024d1bbc8d6b71242738d065deae Homepage: https://cran.r-project.org/package=GENESIS Description: Bioc Package 'GENESIS' (GENetic EStimation and Inference in Structured samples(GENESIS): Statistical methods for analyzing genetic data fromsamples with population structure and/or relatedness) The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses. The current implementation provides functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a Principal Components Analysis on genome-wide SNP data for the detection of population structure in a sample that may contain known or cryptic relatedness. Unlike standard PCA, PC-AiR accounts for relatedness in the sample to provide accurate ancestry inference that is not confounded by family structure. PC-Relate uses ancestry representative principal components to adjust for population structure/ancestry and accurately estimate measures of recent genetic relatedness such as kinship coefficients, IBD sharing probabilities, and inbreeding coefficients. Additionally, functions are provided to perform efficient variance component estimation and mixed model association testing for both quantitative and binary phenotypes. Package: r-bioc-genie3 Architecture: arm64 Version: 1.34.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 777 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-cran-reshape2, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-foreach, r-cran-dorng, r-cran-doparallel, r-bioc-biobase, r-bioc-summarizedexperiment, r-cran-testthat, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-genie3_1.34.0-1.ca2604.1_arm64.deb Size: 251662 MD5sum: d80aa40af489fd25294ab90e96505899 SHA1: ed6e6e8b1de1ac7e1d921eb97efc27421132a742 SHA256: a884a7b40bd739af8bb87c22fc7fbeb68ab93986980ddb3d96d72acf04a86213 SHA512: 8644b01abff3991d4f7dc8b584789bb5b96e6e96036ba377e1a7ffe6fc4e6e9e4a59d77df1637f9be3c1748aea4c0b8689b6631e5f9525a8bf55fbe722a1ac77 Homepage: https://cran.r-project.org/package=GENIE3 Description: Bioc Package 'GENIE3' (GEne Network Inference with Ensemble of trees) This package implements the GENIE3 algorithm for inferring gene regulatory networks from expression data. 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Users can visualize and quantify genomic intervals over pre-defined functional regions, such as promoters, exons, introns, etc. The genomic intervals represent regions with a defined chromosome position, which may be associated with a score, such as aligned reads from HT-seq experiments, TF binding sites, methylation scores, etc. The package can use any tabular genomic feature data as long as it has minimal information on the locations of genomic intervals. In addition, It can use BAM or BigWig files as input. Package: r-bioc-genomicalignments Architecture: arm64 Version: 1.48.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3362 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-biostrings, r-bioc-rsamtools, r-bioc-biocparallel, r-bioc-cigarillo Suggests: r-bioc-shortread, r-bioc-rtracklayer, r-bioc-bsgenome, r-bioc-genomicfeatures, r-bioc-rnaseqdata.hnrnpc.bam.chr14, r-bioc-pasillabamsubset, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.dmelanogaster.ucsc.dm3.ensgene, r-bioc-bsgenome.dmelanogaster.ucsc.dm3, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-deseq2, r-bioc-edger, r-cran-runit, r-cran-knitr, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-genomicalignments_1.48.0-1.ca2604.1_arm64.deb Size: 2125312 MD5sum: ac0a27c01bd7bd2b698bf6334c83a53a SHA1: c02a574854619dcd85bf6aa8341e6956241c40fd SHA256: 7db7ee089ffcb266bf4fc07f3c08245a0226f3075bcb1e5d14fd8ed69d5c06af SHA512: 04a6353022bfa791832fdb7be8cc3afd50fcd2123336a21ed83f2cb69d3e34b61a9c087be5874b6b91fed192dc814ef58ff1870774119a1f55bb82e83f3ee8e4 Homepage: https://cran.r-project.org/package=GenomicAlignments Description: Bioc Package 'GenomicAlignments' (Representation and manipulation of short genomic alignments) Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments. Package: r-bioc-genomicfeatures Architecture: arm64 Version: 1.64.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2468 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomicranges, r-bioc-annotationdbi, r-cran-dbi, r-bioc-xvector, r-bioc-biostrings, r-bioc-rtracklayer Suggests: r-bioc-genomeinfodb, r-bioc-txdbmaker, r-bioc-org.mm.eg.db, r-bioc-org.hs.eg.db, r-bioc-bsgenome, r-bioc-bsgenome.hsapiens.ucsc.hg19, r-bioc-bsgenome.celegans.ucsc.ce11, r-bioc-bsgenome.dmelanogaster.ucsc.dm3, r-bioc-fdb.ucsc.trnas, r-bioc-txdb.hsapiens.ucsc.hg19.knowngene, r-bioc-txdb.celegans.ucsc.ce11.ensgene, r-bioc-txdb.dmelanogaster.ucsc.dm3.ensgene, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene, r-bioc-txdb.hsapiens.ucsc.hg19.lincrnastranscripts, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-snplocs.hsapiens.dbsnp144.grch38, r-bioc-rsamtools, r-bioc-pasillabamsubset, r-bioc-genomicalignments, r-bioc-ensembldb, r-bioc-annotationfilter, r-cran-runit, r-bioc-biocstyle, r-cran-knitr, r-cran-markdown Filename: pool/dists/resolute/main/r-bioc-genomicfeatures_1.64.0-1.ca2604.1_arm64.deb Size: 1349314 MD5sum: b9e8eaa8355e711a950e6f5e63aa9d0a SHA1: d9b4be7b2287ffe6a61142e7d33628f3517267cf SHA256: 3d650c316cd29705647ae176042de6092e726890b8bc3ccba919d95a126325bc SHA512: b5125d3eded10dc0217d5df54f6e1bb2ec3526496916aa9defd787593b956befc2383faffbbac855e12b6651f38fbf15336a82743dfb7e039525a29e3a9c21f0 Homepage: https://cran.r-project.org/package=GenomicFeatures Description: Bioc Package 'GenomicFeatures' (Query the gene models of a given organism/assembly) Extract the genomic locations of genes, transcripts, exons, introns, and CDS, for the gene models stored in a TxDb object. A TxDb object is a small database that contains the gene models of a given organism/assembly. Bioconductor provides a small collection of TxDb objects in the form of ready-to-install TxDb packages for the most commonly studied organisms. Additionally, the user can easily make a TxDb object (or package) for the organism/assembly of their choice by using the tools from the txdbmaker package. Package: r-bioc-genomicranges Architecture: arm64 Version: 1.62.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3626 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo Suggests: r-bioc-genomeinfodb, r-bioc-biobase, r-bioc-annotationdbi, r-bioc-annotate, r-bioc-biostrings, r-bioc-summarizedexperiment, r-bioc-rsamtools, r-bioc-genomicalignments, r-bioc-bsgenome, r-bioc-genomicfeatures, r-bioc-ucsc.utils, r-bioc-txdbmaker, r-bioc-gviz, r-bioc-variantannotation, r-bioc-annotationhub, r-bioc-deseq2, r-bioc-dexseq, r-bioc-edger, r-bioc-kegggraph, r-bioc-rnaseqdata.hnrnpc.bam.chr14, r-bioc-pasillabamsubset, r-bioc-keggrest, r-bioc-hgu95av2.db, r-bioc-hgu95av2probe, r-bioc-bsgenome.scerevisiae.ucsc.saccer2, r-bioc-bsgenome.hsapiens.ucsc.hg38, r-bioc-bsgenome.mmusculus.ucsc.mm10, r-bioc-txdb.athaliana.biomart.plantsmart22, r-bioc-txdb.dmelanogaster.ucsc.dm3.ensgene, r-bioc-txdb.hsapiens.ucsc.hg38.knowngene, r-bioc-txdb.mmusculus.ucsc.mm10.knowngene, r-cran-runit, r-cran-digest, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-genomicranges_1.62.1-1.ca2604.1_arm64.deb Size: 2295238 MD5sum: 6fb9f60619e4db2fed6dc72bc00822d3 SHA1: 1cb4bb03312c0e766e8fd0e377ec5514166ec077 SHA256: d31f32a9667aa4c56f3048274a4b8bf2b6a2b9b5caf8577745934a63a258add0 SHA512: 10cabdd7292e04678cc670cae2c7d1e5cb30cb3411d8bad0b0482fa476e212b159497bedb099e078fe7e7b33cc7e54fcc4c409fa02539026d6c2d8ad3bbb26c5 Homepage: https://cran.r-project.org/package=GenomicRanges Description: Bioc Package 'GenomicRanges' (Representation and manipulation of genomic intervals) The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose containers for storing and manipulating genomic intervals and variables defined along a genome. More specialized containers for representing and manipulating short alignments against a reference genome, or a matrix-like summarization of an experiment, are defined in the GenomicAlignments and SummarizedExperiment packages, respectively. Both packages build on top of the GenomicRanges infrastructure. Package: r-bioc-glmgampoi Architecture: arm64 Version: 1.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3362 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1679744 MD5sum: c4a82d3f5ad9b67002ebb57d5924a45f SHA1: 745afc319663b4ed9b7c9641e551be18371c3ea8 SHA256: 8bc749324fd1c532dd262569c7f9bc4e46e01fad5d7ce31d55a99758b7f6597c SHA512: 1bf9782f5194a6a42b567e01fd133963c0f084e7e35dc4a2c9b2b29cdd343040753a91c883e3a5a910bb71c4371ff6681f49ba737deaa666e26689eb3bf3d814 Homepage: https://cran.r-project.org/package=glmGamPoi Description: Bioc Package 'glmGamPoi' (Fit a Gamma-Poisson Generalized Linear Model) Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments. Package: r-bioc-globalancova Architecture: arm64 Version: 4.30.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1882 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-cran-corpcor, r-bioc-globaltest, r-bioc-annotate, r-bioc-annotationdbi, r-bioc-biobase, r-cran-dendextend, r-bioc-gseabase, r-cran-vgam Suggests: r-bioc-go.db, r-bioc-golubesets, r-bioc-hu6800.db, r-bioc-vsn, r-bioc-rgraphviz Filename: pool/dists/resolute/main/r-bioc-globalancova_4.30.0-1.ca2604.1_arm64.deb Size: 1621576 MD5sum: a62d068bea38e516ddacc6fb7a622766 SHA1: f2923a9c6cb57df1405dd086e871cd9d55203710 SHA256: 84f39953393be05df73cbc36380749954b8755c939063f93baa7e905a06fd842 SHA512: f249b5ea846ef834aa06b951e4ed6cd81bbd65e57fbc45a58f2947a61bfcaf5cc27b686336fa13bbf0ec868bac9e86e48789c2cbe3d677aed62d255c1db30f0e Homepage: https://cran.r-project.org/package=GlobalAncova Description: Bioc Package 'GlobalAncova' (Global test for groups of variables via model comparisons) The association between a variable of interest (e.g. two groups) and the global pattern of a group of variables (e.g. a gene set) is tested via a global F-test. We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. The framework is generalized to groups of categorical variables or even mixed data by a likelihood ratio approach. Closed and hierarchical testing procedures are supported. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany. Package: r-bioc-gosemsim Architecture: arm64 Version: 2.38.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1375 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_arm64.deb Size: 1119172 MD5sum: 0cff29dab1ecbdea1e2498cf946291f1 SHA1: bce6ace1d364cdba867a4b2fc36d078f28861205 SHA256: 25ef73c5bc7a353961066e12361e67fc6eefe7708d79a5eba23b1cdfb0f66aa8 SHA512: 92552d02a669f01c9c2b57c88ba39069953ede9da393283581f9b7527718e185c6fe4284fbef5ad9f8a0f475ff9adf08f8f2da634cdb8bcbbd190121714284b8 Homepage: https://cran.r-project.org/package=GOSemSim Description: Bioc Package 'GOSemSim' (GO-terms Semantic Similarity Measures) The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R package for semantic similarity computation among GO terms, sets of GO terms, gene products and gene clusters. GOSemSim implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively. Package: r-bioc-graph Architecture: arm64 Version: 1.90.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4955 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics Suggests: r-cran-sparsem, r-cran-xml, r-bioc-rbgl, r-cran-runit, r-cran-cluster, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/resolute/main/r-bioc-graph_1.90.0-1.ca2604.1_arm64.deb Size: 1290806 MD5sum: f4d1e5d5cf58619ff8fa020f4d90a4e5 SHA1: 259e1e58f5a2f1ff95a3c7f54afa3e5eb7c2792c SHA256: 78eaf52c721f432608ddee86d99493b1751c637e73bb0c7c647f2550e135a8d7 SHA512: 71f6bd7ee052fb1ed2cf10981d451a7e199046f622bdb06677180ca39cad4f23a3c92f79a84f442a785924f403084828f676715ba2bc506e42795a13dccd2e5e Homepage: https://cran.r-project.org/package=graph Description: Bioc Package 'graph' (graph: A package to handle graph data structures) A package that implements some simple graph handling capabilities. Package: r-bioc-gsva Architecture: arm64 Version: 2.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5698 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-matrixgenerics, r-bioc-s4vectors, r-bioc-s4arrays, r-bioc-hdf5array, r-bioc-sparsearray, r-bioc-delayedarray, r-bioc-iranges, r-bioc-biobase, r-bioc-summarizedexperiment, r-bioc-gseabase, r-cran-matrix, r-bioc-delayedmatrixstats, r-bioc-biocparallel, r-bioc-singlecellexperiment, r-bioc-biocsingular, r-bioc-spatialexperiment, r-bioc-sparsematrixstats, r-cran-cli, r-cran-memuse Suggests: r-cran-runit, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-bioc-limma, r-cran-rcolorbrewer, r-bioc-org.hs.eg.db, r-bioc-genefilter, r-bioc-edger, r-bioc-gsvadata, r-bioc-sva, r-bioc-tenxpbmcdata, r-bioc-tenxvisiumdata, r-bioc-scrapper, r-bioc-bluster, r-cran-igraph, r-cran-shiny, r-cran-shinydashboard, r-cran-ggplot2, r-cran-data.table, r-cran-plotly, r-cran-future, r-cran-promises, r-cran-shinybusy, r-cran-shinyjs Filename: pool/dists/resolute/main/r-bioc-gsva_2.6.2-1.ca2604.1_arm64.deb Size: 2327826 MD5sum: 17d1be35915ba258e7e1e0be7e38799f SHA1: f1686d46ec0ba975004df1591a19c5dbbb9efe35 SHA256: dcc89e8e31a747783ac11652c9bfa647b5a5e5f03e157461500e8c8bc4cb0c3e SHA512: 25a9ba9249ad2012eaed2b87f9ed6d9124eed82fa9ca0d483c493b508b46451a8fd77d2d756806a8945f7f205535898ef0114a40c83d043d943d28c4b96b315e Homepage: https://cran.r-project.org/package=GSVA Description: Bioc Package 'GSVA' (Gene Set Variation Analysis for Microarray and RNA-Seq Data) Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner. Package: r-bioc-h5mread Architecture: arm64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8645 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_arm64.deb Size: 4375422 MD5sum: d8df111c06703659e4b450ef3a978979 SHA1: 5c1ac1674f9ecaeaae19bd491988968ed076d3e0 SHA256: 56fd854724ce0618285281150f95981636d6a2c60ff228e3c61502820b4c2389 SHA512: 84ebe43f737a5dbb74758436e1a54fe4ade092c869b3bb932f9aa30a4f80fcc5f22a34fb9c73ba4442194f95f1fd3bcbce11f248e42d1d92e231e326965f6331 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: arm64 Version: 1.14.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4867 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 3470870 MD5sum: dc6c2cf1ab9b82f9eb6cd334fc51103b SHA1: ceecbddd2f7f3f2451c736650a7fb039caf614d7 SHA256: f57ab3cf709074bf840313c511c3481914e2573f498fba3abce60cc48958887d SHA512: 7a56516b1b6ec134680bb3fdfd5c0435157578ecdba0438a0b7f8b6b09c15d073dbe580024a9707c272ba1acf7c901f4ce7ffb1ebb1c525bbe37135147ece734 Homepage: https://cran.r-project.org/package=HiCDOC Description: Bioc Package 'HiCDOC' (A/B compartment detection and differential analysis) HiCDOC normalizes intrachromosomal Hi-C matrices, uses unsupervised learning to predict A/B compartments from multiple replicates, and detects significant compartment changes between experiment conditions. It provides a collection of functions assembled into a pipeline to filter and normalize the data, predict the compartments and visualize the results. It accepts several type of data: tabular `.tsv` files, Cooler `.cool` or `.mcool` files, Juicer `.hic` files or HiC-Pro `.matrix` and `.bed` files. Package: r-bioc-hopach Architecture: arm64 Version: 2.72.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3062 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cluster, r-bioc-biobase, r-bioc-biocgenerics Filename: pool/dists/resolute/main/r-bioc-hopach_2.72.0-1.ca2604.1_arm64.deb Size: 1018024 MD5sum: d69c06edbf30ea96aa45ab48a842aead SHA1: 47b8c46e728847e8b596450e451005ad7f2421ef SHA256: 9992c1150d6ae1af7be8006956fab3a8cf4e77aa4e8d72aee0c1a7a6e99be2e0 SHA512: 76ba7274785ddda771ac201cd0290b6f34dc318fa7c8f8a2a23915a4cf0da178701e99994619c77fd35ffd91828f6545c344ceb52bb8ec0bfe2de0643ccd5a80 Homepage: https://cran.r-project.org/package=hopach Description: Bioc Package 'hopach' (Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH)) The HOPACH clustering algorithm builds a hierarchical tree of clusters by recursively partitioning a data set, while ordering and possibly collapsing clusters at each level. The algorithm uses the Mean/Median Split Silhouette (MSS) criteria to identify the level of the tree with maximally homogeneous clusters. It also runs the tree down to produce a final ordered list of the elements. The non-parametric bootstrap allows one to estimate the probability that each element belongs to each cluster (fuzzy clustering). Package: r-bioc-iclusterplus Architecture: arm64 Version: 1.46.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18284 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_arm64.deb Size: 16618538 MD5sum: 5132b052aa8e8e69d8e7d74a37513691 SHA1: 125f3185b2fdd1e7fdc096518e063844cdb0f510 SHA256: a86430ec86ae9daace6ccc7351d05efddd6798ee3a55abca871bb24dbc4c6477 SHA512: f9d9914c1f31a8fd2f3f9d14feb2bf2086421ec4070bc3b3339e14e1e1eb4b8f5f82339b6f2959ee900c34a1a4ac3a1837cd84a84e83527197771369b266e28c Homepage: https://cran.r-project.org/package=iClusterPlus Description: Bioc Package 'iClusterPlus' (Integrative clustering of multi-type genomic data) Integrative clustering of multiple genomic data using a joint latent variable model. Package: r-bioc-illuminaio Architecture: arm64 Version: 0.54.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 642 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-cran-base64 Suggests: r-cran-runit, r-bioc-biocgenerics, r-bioc-illuminadatatestfiles, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-illuminaio_0.54.0-1.ca2604.1_arm64.deb Size: 502308 MD5sum: 49b57bf05d93c54bbf53c536838743c5 SHA1: c0ec51d3720cdb43e8d36120955757f5bd867039 SHA256: 6709ee3d08da9bc694f1677287dba96556a39cb05c95d0e9a71a0097779da483 SHA512: b31f106aad792206cb22bcd7246f0727433c8f4ac242023680ed6fbde3e6281844b9495e1497613041359bb204f9eb8e7e7dac8e4edd00c65b167b7e13e5fb99 Homepage: https://cran.r-project.org/package=illuminaio Description: Bioc Package 'illuminaio' (Parsing Illumina Microarray Output Files) Tools for parsing Illumina's microarray output files, including IDAT. 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The package includes pre-processing of sequences, unifying gene nomenclature usage, encoding sequences, and combining models. This package will serve as the basis of future immune receptor sequence functions/packages/models compatible with the scRepertoire ecosystem. Package: r-bioc-impute Architecture: arm64 Version: 1.86.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 744 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/resolute/main/r-bioc-impute_1.86.0-1.ca2604.1_arm64.deb Size: 662704 MD5sum: 31d56e57865f0a10f27b8dfb2994acc8 SHA1: 44d7fe4cf1e34026fa8515694ade11860f0b6851 SHA256: 2308123c722980d6b2cef2f5288aee6e1953bca3d83d756388ed51b6850c478d SHA512: b67c92fb864b7430dc5a8c97195134de77bee2edf25a64a42fc0a6d2fa87dd5b1c63422318096907495e267cfb749664d6cc53275dc5afd1b37ebc20d874193d Homepage: https://cran.r-project.org/package=impute Description: Bioc Package 'impute' (impute: Imputation for microarray data) Imputation for microarray data (currently KNN only) Package: r-bioc-interactionset Architecture: arm64 Version: 1.40.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2835 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-cran-matrix, r-cran-rcpp, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-seqinfo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-interactionset_1.40.0-1.ca2604.1_arm64.deb Size: 1290918 MD5sum: 56105afbe0e25e98efbe6680db43316e SHA1: efc114b308fc927e6e1dd59566c6fe67e57ad0a9 SHA256: 7817a05dcb1b7bafd97240a3f7974dd0c49e46daf8701a2b74f3857f3ec35a94 SHA512: 9c29d88f4cc90dc13f9274de5becdf0254602d7d018df812e4ff1516210755b16bae1353eea2ccd923336fe1aee5225c71d56059144f4f9066d982acd19ce9a5 Homepage: https://cran.r-project.org/package=InteractionSet Description: Bioc Package 'InteractionSet' (Base Classes for Storing Genomic Interaction Data) Provides the GInteractions, InteractionSet and ContactMatrix objects and associated methods for storing and manipulating genomic interaction data from Hi-C and ChIA-PET experiments. Package: r-bioc-iranges Architecture: arm64 Version: 2.46.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3485 Depends: libc6 (>= 2.17), libgomp1 (>= 4.9), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors Suggests: r-bioc-xvector, r-bioc-genomicranges, r-bioc-rsamtools, r-bioc-genomicalignments, r-bioc-genomicfeatures, r-bioc-bsgenome.celegans.ucsc.ce2, r-bioc-pasillabamsubset, r-cran-runit, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-iranges_2.46.0-1.ca2604.1_arm64.deb Size: 2319470 MD5sum: 4c806aaa74aefa1cae4c57d349648b6f SHA1: 4b13c6fd95be161d5ff51a02c9081129b99a2224 SHA256: 81d5cdfb40ae738221f2efd19f93787d384a9c71ad9803167a97839f5c65e72c SHA512: ed1708c4c8b43c2a62abb7f619b486be9e81e3587a7a54709bb8d6ca3a25a3cf5ceccd88898a84d40f3adecc0f6e741fbf74eb6d4914a6276abf67285f068cdc Homepage: https://cran.r-project.org/package=IRanges Description: Bioc Package 'IRanges' (Foundation of integer range manipulation in Bioconductor) Provides efficient low-level and highly reusable S4 classes for storing, manipulating and aggregating over annotated ranges of integers. Implements an algebra of range operations, including efficient algorithms for finding overlaps and nearest neighbors. Defines efficient list-like classes for storing, transforming and aggregating large grouped data, i.e., collections of atomic vectors and DataFrames. Package: r-bioc-lea Architecture: arm64 Version: 3.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1392 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_arm64.deb Size: 959206 MD5sum: 14db9e967374c79892cbc2746dba30af SHA1: cf3a4149a751b9c8f223e230bab170a310f68dc3 SHA256: f7f6a62f4cf9129e479fbf8d45980a20b3aab69b1969ee427d7bfbb6753206e9 SHA512: 0ce03ec390b0a0ef1a8da47ef584ad558174740e06a0b4640e707dc23dd5b8e021df58e93e1d483e157893e330501ea573786011d173e3246949beda62dbd65c 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. 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The main method estimates genetic population structure from genotype data. There are also methods for estimating individual-specific allele frequencies using the population structure. Lastly, a structured Hardy-Weinberg equilibrium (HWE) test is developed, which quantifies the goodness of fit of the genotype data to the estimated population structure, via the estimated individual-specific allele frequencies (all of which generalizes traditional HWE tests). Package: r-bioc-limma Architecture: arm64 Version: 3.68.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4062 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-cran-statmod Suggests: r-cran-biasedurn, r-cran-ellipse, r-cran-gplots, r-cran-knitr, r-cran-locfit, r-cran-mass, r-bioc-affy, r-bioc-annotationdbi, r-bioc-biobase, r-bioc-biocstyle, r-bioc-go.db, r-bioc-illuminaio, r-bioc-org.hs.eg.db, r-bioc-vsn Filename: pool/dists/resolute/main/r-bioc-limma_3.68.3-1.ca2604.1_arm64.deb Size: 3070422 MD5sum: 65b59a796aff6af17e8d5ee590e58a20 SHA1: 5c69abc0af222128ab4e1ffd67f995aa8be78491 SHA256: 0fd1dbe568d225415c420a40a2d49ba4664e7e422422b66c69f0aaf5772b4cbf SHA512: c0311b6336b818817e02a4b4eb9969495b7db7bcccea6e2bb9f6f9945ca80e66aafcf3f5e7e988cf8ca0930e4a125c7563c9928f274200ee67eb9c078d391f1e Homepage: https://cran.r-project.org/package=limma Description: Bioc Package 'limma' (Linear Models for Microarray and Omics Data) Data analysis, linear models and differential expression for omics data. Package: r-bioc-lpsymphony Architecture: arm64 Version: 1.40.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3857 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_arm64.deb Size: 1766036 MD5sum: dc9e6d080a4539e65c5cac899b3be446 SHA1: 9b0b9ccd7c77c0668596f2f23e3e2debee6d675c SHA256: ed17f9655d030f9578e49759bf55d68aff8d0e515245defd830a98b68be10c0c SHA512: 1838e3d0302534919658fc71b48334d8687984034a4f8ea721957743b4e438d65ca2776d4d719e30fbb026b092bd89438c699ac52d53c6a381d51e647eb95c1d 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: arm64 Version: 1.78.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3906 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-signal, r-cran-waveslim, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-runit, r-cran-bench Filename: pool/dists/resolute/main/r-bioc-massspecwavelet_1.78.0-1.ca2604.1_arm64.deb Size: 2033888 MD5sum: 108d9f534d7f4c8788247b2e2f22d65c SHA1: b272e1ab28b1ec760cfb29aa3cb2a018dcb103fe SHA256: 88745be54564f39dabe768d86b51e4226cb3eb2962106e99ad9cdc44dd0cb18e SHA512: 6acb8807b051cd529433d3f64d76f30828aecba631a0e8c375df99304659a517a3a2ea01966b3997480c00cdd00f92be64d8caf9abe448332ebec79962360786 Homepage: https://cran.r-project.org/package=MassSpecWavelet Description: Bioc Package 'MassSpecWavelet' (Peak Detection for Mass Spectrometry data using wavelet-basedalgorithms) Peak Detection in Mass Spectrometry data is one of the important preprocessing steps. The performance of peak detection affects subsequent processes, including protein identification, profile alignment and biomarker identification. Using Continuous Wavelet Transform (CWT), this package provides a reliable algorithm for peak detection that does not require any type of smoothing or previous baseline correction method, providing more consistent results for different spectra. See Installed-Size: 1255 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 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_arm64.deb Size: 413712 MD5sum: f41429ab57b7944bde78cd3aec973c1a SHA1: d59bb9f08a47750f39302d99e169c9a51efad023 SHA256: 396dd1155a61e31a6c1a855836f80236b05f09d67e75388df48687206d821186 SHA512: 8ac539f16e5a006e4bc5492de423d926b8e735d1b52b3a3838867256cd07b56d79b3709c886378f16b0385ac2d9ae0e1d706921d3d3eafd211dcc331dc9196e5 Homepage: https://cran.r-project.org/package=mbkmeans Description: Bioc Package 'mbkmeans' (Mini-batch K-means Clustering for Single-Cell RNA-seq) Implements the mini-batch k-means algorithm for large datasets, including support for on-disk data representation. Package: r-bioc-metapod Architecture: arm64 Version: 1.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1344 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_arm64.deb Size: 483686 MD5sum: 34bcd00f7097dcb03248ff369699cfe4 SHA1: 1c3218c5651d2721caf7fbd03e2d920a03e713f2 SHA256: 5b5a3fdce071c02fdc28cbbf06b8aeaa5a31aba1f1d135cf49653d69be7923b5 SHA512: e94e9e59f5145bbbfc0b56ef5ff86a24a87a477ecb4ea834f7ed93e851164f0cc3e183b0b145977066e5bb622e400cc0ee0ffd9c625c7a683dffaa3d582875f5 Homepage: https://cran.r-project.org/package=metapod Description: Bioc Package 'metapod' (Meta-Analyses on P-Values of Differential Analyses) Implements a variety of methods for combining p-values in differential analyses of genome-scale datasets. Functions can combine p-values across different tests in the same analysis (e.g., genomic windows in ChIP-seq, exons in RNA-seq) or for corresponding tests across separate analyses (e.g., replicated comparisons, effect of different treatment conditions). Support is provided for handling log-transformed input p-values, missing values and weighting where appropriate. Package: r-bioc-methylkit Architecture: arm64 Version: 1.38.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5278 Depends: libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), libgcc-s1 (>= 3.0), liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 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_arm64.deb Size: 2510052 MD5sum: 1fcc12a2d0c0d3a173b1ff786e698654 SHA1: 4706d0c42e98cf4d52c281a66ae811fdb770ec1c SHA256: 59aebbebccfbd536d1360a7b9dc501d7230adff7f1e015cb9da8f88851044b45 SHA512: ebbd91d9afb04baebcc2a121be481a861c81de9a75d384b0c20d6093f522b13af60d8b3feb4ee3b255f849e1f8bdd5adb0e79595f45e9ebb451b36a3780530b7 Homepage: https://cran.r-project.org/package=methylKit Description: Bioc Package 'methylKit' (DNA methylation analysis from high-throughput bisulfitesequencing results) methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods and whole genome bisulfite sequencing. It also has functions to analyze base-pair resolution 5hmC data from experimental protocols such as oxBS-Seq and TAB-Seq. Methylation calling can be performed directly from Bismark aligned BAM files. Package: r-bioc-mia Architecture: arm64 Version: 1.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6205 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 4684934 MD5sum: 6ce751ec2ed8a5a2e499f06ca68cebad SHA1: 790407985b9c7a7b28c44deeaee5f57383151acd SHA256: 4a7a068593d6053b4e4ed7cce6e7dbf979702f1360fa88c54bd45c7abcf9a20f SHA512: 3320d2e66ca5fd80145a1648c27da38a878f91ad084ba6ceb27b7842258285adf2122c0334c3237cc00b7bc4ba5c16dfe74fe5412d50d10094570cdb64bd3292 Homepage: https://cran.r-project.org/package=mia Description: Bioc Package 'mia' (Microbiome analysis) mia implements tools for microbiome analysis based on the SummarizedExperiment, SingleCellExperiment and TreeSummarizedExperiment infrastructure. Data wrangling and analysis in the context of taxonomic data is the main scope. Additional functions for common task are implemented such as community indices calculation and summarization. Package: r-bioc-minet Architecture: arm64 Version: 3.70.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-infotheo Filename: pool/dists/resolute/main/r-bioc-minet_3.70.0-1.ca2604.1_arm64.deb Size: 96078 MD5sum: f3ea2a60dd8973c18340814ca394f452 SHA1: 9c8112d8098a2b253f5fea6a7788af28c9772273 SHA256: d86cf2633032331cd434220f0f2f591ef987a4e92972f6d9084c92f2530493c3 SHA512: fd08767298cb34fe9c1f0460f78763fa9a3bbffb17db18b1c90e4b0bdaf3a84ef0c1131f4fbdd64856fd2868e1bacd266d44ac43a87dd88f26bbb34ccb18dcfa Homepage: https://cran.r-project.org/package=minet Description: Bioc Package 'minet' (Mutual Information NETworks) This package implements various algorithms for inferring mutual information networks from data. Package: r-bioc-mofa2 Architecture: arm64 Version: 1.22.0-1.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_arm64.deb Size: 4624774 MD5sum: 1f73e8265d27e4937ee733302a0e51ea SHA1: 3d3904721501ca9850e7265b70fedf6cdc10ddc4 SHA256: 1582120dd4e63366091cfce6aa44fce74006d14e46acb850edda2183c9db7a5b SHA512: 1812d663d2ef169194b3cc045c31fcbeaaa3cb1feda35b98c15b05d3ba4e9671f4a4ec21b64cea3c27cc0c2e073d8d2b7824af6e81d5238be321c6849d76581f Homepage: https://cran.r-project.org/package=MOFA2 Description: Bioc Package 'MOFA2' (Multi-Omics Factor Analysis v2) The MOFA2 package contains a collection of tools for training and analysing multi-omic factor analysis (MOFA). MOFA is a probabilistic factor model that aims to identify principal axes of variation from data sets that can comprise multiple omic layers and/or groups of samples. Additional time or space information on the samples can be incorporated using the MEFISTO framework, which is part of MOFA2. Downstream analysis functions to inspect molecular features underlying each factor, visualisation, imputation etc are available. Package: r-bioc-monocle Architecture: arm64 Version: 2.40.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1796 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1512902 MD5sum: bb2c99bdfd4d83a092724e9bb7d155d8 SHA1: 4ffa4591300b4990cb53d473c50a0a988832ffcf SHA256: ddb0cf1447034b86b7c8de969a359f92a69b031a7786020671e40fbcd1c184f3 SHA512: 49c52268e4b643aaebf84f78f5045cd42b0c96502c4045e7de61d95354fc9690bf58cdde78acae04d3ace43e50949106f1a1720238ee86df1a53daa41c4a8d08 Homepage: https://cran.r-project.org/package=monocle Description: Bioc Package 'monocle' (Clustering, differential expression, and trajectory analysis forsingle- cell RNA-Seq) Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well. Package: r-bioc-motifmatchr Architecture: arm64 Version: 1.34.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 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_arm64.deb Size: 176148 MD5sum: 065ee9536428b69266677728ffcae7f6 SHA1: 4b89874cadc52af9b1bc30068687ab9cf30dbf76 SHA256: 9f77240530603f829c3467ebbfcc65af3faffb2fb92bd287814eb47a37428155 SHA512: b38a218c519cb2869136cb85aa5343dd45960ddb2653f5ff5811099765d90867d14bc2b0e40bac11ebed98635f4bbed40b20ce85aa6ec01c6f85933a74ed4a6d Homepage: https://cran.r-project.org/package=motifmatchr Description: Bioc Package 'motifmatchr' (Fast Motif Matching in R) Quickly find motif matches for many motifs and many sequences. Wraps C++ code from the MOODS motif calling library, which was developed by Pasi Rastas, Janne Korhonen, and Petri Martinmäki. Package: r-bioc-msa Architecture: arm64 Version: 1.44.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3785 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_arm64.deb Size: 1617338 MD5sum: 42efcedce0c5d9eefd08db2e8a49fe25 SHA1: 55b388b04ba7e744074a2b897bad250049d2acf8 SHA256: 27ef9ce81509c94e8af3cd46fb439d180c5343ffd03ab8190d7d0430510ba411 SHA512: 4a224854da8ffa937e3c2e03a96e0efb05989a6832b2c27f5982dee2c9fca7319d3395847774b96bbde1215878299c2cf4f0b8d9b04d3e0bce79d00abbc2e655 Homepage: https://cran.r-project.org/package=msa Description: Bioc Package 'msa' (Multiple Sequence Alignment) The 'msa' package provides a unified R/Bioconductor interface to the multiple sequence alignment algorithms ClustalW, ClustalOmega, and Muscle. All three algorithms are integrated in the package, therefore, they do not depend on any external software tools and are available for all major platforms. The multiple sequence alignment algorithms are complemented by a function for pretty-printing multiple sequence alignments using the LaTeX package TeXshade. 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These functions include mass spectra processing functions (noise estimation, smoothing, binning, baseline estimation), quantitative aggregation functions (median polish, robust summarisation, ...), missing data imputation, data normalisation (quantiles, vsn, ...), misc helper functions, that are used across high-level data structure within the R for Mass Spectrometry packages. Package: r-bioc-msnbase Architecture: arm64 Version: 2.37.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14565 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 7827238 MD5sum: 681b6d090020f3f2bf07f3029f80be31 SHA1: fd036e7e001747457a61f350f22179d354a82a91 SHA256: 25e55d8985c19f95eb285d6724060083c4d3ca14283c0fb6dcf39ccd3234054a SHA512: 9b981561fd9280bd20202d102ae71134b3e014655713d5b8e19810d51ff197290c1deafa8d129adecd6c3b1e02087ec7aee0c234ea0e6a51fecb013f9c86061c Homepage: https://cran.r-project.org/package=MSnbase Description: Bioc Package 'MSnbase' (Base Functions and Classes for Mass Spectrometry and Proteomics) MSnbase provides infrastructure for manipulation, processing and visualisation of mass spectrometry and proteomics data, ranging from raw to quantitative and annotated data. 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Package: r-bioc-msstatsconvert Architecture: arm64 Version: 1.22.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7091 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-log4r, r-cran-checkmate, r-cran-stringi, r-cran-rcpp Suggests: r-cran-tinytest, r-cran-covr, r-cran-knitr, r-cran-arrow, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-msstatsconvert_1.22.0-1.ca2604.1_arm64.deb Size: 2126928 MD5sum: fba6c4e3a678ee7ebdd3543832955f38 SHA1: 57ab4d0af2e4e9eacedea186d4c5cb7e3a1fe264 SHA256: cf4ef4eebbc5c6142a3a0894061dd45b3c0e4fdb3dc2f5251f5772dbafd08f4d SHA512: 9e327adf8e96289b66a652dcea83885de5a806338d33f1cd71e7f9197373e249f299939109beb01da9b5cfbd31966b9590bcb9dcb33dce05e1df36a9b8032e66 Homepage: https://cran.r-project.org/package=MSstatsConvert Description: Bioc Package 'MSstatsConvert' (Import Data from Various Mass Spectrometry Signal ProcessingTools to MSstats Format) MSstatsConvert provides tools for importing reports of Mass Spectrometry data processing tools into R format suitable for statistical analysis using the MSstats and MSstatsTMT packages. Package: r-bioc-multtest Architecture: arm64 Version: 2.68.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1077 Depends: libc6 (>= 2.38), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-biobase, r-cran-survival, r-cran-mass Suggests: r-cran-snow Filename: pool/dists/resolute/main/r-bioc-multtest_2.68.0-1.ca2604.1_arm64.deb Size: 841572 MD5sum: a4cc3dcb3d66ce264ec667e425b0bd0d SHA1: 7ddab968ad0a69fbb79f998d94ff99551f3445f8 SHA256: 4e26aa8241f01cfe365ea4b4ec316566a850e5bbb81304bed034da1ea931bb5c SHA512: 9fc7ca5f6b062e9004d605a8f7f980bb55b6d32cb047d8a5dacdb71e6a72d8b89e99d2c63623f0bcea7885b03aa2b821439330b30480ff0c9701d062e8d0e58a Homepage: https://cran.r-project.org/package=multtest Description: Bioc Package 'multtest' (Resampling-based multiple hypothesis testing) Non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with t-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted p-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments. Package: r-bioc-muscle Architecture: arm64 Version: 3.54.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 799 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biostrings Filename: pool/dists/resolute/main/r-bioc-muscle_3.54.0-1.ca2604.1_arm64.deb Size: 481174 MD5sum: 7b0134ecabea27dacf0cbfe3c346a5f8 SHA1: ce991697b8e54d4c732e8c76dd468fbab9448540 SHA256: e8ec576efbe7dd039a743d9e4f3f0fae63675e7296fdc65a6a30f8cdcddca863 SHA512: e5d71cb4766ad1cf7cdb35153d17b49a99ab125d845e8b2d6d86bb0885b20c72e99871c6e41bb82dfe63d1e770f734cb37593f475a496ddcdf8afa8242a3d85f Homepage: https://cran.r-project.org/package=muscle Description: Bioc Package 'muscle' (Multiple Sequence Alignment with MUSCLE) MUSCLE performs multiple sequence alignments of nucleotide or amino acid sequences. Package: r-bioc-mzr Architecture: arm64 Version: 2.46.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14505 Depends: libc6 (>= 2.43), libcurl4t64 (>= 7.16.2), libgcc-s1 (>= 11), libssl3t64 (>= 3.0.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-protgenerics, r-cran-ncdf4, r-bioc-rhdf5lib Suggests: r-bioc-msdatahub, r-cran-runit, r-bioc-mzid, r-bioc-biocstyle, r-cran-knitr, r-cran-xml, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-mzr_2.46.0-1.ca2604.1_arm64.deb Size: 3992606 MD5sum: c738d590f01c13e4c24a9a0f9e346eb2 SHA1: 0a5e9ed882b7ee18cb5720ece94c5cb5083c5dd0 SHA256: fe6ba8be52dab4878406ba2881441c160a4731a82d8ec326a9f388ea1c922235 SHA512: f00bfeb9ca993b3fffb0304b256122b31cf244ef28b0807bf53ac9fd07266e46fc97b426989c69da93f5d1ca863471a923856c70e087f5f6f29e3023f461265b Homepage: https://cran.r-project.org/package=mzR Description: Bioc Package 'mzR' (parser for netCDF, mzXML and mzML and mzIdentML files (massspectrometry data)) mzR provides a unified API to the common file formats and parsers available for mass spectrometry data. It comes with a subset of the proteowizard library for mzXML, mzML and mzIdentML. The netCDF reading code has previously been used in XCMS. Package: r-bioc-ncdfflow Architecture: arm64 Version: 2.56.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4523 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.2), libstdc++6 (>= 5.2), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-flowcore, r-cran-bh, r-bioc-biobase, r-bioc-biocgenerics, r-cran-cpp11, r-bioc-rhdf5lib Suggests: r-cran-testthat, r-bioc-flowstats, r-cran-knitr Filename: pool/dists/resolute/main/r-bioc-ncdfflow_2.56.0-1.ca2604.1_arm64.deb Size: 1660656 MD5sum: 2a28fb4ef266f56b31c72e9ed98972c9 SHA1: dfabf3dad45397b7b416841ec3e9b9831c38ae13 SHA256: def4e4d5ed312987e7a9e657993e556ee7bfc65955893f0ca2481012a0475944 SHA512: 523735183f495c34717bde0486e9489988c6c443586f97306cc35f96850ab634177673710fa515bc042d5e96682c42102168540c458bee24686ce184b3bbd04c Homepage: https://cran.r-project.org/package=ncdfFlow Description: Bioc Package 'ncdfFlow' (ncdfFlow: A package that provides HDF5 based storage for flowcytometry data.) Provides HDF5 storage based methods and functions for manipulation of flow cytometry data. Package: r-bioc-oligo Architecture: arm64 Version: 1.76.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 30529 Depends: libc6 (>= 2.38), zlib1g (>= 1:1.1.4), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-oligoclasses, r-bioc-biobase, r-bioc-biostrings, r-bioc-affyio, r-bioc-affxparser, r-cran-dbi, r-cran-ff, r-bioc-preprocesscore, r-cran-rsqlite, r-cran-bit Suggests: r-bioc-bsgenome.hsapiens.ucsc.hg18, r-bioc-hapmap100kxba, r-bioc-pd.hg.u95av2, r-bioc-pd.mapping50k.xba240, r-bioc-pd.huex.1.0.st.v2, r-bioc-pd.hg18.60mer.expr, r-bioc-pd.hugene.1.0.st.v1, r-bioc-maqcexpression4plex, r-bioc-genefilter, r-bioc-limma, r-cran-rcolorbrewer, r-bioc-oligodata, r-bioc-biocstyle, r-cran-knitr, r-cran-runit, r-bioc-biomart, r-bioc-annotationdbi, r-bioc-acme, r-cran-rcurl Filename: pool/dists/resolute/main/r-bioc-oligo_1.76.0-1.ca2604.1_arm64.deb Size: 28120086 MD5sum: 6abc192f9b3f25630f57d6d6bf75f70a SHA1: e20b05ea39fe45394e60c6d6f3378f26530f8e5f SHA256: c3673013d5fcf877ea2df5f4bb771761412aca7ab0dc93a2989b9255605f6f08 SHA512: 78abed1b7ebd708a246abbed5436d0fb87cf23f660991dbe2aa8a0c5f7474fd316b730d5c0d7aca5401eb4df03c4e62d253c1cb72ae6928dd434acdae115fe44 Homepage: https://cran.r-project.org/package=oligo Description: Bioc Package 'oligo' (Preprocessing tools for oligonucleotide arrays) A package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files). Package: r-bioc-opencyto Architecture: arm64 Version: 2.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4081 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-flowcore, r-bioc-flowviz, r-bioc-ncdfflow, r-bioc-flowworkspace, r-bioc-flowclust, r-bioc-rbgl, r-bioc-graph, r-cran-data.table, r-cran-rcolorbrewer, r-cran-cpp11, r-cran-bh Suggests: r-bioc-flowworkspacedata, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-testthat, r-bioc-ggcyto, r-bioc-cytoml, r-bioc-flowstats, r-cran-mass Filename: pool/dists/resolute/main/r-bioc-opencyto_2.24.0-1.ca2604.1_arm64.deb Size: 1845280 MD5sum: 573d5ce30f082b7dc821a58ea3ee5f78 SHA1: 3689291debf1644148b89d65d65226bf17fc178b SHA256: d130eb19beb9e03e78da2adfc0cd794ff77e2c787913f7b53d4340a2139e99fa SHA512: 138a949c41ff1c3f209c8764e1945e08840922eb1b44277f3d9a8b7805fa6cc013992d1a0352ced2ed635c39f2cb912b12659a9fb4ca84cb9d1ca17eb7be7d5d Homepage: https://cran.r-project.org/package=openCyto Description: Bioc Package 'openCyto' (Hierarchical Gating Pipeline for flow cytometry data) This package is designed to facilitate the automated gating methods in sequential way to mimic the manual gating strategy. Package: r-bioc-orfik Architecture: arm64 Version: 1.32.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10600 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 4719008 MD5sum: b60224348683e231372c2e827825e1d0 SHA1: ff3408e2787057d7b0c31e0dbf593f746f7055f1 SHA256: af6ca894176aaf6cf6ce9e186ee2555dd397559c136414da47473bfb789fc496 SHA512: 288020b17f7f34c9e11da378b3a0017eb6d0c878340b293f381e3c47c7fe1b30ffa3d53513703a7a53d1ca8fa8204f62e55e22b31511e1cce3b6380295c05218 Homepage: https://cran.r-project.org/package=ORFik Description: Bioc Package 'ORFik' (Open Reading Frames in Genomics) R package for analysis of transcript and translation features through manipulation of sequence data and NGS data like Ribo-Seq, RNA-Seq, TCP-Seq and CAGE. It is generalized in the sense that any transcript region can be analysed, as the name hints to it was made with investigation of ribosomal patterns over Open Reading Frames (ORFs) as it's primary use case. ORFik is extremely fast through use of C++, data.table and GenomicRanges. Package allows to reassign starts of the transcripts with the use of CAGE-Seq data, automatic shifting of RiboSeq reads, finding of Open Reading Frames for whole genomes and much more. Package: r-bioc-pcamethods Architecture: arm64 Version: 2.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1781 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1385168 MD5sum: f5e186bf9469fa48540735b42d8c439d SHA1: 55c37749b12146044896f880e11afa8440122659 SHA256: cea8c7404244bd6e8e095fe7d493474065aedc0fb63af3bd8d8c9f4d68748e4d SHA512: 0fc0a89ffcc60301ed449167fbf2468a2eec96e3b1a9781141b015880ca144a3949086dcd8c1fe5d3db6501eef8115352ecc173205d7007e9f18620a5deaf93d Homepage: https://cran.r-project.org/package=pcaMethods Description: Bioc Package 'pcaMethods' (A collection of PCA methods) Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany. Package: r-bioc-pcatools Architecture: arm64 Version: 2.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8705 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_arm64.deb Size: 6122932 MD5sum: d31e5051cb1c90daae0d619801f799b8 SHA1: b2d087daee65407623d67ac15e7f791d5ddaa090 SHA256: 678b2f99cf7bb935e6496b7946fb0230d9ffeefb038b1ea40f849ffa1309ceb2 SHA512: 9acf02ac478586e3ff2f1e366a3c8e8a3bf26251d44341325d270166c1fb44ce623a41179ee63f3d1f4cfdafe53fe0f5dcf11fb8e372e87f2cf879f982ee6eca Homepage: https://cran.r-project.org/package=PCAtools Description: Bioc Package 'PCAtools' (PCAtools: Everything Principal Components Analysis) Principal Component Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. It extracts the fundamental structure of the data without the need to build any model to represent it. This 'summary' of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. the 'principal components'), while at the same time being capable of easy interpretation on the original data. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular - users can also identify optimal number of principal components via different metrics, such as elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data. Package: r-bioc-plotgardener Architecture: arm64 Version: 1.18.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4226 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 3572136 MD5sum: fbd181a6bb0b3e84cd90a2221c45be54 SHA1: 9e70f76293d25f2a17e4a128def368dd52828890 SHA256: 78609bd36ef04a1dc219a3d6a17e1af6ee724ad930e472d3c4e81e54ec05b6d8 SHA512: dc06a26f8c163039444ae02305da63f59552e991f2c07e18c1ae5f9b0f24779188b34b0d71faf67f50238b188257610d63c342bf2bc9bacf9c058fd3e79dda29 Homepage: https://cran.r-project.org/package=plotgardener Description: Bioc Package 'plotgardener' (Coordinate-Based Genomic Visualization Package for R) Coordinate-based genomic visualization package for R. It grants users the ability to programmatically produce complex, multi-paneled figures. Tailored for genomics, plotgardener allows users to visualize large complex genomic datasets and provides exquisite control over how plots are placed and arranged on a page. Package: r-bioc-preprocesscore Architecture: arm64 Version: 1.74.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 456 Depends: libc6 (>= 2.38), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/resolute/main/r-bioc-preprocesscore_1.74.0-1.ca2604.1_arm64.deb Size: 149878 MD5sum: d0ca44da2ea6a495444c61a7dad321f6 SHA1: a5829ff1354a355f554c61409fe97f8a29fa01dd SHA256: d36af4461414616d6609e744d9500e998333f84cb733792cd8df57272a456aa3 SHA512: d28832f70841362c9b5b9e7346f7c85c14109d1098d7749ddd978138bc936012fa0e8ed1ce47c8e25e237ccad6b17b560db0bc7c116fb6d751fb001e876faa8c Homepage: https://cran.r-project.org/package=preprocessCore Description: Bioc Package 'preprocessCore' (A collection of pre-processing functions) A library of core preprocessing routines. Package: r-bioc-pwalign Architecture: arm64 Version: 1.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1133 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges, r-bioc-biostrings, r-bioc-xvector Suggests: r-cran-runit Filename: pool/dists/resolute/main/r-bioc-pwalign_1.8.0-1.ca2604.1_arm64.deb Size: 750820 MD5sum: 2f75565bb4afa5ebe65580b179a476e3 SHA1: b4b93eb313776630cbb6b8e0e6d6904f1c7a7177 SHA256: 496ba2501ede412ea5b31a4a41bfecf1df11beb798cfe2fd7e9c5f817aacaf3f SHA512: 40cb590f36c53dc0608df721d213a77dd261c9ce0d5aea909ca04ab6367e6fb9fc18bbf295177f9cf03a809c776c57c758eabbf0fe73ba6ebfa9b814cc7d3304 Homepage: https://cran.r-project.org/package=pwalign Description: Bioc Package 'pwalign' (Perform pairwise sequence alignments) The two main functions in the package are pairwiseAlignment() and stringDist(). The former solves (Needleman-Wunsch) global alignment, (Smith-Waterman) local alignment, and (ends-free) overlap alignment problems. The latter computes the Levenshtein edit distance or pairwise alignment score matrix for a set of strings. Package: r-bioc-qpgraph Architecture: arm64 Version: 2.46.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4836 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-bioc-annotate, r-bioc-graph, r-bioc-biobase, r-bioc-s4vectors, r-bioc-biocparallel, r-bioc-annotationdbi, r-bioc-iranges, r-bioc-seqinfo, r-bioc-genomicranges, r-bioc-genomicfeatures, r-cran-mvtnorm, r-cran-qtl, r-bioc-rgraphviz Suggests: r-cran-runit, r-bioc-biocgenerics, r-bioc-biocstyle, r-bioc-genefilter, r-bioc-org.eck12.eg.db, r-cran-rlecuyer, r-cran-snow, r-bioc-category, r-bioc-gostats Filename: pool/dists/resolute/main/r-bioc-qpgraph_2.46.0-1.ca2604.1_arm64.deb Size: 3919846 MD5sum: f5c18aeeee24680043ef26184c35ddf7 SHA1: 10f7a459c3aa7638d3030325e2d65f6d3b6f7c51 SHA256: 136026fcdd6f5fecaa02ca99d11e74c645e5af8f6697042cd97a35bd16c20f75 SHA512: cd99049192d6031da42a4e31c4ec1d704deb74eca532e1d06ac9fbdee02c68b53c107944ae30240dd35424ff937e85736110b08613b8a26f43a384bc48f81327 Homepage: https://cran.r-project.org/package=qpgraph Description: Bioc Package 'qpgraph' (Estimation of Genetic and Molecular Regulatory Networks fromHigh-Throughput Genomics Data) Estimate gene and eQTL networks from high-throughput expression and genotyping assays. Package: r-bioc-quasr Architecture: arm64 Version: 1.52.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5426 Depends: libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), libgcc-s1 (>= 3.0), liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 13.1), zlib1g (>= 1:1.2.3.4), 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_arm64.deb Size: 3320666 MD5sum: 75737bef27ebed05c9dcb2d599505f7f SHA1: 2ab745a968ab0487c0e4417b472af84a04528a99 SHA256: 17a291c7d57580c039600101e876a9f7cfb502650633803da7baa183dd3d41b3 SHA512: ca2175c1d824e07797f779a7b96b3f0de9fa123ba06a47382f3a4bd02ccf72b95c2a87a07137ed7f859b8b5c17178d398788d0f2049f3f38f61e2fc9f43d0250 Homepage: https://cran.r-project.org/package=QuasR Description: Bioc Package 'QuasR' (Quantify and Annotate Short Reads in R) This package provides a framework for the quantification and analysis of Short Reads. It covers a complete workflow starting from raw sequence reads, over creation of alignments and quality control plots, to the quantification of genomic regions of interest. Read alignments are either generated through Rbowtie (data from DNA/ChIP/ATAC/Bis-seq experiments) or Rhisat2 (data from RNA-seq experiments that require spliced alignments), or can be provided in the form of bam files. Package: r-bioc-rbgl Architecture: arm64 Version: 1.88.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6261 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-graph, r-cran-bh Suggests: r-bioc-rgraphviz, r-cran-xml, r-cran-runit, r-bioc-biocgenerics, r-bioc-biocstyle, r-cran-knitr Filename: pool/dists/resolute/main/r-bioc-rbgl_1.88.0-1.ca2604.1_arm64.deb Size: 4342732 MD5sum: 87b3a181d219d34e79dcaa1d004ec5f0 SHA1: 9ab6ae4a1fa3b90d578558df5da43014a1f8de36 SHA256: 9c2bac3ce82e82dd1f853c7548fcb3e49ca5381377afe9f35c4043814b02ea53 SHA512: c196d22ccbc0acd1fd16cf8a9186e21efdc8b97b1bb0783573005a9b00a6ae1dab1daf94e7b5b0bde177ab35814806f578c714face88d9b6fe5c8fc5fcc8f23c Homepage: https://cran.r-project.org/package=RBGL Description: Bioc Package 'RBGL' (An interface to the BOOST graph library) A fairly extensive and comprehensive interface to the graph algorithms contained in the BOOST library. Package: r-bioc-rbowtie2 Architecture: arm64 Version: 2.18.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6206 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), 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_arm64.deb Size: 2047572 MD5sum: 74aba368873ba57e7b10fb2eb75e27f5 SHA1: c50cd86f5964804d72026c7e17963233855dba0c SHA256: d4aad5ab4690dd275bcae76ee14ef8c2fe2eb50ad9d5fabdce0cb5234382fbc0 SHA512: 189c178b0c4e882851319e26b3bbb89c28b2b31b7eab911263a897cc755ecc99a1d5dbf5e91e6f2f2ae805e53b910dc3fd8c28238722bf009208bd5b1c958665 Homepage: https://cran.r-project.org/package=Rbowtie2 Description: Bioc Package 'Rbowtie2' (An R Wrapper for Bowtie2 and AdapterRemoval) This package provides an R wrapper of the popular bowtie2 sequencing reads aligner and AdapterRemoval, a convenient tool for rapid adapter trimming, identification, and read merging. The package contains wrapper functions that allow for genome indexing and alignment to those indexes. The package also allows for the creation of .bam files via Rsamtools. Package: r-bioc-rbowtie Architecture: arm64 Version: 1.52.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3208 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 14), zlib1g (>= 1:1.2.6), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-testthat, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-rbowtie_1.52.0-1.ca2604.1_arm64.deb Size: 868394 MD5sum: 04b22cab236d35d6193e4bd648d9e73f SHA1: a68199942ccede9719e8fbd6b326fbc415cc3f8c SHA256: bebc7f0e7b1a3893c7bdfbc4ddf0d11f4514b76f2b302446b7be4f7e04e13356 SHA512: 61fae33c119719c85ed4d00ea3205aedbd08858b3252be4d44d5d661a8277223608460fbeec3938d38fa39b74a132c751199c3c15aaeb60cde1a362178df7a1a Homepage: https://cran.r-project.org/package=Rbowtie Description: Bioc Package 'Rbowtie' (R bowtie wrapper) This package provides an R wrapper around the popular bowtie short read aligner and around SpliceMap, a de novo splice junction discovery and alignment tool. 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: arm64 Version: 1.72.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 513 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_arm64.deb Size: 229736 MD5sum: 58e7051f6f3d8f064cec1554a12c7027 SHA1: 5e9080b3e31ef8157d613072c9147d5c0a3627fb SHA256: 37192282ff29eaee9e24f5961715a34350813db17b0d7142dcc60cff18f719af SHA512: f2306905cc254ca554e0b56be11ecf5780517c272aeeabbca823075efadf8b82dac993385c4c3ee76eb5e6f0e8d84ce5cc11f83d38dabff4117249f4d2ace480 Homepage: https://cran.r-project.org/package=Rdisop Description: Bioc Package 'Rdisop' (Decomposition of Isotopic Patterns) In high resolution mass spectrometry (HR-MS), the measured masses can be decomposed into potential element combinations (chemical sum formulas). Where additional mass/intensity information of respective isotopic peaks is available, decomposition can take this information into account to better rank the potential candidate sum formulas. To compare measured mass/intensity information with the theoretical distribution of candidate sum formulas, the latter needs to be calculated. This package implements fast algorithms to address both tasks, the calculation of isotopic distributions for arbitrary sum formulas (assuming a HR-MS resolution of roughly 30,000), and the ranked list of sum formulas fitting an observed peak or isotopic peak set. Package: r-bioc-rfastp Architecture: arm64 Version: 1.22.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3934 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_arm64.deb Size: 2749548 MD5sum: ba20a6b0cd9ed221fd45731ae6e9fb60 SHA1: 7a7a4dee7a434f0456fd9e5eba1c75357f5910b9 SHA256: 9742bef14ea610811c3614cffea0bc8976b19c8e16d7e1d9d21304b96a2e746f SHA512: c4971ec137e97a22f5275ff86ce1c6ecfb82082677d5a24f2832e623a70ae7cbd86320d8447ed3e0cce3868d1184d4b62d1f575395541befc857b4931205a1fb Homepage: https://cran.r-project.org/package=Rfastp Description: Bioc Package 'Rfastp' (An Ultra-Fast and All-in-One Fastq Preprocessor (QualityControl, Adapter, low quality and polyX trimming) and UMISequence Parsing).) Rfastp is an R wrapper of fastp developed in c++. fastp performs quality control for fastq files. including low quality bases trimming, polyX trimming, adapter auto-detection and trimming, paired-end reads merging, UMI sequence/id handling. Rfastp can concatenate multiple files into one file (like shell command cat) and accept multiple files as input. Package: r-bioc-rgraphviz Architecture: arm64 Version: 2.56.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2789 Depends: libc6 (>= 2.38), 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_arm64.deb Size: 1690660 MD5sum: 05497b9dc483b52a5b4b87bcde547278 SHA1: 3c699178d2af43355df0c5b8392dccc7471818b4 SHA256: 0a37a78b1c3115271018b12eade75d8c7870406710f31f7caaed59fd58f0dc6e SHA512: 38ddf8ea65cf0522b3de7df13885fe2f8f0ff36fbc112dd1f6b2c6b09b8c82222894cc5a002b165adc3a9973af7e6904219d53332c08bd7561a63669845ee933 Homepage: https://cran.r-project.org/package=Rgraphviz Description: Bioc Package 'Rgraphviz' (Provides plotting capabilities for R graph objects) Interfaces R with the AT and T graphviz library for plotting R graph objects from the graph package. Package: r-bioc-rgreat Architecture: arm64 Version: 2.14.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3669 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 3500268 MD5sum: 767ba5e88c19a644021dfa2b67ce0598 SHA1: 9997d169db5d670285bc5fd514cb56981783c694 SHA256: a74af82d9652d7d1f49566c3e30a7885fa3cbe67b62aeab7bfbe119d4f7ce38d SHA512: b4d74b50946a06a4c68c3ba9eadb20a679dd8e8afb0b9e4015f7acfe6b0815f3486cdc571ff18f56aca75c1bcbcc682bef5989fc54921512d61391e95c5cdef1 Homepage: https://cran.r-project.org/package=rGREAT Description: Bioc Package 'rGREAT' (GREAT Analysis - Functional Enrichment on Genomic Regions) GREAT (Genomic Regions Enrichment of Annotations Tool) is a type of functional enrichment analysis directly performed on genomic regions. This package implements the GREAT algorithm (the local GREAT analysis), also it supports directly interacting with the GREAT web service (the online GREAT analysis). Both analysis can be viewed by a Shiny application. rGREAT by default supports more than 600 organisms and a large number of gene set collections, as well as self-provided gene sets and organisms from users. Additionally, it implements a general method for dealing with background regions. Package: r-bioc-rhdf5 Architecture: arm64 Version: 2.56.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8498 Depends: libc6 (>= 2.38), libcurl4t64 (>= 7.16.2), libgcc-s1 (>= 11), libssl3t64 (>= 3.0.0), libstdc++6 (>= 5), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-rhdf5filters, r-bioc-rhdf5lib Suggests: r-cran-bench, r-bioc-biocparallel, r-bioc-biocstyle, r-cran-bit64, r-cran-curl, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-bioc-rhdf5_2.56.0-1.ca2604.1_arm64.deb Size: 2713168 MD5sum: 0ff2e09035e00350f4ee3fb86bfb8536 SHA1: cd20f97251fdfdc13e61f122dc50f44b7931dce1 SHA256: 984511f397b3370474d852562e36ab774a754f4628f63a466280d3d8b5f4db27 SHA512: 5847ca06d5267ecf31a76f7a61afabfa8eac47956818a993a1e5c24a4d9103f1801ceec086112503c7a4c5c69090f31ab886deb27b4b47030b55d4a17f10c7d6 Homepage: https://cran.r-project.org/package=rhdf5 Description: Bioc Package 'rhdf5' (R Interface to HDF5) This package provides an interface between HDF5 and R. 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Package: r-bioc-rsubread Architecture: arm64 Version: 2.26.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 37807 Depends: libc6 (>= 2.38), zlib1g (>= 1:1.2.2.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-bioc-rsubread_2.26.0-1.ca2604.1_arm64.deb Size: 10829306 MD5sum: c79f759f4bb63c1c2c8833b843b26fe9 SHA1: 62bcb3f7798e030d493e38e9203c929a74081796 SHA256: f73d38c8edfc73061b62ae18c1a2b212bbd9d5279f8ab6c79a343a8b5f204647 SHA512: 45c0e6f735f2396431d39b04d4f28a66bca90b5d81c2c5e96734079659a2d4a57adc79a64a1934969c49fa153f3b4c9d81b16a047861d419f30bf76f16188459 Homepage: https://cran.r-project.org/package=Rsubread Description: Bioc Package 'Rsubread' (Mapping, quantification and variant analysis of sequencing data) Alignment, quantification and analysis of RNA sequencing data (including both bulk RNA-seq and scRNA-seq) and DNA sequenicng data (including ATAC-seq, ChIP-seq, WGS, WES etc). Includes functionality for read mapping, read counting, SNP calling, structural variant detection and gene fusion discovery. Can be applied to all major sequencing techologies and to both short and long sequence reads. Package: r-bioc-rtracklayer Architecture: arm64 Version: 1.72.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6597 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_arm64.deb Size: 5179488 MD5sum: 3b24e3c67bd1abd2ef466b390fec8816 SHA1: e5b74cabe7ff90405a1788e6f95d0bbed476d5eb SHA256: 0adae002ae452da9f9807f3e11edea8c8aaccf0610daf4b7ee349f7b5602d417 SHA512: b819a3b09296c9179a8038e55ef38bdb66abfad003008705ab694dfefcd1fe3c771aca3caf69ee331ea1ae481c6b688fbd4c9f74d580f9d273ce31d4529ea8d7 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. Package: r-bioc-s4arrays Architecture: arm64 Version: 1.12.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1905 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-abind, r-bioc-biocgenerics, r-bioc-s4vectors, r-bioc-iranges Suggests: r-bioc-biocparallel, r-bioc-sparsearray, r-bioc-delayedarray, r-bioc-hdf5array, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-s4arrays_1.12.0-1.ca2604.1_arm64.deb Size: 1017726 MD5sum: 7da7256ea8f2be261e0b18376aa6469b SHA1: cc6eec9ecbce22e07af6379b0b9843462986d971 SHA256: a4800371d2bda955d98a90b3a500ca0a4d9e7ec84130f70a64758ad329056eab SHA512: b8a509b9b9394692bf130e1cafee0658f480aa1c315cbd9062a1beb0fef32217f7a8f7114d983c0d792b3e4f6b92c26397b607cd61dd8f9efdd12cb1b69323be Homepage: https://cran.r-project.org/package=S4Arrays Description: Bioc Package 'S4Arrays' (Foundation of array-like containers in Bioconductor) The S4Arrays package defines the Array virtual class to be extended by other S4 classes that wish to implement a container with an array-like semantic. 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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). 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Package: r-bioc-scde Architecture: arm64 Version: 2.40.0-1.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), 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_arm64.deb Size: 2210576 MD5sum: 797e130cebcec19da4d23667ffa1ad18 SHA1: ab969c0397c0bb21825c5785dd54f53041e9f3c8 SHA256: 1b780faa006b1d365e9344748cae0e9cc2c251cef8a11aaef098baeb6dad1ee3 SHA512: 4683ac545b69d7e061cc8262fe3be0eb65f9d5cf0ac1c0b51622edf8e6461c0cdce704d739bcfa259bdaf92e4cca11f87d1eea0962b0f75f5c0a677d8a6e5691 Homepage: https://cran.r-project.org/package=scde Description: Bioc Package 'scde' (Single Cell Differential Expression) The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify and characterize putative cell subpopulations based on transcriptional signatures. The overall approach to the differential expression analysis is detailed in the following publication: "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi: 10.1038/nmeth.2967). The overall approach to subpopulation identification and characterization is detailed in the following pre-print: "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734). Package: r-bioc-scran Architecture: arm64 Version: 1.40.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2326 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1266912 MD5sum: 92086d71d5e0cced4992338784c967ee SHA1: 47075cf73305ae2fd93a764fc6d5b8001b50d5e2 SHA256: e651119a9e0e8cbe3a05f6b795885ff4e7ee7d8f954fbff56eb5ea2f0899e6b4 SHA512: 8d34645e2be2f8b8d01544c0b838d9fafee78832ab58b632782798bab4825c58891035ed63bacb62cfdabc191d0d9495e5788b4db023d8648c457d394ac55d52 Homepage: https://cran.r-project.org/package=scran Description: Bioc Package 'scran' (Methods for Single-Cell RNA-Seq Data Analysis) Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and other common tasks in routine single-cell analysis workflows. Package: r-bioc-scrapper Architecture: arm64 Version: 1.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5947 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), 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-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_arm64.deb Size: 2743346 MD5sum: 655d56d4b5e02cea2361a6d6dd6cc001 SHA1: 9dbd3d58e147968ef52797b609339e5476ae7ce0 SHA256: 4b968491de1cdefc466e289b063b702a919297ae696c66823c67650e7fcf6559 SHA512: fcf1b5b9b84fb63b35b78e7ae174af7ca5ca2f4232551ac9c699f2c125b36689d4bde6a0af030c59ac1d1f523e0dda52cde1b466c7330111082ebde2da3b7503 Homepage: https://cran.r-project.org/package=scrapper Description: Bioc Package 'scrapper' (Bindings to C++ Libraries for Single-Cell Analysis) Implements R bindings to C++ code for analyzing single-cell (expression) data, mostly from various libscran libraries. Each function performs an individual step in the single-cell analysis workflow, ranging from quality control to clustering and marker detection. Additional wrappers are provided for easy construction of end-to-end workflows involving Bioconductor objects like SingleCellExperiments. Package: r-bioc-screpertoire Architecture: arm64 Version: 2.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12887 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 9568496 MD5sum: c85e959a8129d70a1574fc4a9919cc32 SHA1: dcc5624ee948e421798a0d6f8885a15049dbe4c5 SHA256: 48860a2d6f362cde764f0b9d5c0ce491e803ff4a66602f4fbdcbf79c1dce5908 SHA512: eef1b7290491fff13842fd77b1f7c0b1623d1605bfe8fcc26b5105b30fb3f5ef626ba00310071c70b66d09d05194d03b8f73f4cc91f4b66e9a2b7ced28af8b24 Homepage: https://cran.r-project.org/package=scRepertoire Description: Bioc Package 'scRepertoire' (A toolkit for single-cell immune receptor profiling) scRepertoire is a toolkit for processing and analyzing single-cell T-cell receptor (TCR) and immunoglobulin (Ig). The scRepertoire framework supports use of 10x, AIRR, BD, MiXCR, TRUST4, and WAT3R single-cell formats. The functionality includes basic clonal analyses, repertoire summaries, distance-based clustering and interaction with the popular Seurat and SingleCellExperiment/Bioconductor R single-cell workflows. Package: r-bioc-scuttle Architecture: arm64 Version: 1.22.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1768 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 731040 MD5sum: 9277b37fc8c0f29824c7926974abee36 SHA1: 2b0b2c855d732024c359427316e7aadd25ba7f56 SHA256: d130ea91fee83b471a04d4b6c0a2c2f8cf9ab606a92af129caa0ad609da52ebc SHA512: 00c2a06e88566828c558a288d6955848daac351414f46c1484b48dd58a3fe8bfa23de4e2042c82632df61d4fbe4b26e2409ee34b8c90c3e9fba995dbb8eb36e4 Homepage: https://cran.r-project.org/package=scuttle Description: Bioc Package 'scuttle' (Legacy Utilities for Single-Cell RNA-Seq Analysis) Provides some legacy utility functions for performing single-cell analyses. Most of these functions are deprecated in favor of newer, more performant alternatives. We just keep this package around for back-compatibility and to point to the replacement functions. Package: r-bioc-seqarray Architecture: arm64 Version: 1.52.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7047 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-gdsfmt, r-cran-digest, r-bioc-s4vectors, r-bioc-iranges, r-bioc-genomicranges, r-bioc-seqinfo, r-bioc-biostrings Suggests: r-bioc-biobase, r-bioc-biocgenerics, r-bioc-biocparallel, r-cran-runit, r-cran-rcpp, r-bioc-snprelate, r-cran-crayon, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-bioc-rsamtools, r-bioc-variantannotation Filename: pool/dists/resolute/main/r-bioc-seqarray_1.52.0-1.ca2604.1_arm64.deb Size: 3780694 MD5sum: d1b47e72b8cd60cd358e04ff61eb27fe SHA1: ec95ee3f8a735f57f132702d27e0376f981cc3ee SHA256: 97ce1195d06189901063c44409eef0bf01a292028e7e6044d904672315da99b3 SHA512: 0ad7b3c18c1c2b3ef2237da302b85c611be77df906ffd2d07948c8293842b2e22862a5ed8fc8738d75b1faabc7e215f4cd0432d44e97f5796e2c5ff4b71b779d Homepage: https://cran.r-project.org/package=SeqArray Description: Bioc Package 'SeqArray' (Data management of large-scale whole-genome sequence variantcalls using GDS files) Data management of large-scale whole-genome sequencing variant calls with thousands of individuals: genotypic data (e.g., SNVs, indels and structural variation calls) and annotations in SeqArray GDS files are stored in an array-oriented and compressed manner, with efficient data access using the R programming language. Package: r-bioc-shortread Architecture: arm64 Version: 1.70.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8288 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_arm64.deb Size: 5274574 MD5sum: e5fc7b81069232ac601e5190a3858edf SHA1: 2aad66d51cfb85c4748423d2d1b79dcd47ee2f05 SHA256: 58d62c89b634f744393f288996d29f2fface70a929e20968ca25a6b02bec57c6 SHA512: d29c763ce3592edff5ec5f2fc0c53f9012655a75cd3912692f67417afde55d97e8c42322e8b59ac9774abeebcfdb131d7fc80f5188edfe47842d57135cf1484c Homepage: https://cran.r-project.org/package=ShortRead Description: Bioc Package 'ShortRead' (FASTQ input and manipulation) This package implements sampling, iteration, and input of FASTQ files. The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, and easily manipulated for a diversity of purposes. The package also contains legacy support for early single-end, ungapped alignment formats. Package: r-bioc-signaturesearch Architecture: arm64 Version: 1.26.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 90514 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_arm64.deb Size: 88003412 MD5sum: 083dd26b7ade4edb15ea8226cc288dd0 SHA1: f56f53b361be8a3c7a0a843d05460ecef91105f8 SHA256: 3c3d8ac84b17d1803ba80a5254effc5aa8bc44adb5eac3d7279f539517ee2e5c SHA512: 14187845a763a26171247cc1043dab40c1694cc6be830ffccbde23b2226dbacc8d478c0417034a2e88920b4cc12a61639d0190215d92d003bb94270badde2dd3 Homepage: https://cran.r-project.org/package=signatureSearch Description: Bioc Package 'signatureSearch' (Environment for Gene Expression Searching Combined withFunctional Enrichment Analysis) This package implements algorithms and data structures for performing gene expression signature (GES) searches, and subsequently interpreting the results functionally with specialized enrichment methods. Package: r-bioc-simona Architecture: arm64 Version: 1.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2565 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_arm64.deb Size: 1899604 MD5sum: 79d043e78a702e58be91601f00715469 SHA1: 6166c19cd03218374c6acd1bbd39260d2c85ebf0 SHA256: d29a00a7f4de9c0d05922087de7fe300d04fb0c1189bc134368b0f1fbfe37cac SHA512: 754ca20329a175f44b29e497fb8ed41cac4189fb1c7df047cd3c6c87423f59a21bc46a63ec349013d628500f80206cc44b9825749e89ab085a1f82bc9357bfad Homepage: https://cran.r-project.org/package=simona Description: Bioc Package 'simona' (Semantic Similarity on Bio-Ontologies) This package implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. It provides a robust toolbox supporting over 70 methods for semantic similarity analysis. Package: r-bioc-singler Architecture: arm64 Version: 2.14.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2030 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_arm64.deb Size: 912890 MD5sum: e6624caf217404da20bac57a6659e189 SHA1: fcef4d13d9fe97bf97e61aa276ffa5bf9f4967c4 SHA256: 8f4ded025a81505ee45a247f9a782e3540995f1a27b1d2ea3fa261cc421689b9 SHA512: db574c98d84b606bb94299ddc637913557710e4b510b6030b296bca9f4a5ece12357fcf168db93137eeebb41c5bbdd3d6b015d1851c9c54397fab4b3105f916d Homepage: https://cran.r-project.org/package=SingleR Description: Bioc Package 'SingleR' (Reference-Based Single-Cell RNA-Seq Annotation) Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently. Package: r-bioc-snprelate Architecture: arm64 Version: 1.46.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6388 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_arm64.deb Size: 3836152 MD5sum: 2a6763c4776a251d3ac5326025050c13 SHA1: 369455cda03497210b312cc4f298d61e665633c7 SHA256: f2b6b112afd32f11472fbf3fdc70a52dfb438d68e90aeae9ed666190eda39469 SHA512: 22e00d98ffe385db202aadf10fd6c5cba4b3810e154728e7b9b4c8086799342517e1925c78fe6b1c093e8e6625d090d7f45898b50a2449a69abf2e162d148017 Homepage: https://cran.r-project.org/package=SNPRelate Description: Bioc Package 'SNPRelate' (Parallel Computing Toolset for Relatedness and PrincipalComponent Analysis of SNP Data) Genome-wide association studies (GWAS) are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed an R package SNPRelate to provide a binary format for single-nucleotide polymorphism (SNP) data in GWAS utilizing CoreArray Genomic Data Structure (GDS) data files. The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. SNPRelate is also designed to accelerate two key computations on SNP data using parallel computing for multi-core symmetric multiprocessing computer architectures: Principal Component Analysis (PCA) and relatedness analysis using Identity-By-Descent measures. The SNP GDS format is also used by the GWASTools package with the support of S4 classes and generic functions. The extended GDS format is implemented in the SeqArray package to support the storage of single nucleotide variations (SNVs), insertion/deletion polymorphism (indel) and structural variation calls in whole-genome and whole-exome variant data. Package: r-bioc-snpstats Architecture: arm64 Version: 1.62.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9274 Depends: libc6 (>= 2.38), zlib1g (>= 1:1.1.4), r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-matrix, r-bioc-biocgenerics Suggests: r-cran-hexbin Filename: pool/dists/resolute/main/r-bioc-snpstats_1.62.0-1.ca2604.1_arm64.deb Size: 8462652 MD5sum: 948bb0043f5fabb90a67d48917d0b136 SHA1: 6503b85fcfa688e07d4e8930f42fdf67cc22ef5a SHA256: 943664a32457f258bcb30a89fca6dd2098202fcc3c46542d319377e761ef8418 SHA512: f0eced9bfad367d740cf7fef4c47e8c70237d436feb1f1b2f35292ed82b3d521dbd6a7f305a663817a0b0ef262b248aa8c2b8dff2fa1dcb5916ff2701941a86a Homepage: https://cran.r-project.org/package=snpStats Description: Bioc Package 'snpStats' (SnpMatrix and XSnpMatrix classes and methods) Classes and statistical methods for large SNP association studies. This extends the earlier snpMatrix package, allowing for uncertainty in genotypes. Package: r-bioc-sparsearray Architecture: arm64 Version: 1.12.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3051 Depends: libc6 (>= 2.17), libgomp1 (>= 9), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-bioc-biocgenerics, r-bioc-matrixgenerics, r-bioc-s4vectors, r-bioc-s4arrays, r-cran-matrixstats, r-bioc-iranges, r-bioc-xvector Suggests: r-bioc-hdf5array, r-bioc-experimenthub, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle Filename: pool/dists/resolute/main/r-bioc-sparsearray_1.12.2-1.ca2604.1_arm64.deb Size: 1609172 MD5sum: 13cae82935e4397af153ae9bce71ea56 SHA1: 80c469a57251931d980535c7c9d7c5dbedd743b9 SHA256: bf8ed8f7940610a7d34e78c0395435fb6e0bf99dc7f9f9455e24eab8c7372ce1 SHA512: b58f79af75794d92c742e18e589f872bc8be092df8d04928b1293dbee04cffd7cb2138ef20e8f345ab5c2bef8ead8e14ad2abf7aa66f3a70257eb4a7a8eec115 Homepage: https://cran.r-project.org/package=SparseArray Description: Bioc Package 'SparseArray' (High-performance sparse data representation and manipulation inR) The SparseArray package provides array-like containers for efficient in-memory representation of multidimensional sparse data in R (arrays and matrices). The package defines the SparseArray virtual class and two concrete subclasses: COO_SparseArray and SVT_SparseArray. Each subclass uses its own internal representation of the nonzero multidimensional data: the "COO layout" and the "SVT layout", respectively. SVT_SparseArray objects mimic as much as possible the behavior of ordinary matrix and array objects in base R. In particular, they suppport most of the "standard matrix and array API" defined in base R and in the matrixStats package from CRAN. Package: r-bioc-sparsematrixstats Architecture: arm64 Version: 1.24.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2045 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_arm64.deb Size: 1138232 MD5sum: c1d96463f49760fa4a95acb446befc05 SHA1: 82fb49b77fc0459d2b419179f43cfae7dfe6cd44 SHA256: 44bfe36f3fdf6aa4e7df4ed4b2579966e72bb8848d324b945ab6a1309037600f SHA512: 8ee248e04e82385be79931d64ad1991d439f676536a3d7ae04b8a177a6bedc0ce6fe541b9ec107461bc1ca35846dc6f504f6bcc57f9a0d750aca6c7cd85dfe95 Homepage: https://cran.r-project.org/package=sparseMatrixStats Description: Bioc Package 'sparseMatrixStats' (Summary Statistics for Rows and Columns of Sparse Matrices) High performance functions for row and column operations on sparse matrices. For example: col / rowMeans2, col / rowMedians, col / rowVars etc. Currently, the optimizations are limited to data in the column sparse format. This package is inspired by the matrixStats package by Henrik Bengtsson. Package: r-bioc-survcomp Architecture: arm64 Version: 1.62.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1019 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-survival, r-cran-prodlim, r-cran-ipred, r-cran-suppdists, r-cran-kernsmooth, r-cran-survivalroc, r-cran-bootstrap, r-cran-rmeta Suggests: r-cran-hmisc, r-cran-clinfun, r-cran-xtable, r-bioc-biobase, r-cran-biocmanager Filename: pool/dists/resolute/main/r-bioc-survcomp_1.62.0-1.ca2604.1_arm64.deb Size: 839108 MD5sum: f14ad1e8468e9ce626d0732a15d9985d SHA1: 7bb37676878a6cdf7ed9645cc6172959d6e8553d SHA256: ef0392ad993ba1a10add9c37b9a141f735772551c2d909e1c737f22b3600d744 SHA512: 1f578f7738e11ec75de6b04fce0e354c5c1cd69cca7b390a03c3e5a63d764bbdb957413a92c8865dcb35ff65fc53548da1da6d53ac34c4fec9f9333773d48104 Homepage: https://cran.r-project.org/package=survcomp Description: Bioc Package 'survcomp' (Performance Assessment and Comparison for Survival Analysis) Assessment and Comparison for Performance of Risk Prediction (Survival) Models. Package: r-bioc-sva Architecture: arm64 Version: 3.60.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1011 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_arm64.deb Size: 460948 MD5sum: 47f0ed6e4d1d3381534a99ab77d4a3d4 SHA1: 0fb9deb550d08b1ef959fdf3a7a760320a476813 SHA256: c94cb174f7b7ab40330a92337779a88b047225c9e75c021453d0344844aa4faf SHA512: e417acf51295cdd55a2a4ce5e8d0aeb53570a82d9245bf71fb7be3c3cb8ee8ef5aff4b89fa618049c8733de630c0fab2119190123ae0a8fb79487f9014e8fa31 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). Package: r-bioc-tfbstools Architecture: arm64 Version: 1.50.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2792 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biobase, r-bioc-biostrings, r-bioc-pwalign, r-bioc-biocgenerics, r-bioc-biocparallel, r-bioc-bsgenome, r-cran-catools, r-bioc-dirichletmultinomial, r-bioc-seqinfo, r-bioc-genomicranges, r-cran-gtools, r-bioc-iranges, r-cran-dbi, r-cran-rsqlite, r-bioc-rtracklayer, r-bioc-seqlogo, r-bioc-s4vectors, r-cran-tfmpvalue, r-cran-xml, r-bioc-xvector Suggests: r-bioc-biocstyle, r-bioc-jaspar2014, r-cran-knitr, r-cran-testthat, r-bioc-jaspar2016, r-bioc-jaspar2018, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-bioc-tfbstools_1.50.0-1.ca2604.1_arm64.deb Size: 1383168 MD5sum: 96d7f5fa344dd98e7b983a367775f02d SHA1: c56232cd4b13622d477787b9798c42fdcb773b33 SHA256: a947920deda0995746c408d343ebba137d49720e17a00055b8e2d2a5febddfaf SHA512: 39f4e611897aa45ae9cde2f64d5e2c112d29f3f4dcc4b4711e1b3b8cffe41e9b1dc11dd5136c3648436ee81d044ba9bc7485d55585a93dc40f7e483742774c0d Homepage: https://cran.r-project.org/package=TFBSTools Description: Bioc Package 'TFBSTools' (Software Package for Transcription Factor Binding Site (TFBS)Analysis) TFBSTools is a package for the analysis and manipulation of transcription factor binding sites. It includes matrices conversion between Position Frequency Matirx (PFM), Position Weight Matirx (PWM) and Information Content Matrix (ICM). It can also scan putative TFBS from sequence/alignment, query JASPAR database and provides a wrapper of de novo motif discovery software. Package: r-bioc-tweedeseq Architecture: arm64 Version: 1.58.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-mass, r-bioc-limma, r-bioc-edger, r-bioc-cqn Suggests: r-bioc-tweedeseqcountdata, r-cran-xtable Filename: pool/dists/resolute/main/r-bioc-tweedeseq_1.58.0-1.ca2604.1_arm64.deb Size: 355318 MD5sum: e485d514ec94c9939a4ec30230dc8713 SHA1: 9b35adcc513172cdf7e7f724a5f0136898305f43 SHA256: a6b92f35a0ff61349425cc7ad5a8d9400eeb12fb4babd8fd9abe8b68c13f500d SHA512: 7c6d83a598c76ba31b44c4d1f10a0e219d9f36f93de7c9ac86636b288448003aaee019a5520d3e243e7a95a09ef25c849ddb120998a36bf24c6e8efa57fa71c3 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. Package: r-bioc-universalmotif Architecture: arm64 Version: 1.30.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6862 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass, r-cran-ggplot2, r-cran-yaml, r-bioc-iranges, r-cran-rcpp, r-bioc-biostrings, r-bioc-biocgenerics, r-bioc-s4vectors, r-cran-rlang, r-bioc-matrixgenerics, r-cran-rcppthread Suggests: r-cran-spelling, r-cran-knitr, r-cran-bookdown, r-bioc-tfbstools, r-cran-rmarkdown, r-bioc-motifdb, r-cran-testthat, r-bioc-biocparallel, r-bioc-seqlogo, r-bioc-motifstack, r-cran-dplyr, r-cran-ape, r-bioc-ggtree, r-cran-processx, r-cran-ggseqlogo, r-cran-cowplot, r-bioc-genomicranges, r-bioc-ggbio Filename: pool/dists/resolute/main/r-bioc-universalmotif_1.30.1-1.ca2604.1_arm64.deb Size: 5358934 MD5sum: d415160fd1256ea1d996cda27fc9c11b SHA1: 3b0c520ffb4322101b69e2e98c413375d9c98d7e SHA256: 02d7238cac499c3401181b0aadebf012ca5efd870236ce0be50972fa62f124a2 SHA512: c22890503a4824a896553e11a3761c1ee6176d4f177dd96a8b4f7118fa2b2ae9699515c18d634231da1fd618b24f2cdb4ea34e82b1f5f316a3719a803920ee1e Homepage: https://cran.r-project.org/package=universalmotif Description: Bioc Package 'universalmotif' (Import, Modify, and Export Motifs with R) Allows for importing most common motif types into R for use by functions provided by other Bioconductor motif-related packages. 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: arm64 Version: 1.58.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6924 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_arm64.deb Size: 3662046 MD5sum: 2023b3e11abf52c23d2c53def37b558b SHA1: 98eed161a7e480c6cdf5b65af26eb659a7a02dcc SHA256: 12fcf843dd7fd5663c8416a5e4a9191d171081542ba8869c99c0dd282bd0b458 SHA512: 8ae984aab1891d2e0fbd85aebbb2338b7fd6fcbc8b7f663493ce64a48196a8f734c6f16df05b629a59661835bd173f807d9261b59964aed8938bcd576526e97a 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: arm64 Version: 3.80.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4509 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2005140 MD5sum: 145470275d07a55e6d6571d60befce06 SHA1: 01f6f7e444264e65c5651023184f779ac1d7ae70 SHA256: 20c88fd45ca3a5cdbed29eafccf7e3126d242e261484a710a05a10ce6625a5ea SHA512: d483dace8b78bf698e262820a346f3fcb659526690dd133e603b583ea7eb22e5ff77a11f754f78a678b22eb15cff20322ea36a9386a9ea2407b5fca77b7b8dc8 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. Package: r-bioc-xcms Architecture: arm64 Version: 4.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 19097 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-bioc-biocparallel, r-bioc-msnbase, r-bioc-mzr, r-bioc-biobase, r-bioc-biocgenerics, r-bioc-protgenerics, r-cran-lattice, r-bioc-massspecwavelet, r-bioc-s4vectors, r-bioc-iranges, r-bioc-summarizedexperiment, r-bioc-mscoreutils, r-bioc-msfeatures, r-bioc-msexperiment, r-bioc-spectra, r-cran-progress, r-cran-rcolorbrewer, r-bioc-metabocoreutils, r-cran-data.table Suggests: r-bioc-biocstyle, r-cran-catools, r-cran-knitr, r-bioc-faahko, r-cran-ncdf4, r-cran-testthat, r-cran-pander, r-cran-rmarkdown, r-cran-maldiquant, r-cran-pheatmap, r-cran-rann, r-bioc-multtest, r-bioc-msbackendmgf, r-cran-signal, r-cran-mgcv, r-bioc-rhdf5, r-bioc-msdatahub Filename: pool/dists/resolute/main/r-bioc-xcms_4.10.0-1.ca2604.1_arm64.deb Size: 11205130 MD5sum: 3e84cc760f97a0a5918ab54effeb8236 SHA1: 5e8ce46daa804f65fc6086d692102241141175f9 SHA256: 83dd84bd5bfc2b3fea56affa6cc301dd5c10a8d152b5108e953a90268517f384 SHA512: 1d99fe40ad9fc6b97a6fab5653dac4154b73e686f182ee392ad0b08d4cff7c3c53a7c140f67c7dae149c547948ba16580779e50f7848616c0d262c34bc1dac7b Homepage: https://cran.r-project.org/package=xcms Description: Bioc Package 'xcms' (LC-MS and GC-MS Data Analysis) Framework for processing and visualization of chromatographically separated and single-spectra mass spectral data. 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: arm64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1680 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_arm64.deb Size: 919930 MD5sum: 388d20273cf8dc1f19bd0dad8d1dcd29 SHA1: 3d7603ee1c9b6f4ddf9db7884b14dd846fcfb6fb SHA256: 37c01b440622805a844138b3dd3e8fb36a6b138a3b468834f13ccdc12a737cf9 SHA512: 018987bdca35b896aac3b0beab2558512019d68abaad7c66e68a24e358bac6e8f92eeca8a5f37fb65981ea2f61e7a8767c80feb98495a450113e6896ed6d896c 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: arm64 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_arm64.deb Size: 34022 MD5sum: 9daccab1a7ac39d92c0031fc26abc0d2 SHA1: 6299ac6875ce49e0ef8d26e0ea00ef8c0e31b402 SHA256: bc17af03d843680e7c8c3b88e5d6d9650090f9880f8aa2aad5c8d98e4675de3f SHA512: a8dce945d756087275b59c3c2e0620c374a8f8b9f9c01775ed655404229d2e8fd0229c1b57e59e9ec9af3af4b5b48ae39cdfb23401f7f5da6a2ea5f39e706442 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: arm64 Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6057 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_arm64.deb Size: 942208 MD5sum: 5547abb73ac2423602ba3a87d5ab654a SHA1: adaf8c3e72196ee8dce8660d5198211cea3996d9 SHA256: 9b3b9ed384d11eee3c7ae5728c571a18d2e3c6b777b527fed099517a1dc2374a SHA512: 719cf518cc46fc94ab69b8497f4258860f80b1c154c80b1a9864d611f9b0ad6b8ea1c4348f2af4391c3aa0c0d43a8a305a3e5903f97a9311f8531d914b850628 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: arm64 Version: 0.15.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 73554 MD5sum: f0fef46bc09dcad90431b82d795ebab8 SHA1: c4623f8f2cfae73fb32c43b14b568d7e1e8fc423 SHA256: e7eeed6750da1905703ecfd790f7fcf68a644f52d666bfa838468a6cc3fb279c SHA512: 2e45cb6138d8274c5393c400dc45e4cbb83f9ac0bb21cd3af4525be9f95e55a0a37a0e52c82aea8de47812acaa71709203df546141ed1b81e654eb161210f574 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: arm64 Version: 2.0-1.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-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_arm64.deb Size: 2902332 MD5sum: da2e682d673c34038ac48f39969050ea SHA1: 09169337aa38a3d3e1315d81be039bec02f40992 SHA256: 3b6442b3c3500826512e80763c0d92b473cfa47ff078f17ab5b8d3da934af42f SHA512: 3c58c6fa2f8bebbc29043313137a9ae60002261a2797d876320f50196ba7476f37d7c5e1e5948961140f8911969dce2e334cd86c51560657bee5aadbc3edcb0d 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: arm64 Version: 1.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2831 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abc, r-cran-abind, r-cran-plyr, r-cran-hmisc Suggests: r-cran-ggplot2, r-cran-abc.data Filename: pool/dists/resolute/main/r-cran-abctools_1.1.8-1.ca2604.1_arm64.deb Size: 2796864 MD5sum: d4832778e2b07bd5ef4efc6b012ba683 SHA1: b16688f438363a4a75d684611f4c97f8ccddb64b SHA256: b62f7ae25ce24a9eb9da61aa97edfaec4065b3d6bf2abf7705216e9f19b5ad2d SHA512: d0ff53dc33b8c63924fb587295aac5c003db055535986978f51c0ce8c4a40e0c60d6f3a75d4c78c8620f3c3c27c28ed6fe8ae6c9c43c9474cbb0521ddd14a9ad Homepage: https://cran.r-project.org/package=abctools Description: CRAN Package 'abctools' (Tools for ABC Analyses) Tools for approximate Bayesian computation including summary statistic selection and assessing coverage. See Nunes and Prangle (2015) and Rodrigues, Prangle and Sisson (2018) . Package: r-cran-aberrance Architecture: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 222284 MD5sum: 6c2bdbe92483ea72ade323f0938ccaf1 SHA1: f8272aaba8bd1846ccadfe86f5b5fd835d59f57b SHA256: c54a53462f07a1cc334bc95fd20d98ef86f324ebb11cb8929a1612ed5c2be4c9 SHA512: 442b95fedd495ecba99cc0d1d24a3cf82a18b7819c09214a7e7fa9ea91d4b870a7b3ccca5da3bfce421a6c52a49b8805c27231512089c78de3a2b12cf80f0c71 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: arm64 Version: 0.4.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2219 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_arm64.deb Size: 945430 MD5sum: ee14636b4bda1dba2662a49ebe0cf074 SHA1: 95dcd853e64913044d46d3b32f59f94dba467fd2 SHA256: 6e744cf9c548083db5e246c91bcbfe13215861951bfa3d5e3940ad79c2416770 SHA512: 10de3af59b0909415ecb350fa15a52965a90a1c2672439d8d2b81af988b376eb038459407612feb190a73ff60c9d88d1a34447e314dfcf094ebcfde81ed2dd3c 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: arm64 Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 799 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_arm64.deb Size: 385978 MD5sum: a88e62714570ec507ffb45add965be12 SHA1: 15b93e809804e20fbfac79349902c440593beb07 SHA256: 1138041251fbe7e4a15197e9a94b7fe32db74e4f35b3ca7443bb2a81840d5530 SHA512: 88f07a03d0e8cdcd28b8396aa09c38bc3a78ee4dd02002b16a148a2719412eb4d98e0d9ebff90136e72f833e3080bd873b0ff1f87bb53a87f699b448858b14bc Homepage: https://cran.r-project.org/package=ABM Description: CRAN Package 'ABM' (Agent Based Model Simulation Framework) A high-performance, flexible and extensible framework to develop continuous-time agent based models. Its high performance allows it to simulate millions of agents efficiently. Agents are defined by their states (arbitrary R lists). The events are handled in chronological order. This avoids the multi-event interaction problem in a time step of discrete-time simulations, and gives precise outcomes. The states are modified by provided or user-defined events. The framework provides a flexible and customizable implementation of state transitions (either spontaneous or caused by agent interactions), making the framework suitable to apply to epidemiology and ecology, e.g., to model life history stages, competition and cooperation, and disease and information spread. The agent interactions are flexible and extensible. The framework provides random mixing and network interactions, and supports multi-level mixing patterns. It can be easily extended to other interactions such as inter- and intra-households (or workplaces and schools) by subclassing an R6 class. It can be used to study the effect of age-specific, group-specific, and contact- specific intervention strategies, and complex interactions between individual behavior and population dynamics. This modeling concept can also be used in business, economical and political models. As a generic event based framework, it can be applied to many other fields. More information about the implementation and examples can be found at . Package: r-cran-abn Architecture: arm64 Version: 3.1.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5543 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_arm64.deb Size: 4019122 MD5sum: 6fe96f6a4bb229bb98ceb367f46ab131 SHA1: d8b6364ae8a368725ff2e8973dc6ae4ea11f2041 SHA256: 8cb76448a0af2a1ff00656e5ca28052573858240f76115f00b519804b9bae709 SHA512: fed4ee5b67cbc0e48d7e6c72328e1956cd87cb755ae9306fdc47ae7470d2574429232ab2871d2a9c8db8d1e8fa71bcd6c8ce3f5b1e06c72cde5f445c12d2cb19 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: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 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_arm64.deb Size: 217644 MD5sum: b305dc4ed0d8f08a3634db4cb4563e13 SHA1: 458876052dadd35c5de112efd2762493da21a29a SHA256: 2910c8bcfe13e29e0f9633388e679bbb9c3a9be1f517569b195ea718588b5878 SHA512: a9358e37063e1be570aa338d0383deeaee786a4bb06d3376c7f2acfcd7189602b7add8a9c3b82754eba445aa6c6629fbf192777ddc2ffff642f5f6a2e683cf95 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: arm64 Version: 1.2-1.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-glasso Filename: pool/dists/resolute/main/r-cran-abundant_1.2-1.ca2604.1_arm64.deb Size: 42168 MD5sum: 0c15d3a4b67d0bcda4ac77abd1652664 SHA1: 9211e4fef11f1012bdf50596a05883d9f9d7be0b SHA256: 81d7e99429bd753c9bff6d754ee77241e0ae762694181b1e84396030f8eafedc SHA512: 4df7865de33b5d2f637b1b5f1bca2d48caee81787c16fe98877613f5bac6ce85080fd361a67d4abdcb2f7609d867a0a2cae36a2fdd33ce3d1e8e15def2548ebe 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: arm64 Version: 3.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 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_arm64.deb Size: 271428 MD5sum: 95af3bbbdf7863b729229a8ee2d56f22 SHA1: 4934ae505347032c2e7b6153df31194143f32d0c SHA256: 23faf8ea11aac7337735190a29e54f95c49e892c4e60a50fce95c59c0fa93b44 SHA512: 4f3670b343ec643d5cb827b8bd2dae1716298f9bb7b0d53c936cd75f902513f822f664ec86498ee19017478503fb93baa6ef9bf7f2c813ecb1f99b47342107b1 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: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1153 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 766414 MD5sum: 4e0a2ad6b031210943d8c773bb923418 SHA1: 91f92cf1c3c9db77a65080a1976185fb730a2592 SHA256: ce4d0d6516389ab367a8451ef533ac3fcb9bbb409c91aac8472d939f720de0c4 SHA512: 01da4df17e0e85495d5d6bead9b3eaa3411e3f6a85872c12edb5e3452e44b6ed5857f54d9e411e6844b8e7e0b76d6c6a51620f194d945d2e23e593955abc50d9 Homepage: https://cran.r-project.org/package=AcceptReject Description: CRAN Package 'AcceptReject' (Acceptance-Rejection Method for Generating Pseudo-RandomObservations) Provides a function that implements the acceptance-rejection method in an optimized manner to generate pseudo-random observations for discrete or continuous random variables. Proposed by von Neumann J. (1951), , the function is optimized to work in parallel on Unix-based operating systems and performs well on Windows systems. The acceptance-rejection method implemented optimizes the probability of generating observations from the desired random variable, by simply providing the probability function or probability density function, in the discrete and continuous cases, respectively. Implementation is based on references CASELLA, George at al. (2004) , NEAL, Radford M. (2003) and Bishop, Christopher M. (2006, ISBN: 978-0387310732). Package: r-cran-acdm Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1731 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_arm64.deb Size: 1398336 MD5sum: 737e9c1c46374debd84da95f12ec0668 SHA1: acbd3cbbf8200344ba99ad61bc3a3ec2136830a8 SHA256: 42b9421b9f3e4a06b66c8a25ce95b4f8983da1cd610eef98e310261498f5e68b SHA512: b85c5e3931ab4030a1df0c0f9e05143d10e1df7d0ee3975720b519809e5270b4e737016ad20bbfa882cdacc6cb840a2a9f22ad4a087220e001e92b318f764263 Homepage: https://cran.r-project.org/package=ACDm Description: CRAN Package 'ACDm' (Tools for Autoregressive Conditional Duration Models) Provides tools for autoregressive conditional duration (ACD, Engle and Russell, 1998) models. Functions to create trade, price, or volume durations from transaction data, perform diurnal adjustments, fit various ACD models, and test them. Package: r-cran-acebayes Architecture: arm64 Version: 1.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1798 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_arm64.deb Size: 1606078 MD5sum: 27db11c2d44a646381548452e71fe823 SHA1: b90e1a85c1a6354a0241cfdf1174f6183b27aa62 SHA256: 21acd71aa13627ef91e0d7d7008ac2b7a4cbc77667fc1041317a55c01a787936 SHA512: 58ecf1bb6d63d38995ae84fcf082418934498749a706582c789883619438abcb22354a3f949f4c3f7d7ee89cf220542ea5b66923a5cf22711c5abf20d5533da6 Homepage: https://cran.r-project.org/package=acebayes Description: CRAN Package 'acebayes' (Optimal Bayesian Experimental Design using the ACE Algorithm) Optimal Bayesian experimental design using the approximate coordinate exchange (ACE) algorithm. Package: r-cran-acepack Architecture: arm64 Version: 1.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-acepack_1.6.3-1.ca2604.1_arm64.deb Size: 84888 MD5sum: 0b34b6da3b829882c912c42594feadf6 SHA1: 90a878c1c22f9fc501bdf84c537bcb4d99d38b85 SHA256: 71826e7605d7551df646a5b5448d9ad6af444b246addd1b1b8e07c5a4c002bd9 SHA512: 0d59250aa2b207e19f23a08ca6fae4a617e3f9a46c5b95315cdae37641cd9ba398908a9ccd6e5d6f8c7dbffe5a6725d8b752343c3c65e2f328c7ea5f5b315d2e Homepage: https://cran.r-project.org/package=acepack Description: CRAN Package 'acepack' (ACE and AVAS for Selecting Multiple Regression Transformations) Two nonparametric methods for multiple regression transform selection are provided. The first, Alternating Conditional Expectations (ACE), is an algorithm to find the fixed point of maximal correlation, i.e. it finds a set of transformed response variables that maximizes R^2 using smoothing functions [see Breiman, L., and J.H. Friedman. 1985. "Estimating Optimal Transformations for Multiple Regression and Correlation". Journal of the American Statistical Association. 80:580-598. ]. Also included is the Additivity Variance Stabilization (AVAS) method which works better than ACE when correlation is low [see Tibshirani, R. 1986. "Estimating Transformations for Regression via Additivity and Variance Stabilization". Journal of the American Statistical Association. 83:394-405. ]. A good introduction to these two methods is in chapter 16 of Frank Harrell's "Regression Modeling Strategies" in the Springer Series in Statistics. A permutation independence test is included from [Holzmann, H., Klar, B. 2025. "Lancaster correlation - a new dependence measure linked to maximum correlation". Scandinavian Journal of Statistics. 52(1):145-169 ]. Package: r-cran-acet Architecture: arm64 Version: 1.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2548 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1140212 MD5sum: c682d3fa98ac6f6047ed8840b3e0550b SHA1: 3554e1b557b5b252e0416c400a323bcf6c0cfdfb SHA256: e7f5029412c1ce46fdec8e996acba51ee7f06959e3bea20be23c8debf1b2b2dc SHA512: 02d1036e7d316c72771e9a51f8ec679f00dd8143909e334b7027d18be25650ee043f5cccc84e1ed453ecbf6ad80a96967b5690870799dc9f518b194393f6657e 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: arm64 Version: 1.0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1990 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_arm64.deb Size: 1855182 MD5sum: 35fc8e62e2e72a0a282c6e580c97623c SHA1: 7c08c4be93c154b2542673de83bd3e4a261aa2a3 SHA256: 2a80835b453903553e3fd3461abe5e1ab0cb465e45dc1055cb46c8569a66ddbb SHA512: 7c000d1804ecb68e77d432e66b3f0aca82726874496e665541db61dfd11e71b7c836a04b021d626f91917ebe1d799e43cb1025d82fc3f79eb87bd9338f0bd353 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: arm64 Version: 1.4-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 143494 MD5sum: e424070cef89b397aff817c93fa9262a SHA1: 8b262e36097cd4607b620c7480227163ae7e607a SHA256: 444968034b4bac2dbfcaf28a3cdb41e340cf459c584064ace159b6367b222106 SHA512: 4042da36041d48d02a55e4d1a21b18bdd26621d66e2348dc18b82b642bbc21057c88719998e24430bd8e111d7f5991ea33146cd7e09056aeb0e9c6f819c20db7 Homepage: https://cran.r-project.org/package=ACTCD Description: CRAN Package 'ACTCD' (Asymptotic Classification Theory for Cognitive Diagnosis) Cluster analysis for cognitive diagnosis based on the Asymptotic Classification Theory (Chiu, Douglas & Li, 2009; ). Given the sample statistic of sum-scores, cluster analysis techniques can be used to classify examinees into latent classes based on their attribute patterns. In addition to the algorithms used to classify data, three labeling approaches are proposed to label clusters so that examinees' attribute profiles can be obtained. 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PDP applied to physical activity data can identify transitions from wakefulness to sleep and vice versa. Baek, Jonggyu, Banker, Margaret, Jansen, Erica C., She, Xichen, Peterson, Karen E., Pitchford, E. Andrew, Song, Peter X. K. (2021) An Efficient Segmentation Algorithm to Estimate Sleep Duration from Actigraphy Data . Package: r-cran-activelearning4spm Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 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-rfast, r-cran-mvnfast, r-cran-rrcov, r-cran-catools, r-cran-abind, r-cran-proc, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-activelearning4spm_0.1.0-1.ca2604.1_arm64.deb Size: 176532 MD5sum: f4f56a6ab0b1177043d4a85a31f272d5 SHA1: 6fe764f48b0ca869f99a1a1b947b8faa3ae89591 SHA256: f44d12a68fcce10f7dc3b48e742dde3ccfefa2fd26bb099d21c50692616795ef SHA512: 0b439045bbfe032bdfc0f9c7e4dee40c101bb4848899d8b5fbac30ea1183bbf32bac4026a1dd26755600a5cc7f56c068534091b9efc515797956281908553aff Homepage: https://cran.r-project.org/package=ActiveLearning4SPM Description: CRAN Package 'ActiveLearning4SPM' (Active Learning for Process Monitoring) Implements the methodology introduced in Capezza, Lepore, and Paynabar (2025) for process monitoring with limited labeling resources. The package provides functions to (i) simulate data streams with true latent states and multivariate Gaussian observations as done in the paper, (ii) fit partially hidden Markov models (pHMMs) using a constrained Baum-Welch algorithm with partial labels, and (iii) perform stream-based active learning that balances exploration and exploitation to decide whether to request labels in real time. The methodology is particularly suited for statistical process monitoring in industrial applications where labeling is costly. Package: r-cran-actuar Architecture: arm64 Version: 3.3-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2124 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_arm64.deb Size: 1411964 MD5sum: e7977c6dac44108a757293843bff88c8 SHA1: 28b907740d87e668bb43557957ada21a4199517c SHA256: 791c983c5ae9afe3a0d2d0b775584c810dab3e35b49c921aba8f9f51c947d416 SHA512: 63c9b3f8c7c1e33a106f1fcf3e9821fcbe4ced1a6b92d04a4457fc78e5b341089c833d5ee054aad29be96779dcb0f4a7e9096bf142a5f53625f25a83acebe533 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: arm64 Version: 2.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4535 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 1507654 MD5sum: c15b26bc64efbae11a13a30e1edc65d9 SHA1: 7330114c5e5c43a7ffc57e868e106c05b3ae586f SHA256: 8ed2e2d737ef7910e5c51885521d6525cd2ec41dafd543771233dabf71126c5f SHA512: 1ede21fb6106f7d6ac0573341d202d75bb7015af9d89a4db364e7329dc190c0f9109b4203ff790e89fcbcbee2858303a45b6100d33efadd5fea330a191cc885f Homepage: https://cran.r-project.org/package=adapt3 Description: CRAN Package 'adapt3' (Adaptive Dynamics and Community Matrix Model Projections) Runs projections of groups of matrix projection models (MPMs), allowing density dependence mechanisms to work across MPMs. This package was developed to run both adaptive dynamics simulations such as pairwise and multiple invasibility analyses, and community projections in which species are represented by MPMs. All forms of MPMs are allowed, including integral projection models (IPMs). Package: r-cran-adaptgauss Architecture: arm64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1256 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 525936 MD5sum: 09ba0f9ae9482de0529e228c2cbcbe20 SHA1: 9881b4b0f9ed74316b5a2458ec70e051bf7f8949 SHA256: a943f94bea797f0e8d34c70ff61181e9f5244378950f777daac58126960419dc SHA512: eb8d1bed515dd057c5d63d22479c04d31219bee511878cf94151c507c1197cdb866ab2c885b21bed238ed5d22be1684165e2683195be43a80feab1a558ff6525 Homepage: https://cran.r-project.org/package=AdaptGauss Description: CRAN Package 'AdaptGauss' (Gaussian Mixture Models (GMM)) Multimodal distributions can be modelled as a mixture of components. The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf. PDE has been designed in particular to identify groups/classes in a dataset. Precise limits for the classes can be calculated using the theorem of Bayes. Verification of the model is possible by QQ plot, Chi-squared test and Kolmogorov-Smirnov test. The package is based on the publication of Ultsch, A., Thrun, M.C., Hansen-Goos, O., Lotsch, J. (2015) . Package: r-cran-adaptivetau Architecture: arm64 Version: 2.3-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-adaptivetau_2.3-2-1.ca2604.1_arm64.deb Size: 209734 MD5sum: 68ad2de78614872a9fc3d6767231d948 SHA1: 47f2aec145b07bfdb5d6d80cf494282510eff68d SHA256: 838230627c5a91f447d9702678c03d4ab90ae61ccfa597b4ecc0ac61939fa7e8 SHA512: 7af288b689bc7a25b4aa4e9d138f262064832a3c785c24b16f769008164c3552ccd899c07795278d0d54d2710cdd097222e98841b0bebe99e08347874f9b56f1 Homepage: https://cran.r-project.org/package=adaptivetau Description: CRAN Package 'adaptivetau' (Tau-Leaping Stochastic Simulation) Implements adaptive tau leaping to approximate the trajectory of a continuous-time stochastic process as described by Cao et al. (2007) The Journal of Chemical Physics (aka. the Gillespie stochastic simulation algorithm). This package is based upon work supported by NSF DBI-0906041 and NIH K99-GM104158 to Philip Johnson and NIH R01-AI049334 to Rustom Antia. Package: r-cran-adaptivpt Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 201 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_arm64.deb Size: 69792 MD5sum: a725f06ab6399fb0c9c43887f85f660d SHA1: 99d8bd56829e7c7270e74ac694d5625bda97a20f SHA256: 345d53441053398d744b2fbe35936cc6a48f28daeee8c00a1bdf999b6bcf0f23 SHA512: d6b4b4fc16e50777556b694c76a6aa41f9e25ccd8616cbee35fc22f0c54ea4b72896b1544e517e6e7d9aadf0be217b1393926f1b77443cdc97503bc84aa6ff3e Homepage: https://cran.r-project.org/package=adaptIVPT Description: CRAN Package 'adaptIVPT' (Adaptive Bioequivalence Design for In-Vitro Permeation Tests) Contains functions carrying out adaptive procedures using mixed scaling approach to establish bioequivalence for in-vitro permeation test (IVPT) data. Currently, the package provides procedures based on parallel replicate design and balanced data, according to the U.S. Food and Drug Administration's "Draft Guidance on Acyclovir" . Potvin et al. (2008) provides the basis for our adaptive design (see Method B). For a comprehensive overview of the method, refer to Lim et al. (2023) . This package reflects the views of the authors and should not be construed to represent the views or policies of the U.S. Food and Drug Administration. Package: r-cran-adar Architecture: arm64 Version: 0.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1122 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-triebeard Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-adar_0.3.5-1.ca2604.1_arm64.deb Size: 507448 MD5sum: cb190b7d29dd2f1588db1200a7c6d66c SHA1: 24ef264a27b7c27eed200eba72c967c6ac763f25 SHA256: 77c09db54cdf437dee925851c4d061d49eca6b3964e7c6f5d544b07abb2a07ee SHA512: a4d43d63c13e246e15608f39ec3af29434e361e25665c4ab1dee74c2e5725ea49db3dd79be66d9d80f6555842a36eeab5d45e8f831ba9ba5717ed33087f597ab Homepage: https://cran.r-project.org/package=adaR Description: CRAN Package 'adaR' (A Fast 'WHATWG' Compliant URL Parser) A wrapper for 'ada-url', a 'WHATWG' compliant and fast URL parser written in modern 'C++'. 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Package: r-cran-ade4 Architecture: arm64 Version: 1.7-24-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6284 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-pixmap, r-cran-sp, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ade4tkgui, r-cran-adegraphics, r-cran-adephylo, r-cran-adespatial, r-cran-ape, r-cran-circstats, r-cran-deldir, r-cran-lattice, r-cran-spdep, r-cran-splancs, r-cran-waveslim, r-cran-progress, r-cran-foreach, r-cran-doparallel, r-cran-iterators, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ade4_1.7-24-1.ca2604.1_arm64.deb Size: 5411754 MD5sum: 683b9425167a6efe76133aafc317b670 SHA1: 7a55f67b4142fc6169b0f2c23672f94a4112227d SHA256: a4d5802e63e19c4101f93e4f9b90701fc0e73c08d0ab4e2e9eb81d0d9b769be2 SHA512: 36ef0a4e0b88b1168a26aa436cb6b9cea909b45c71f4f21b66ca359948d653ca47ec0a992f455d93c9a6356c4d63af5dc34eda9a6bfd4be5ed1c30075733f14d Homepage: https://cran.r-project.org/package=ade4 Description: CRAN Package 'ade4' (Analysis of Ecological Data: Exploratory and Euclidean Methodsin Environmental Sciences) Tools for multivariate data analysis. 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Similar to the R package 'glmnet' in scope of models, and in computational speed. This package provides R bindings to the C++ code underlying the corresponding Python package 'adelie'. These bindings offer a general purpose group elastic net solver, a wide range of matrix classes that can exploit special structure to allow large-scale inputs, and an assortment of generalized linear model classes for fitting various types of data. The package is an implementation of Yang, J. and Hastie, T. (2024) . 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(2024) . Specifically, this package is written to work with trial designs created by the 'adoptr' package (Kunzmann et al. (2021) ; Pilz et al. (2021) )). Apart from the a priori evaluation of performance characteristics, this package also allows for the evaluation of the implemented estimators on real datasets, and it implements methods to calculate p-values. 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Package: r-cran-adjsurvci Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 596 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_arm64.deb Size: 268116 MD5sum: 1ee3d54f41d2ae41a28037fb207bbde6 SHA1: a1350966747be455e64eac3113bd8a4299a9e883 SHA256: 932a4bef316bb39b1e91d24cd150282d05a7ae3584d9ef0caada6c685f472505 SHA512: 6a804f682591ececdbf836ce274c2e50fc311810e8b9cc21e4ffcf08ca9b7208b57251dd758b15cd4be23da39773484421344d95aa70be4a2bd7faeb599c4e92 Homepage: https://cran.r-project.org/package=adjSURVCI Description: CRAN Package 'adjSURVCI' (Parameter and Adjusted Probability Estimation for Right-CensoredData) Functions in this package fit a stratified Cox proportional hazards and a proportional subdistribution hazards model by extending Zhang et al., (2007) and Zhang et al., (2011) respectively to clustered right-censored data. The functions also provide the estimates of the cumulative baseline hazard along with their standard errors. Furthermore, the adjusted survival and cumulative incidence probabilities are also provided along with their standard errors. Finally, the estimate of cumulative incidence and survival probabilities given a vector of covariates along with their standard errors are also provided. 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Interprets and translates, factorizes and negates SOP - Sum of Products expressions, for both binary and multi-value crisp sets, and extracts information (set names, set values) from those expressions. Other functions perform various other checks if possibly numeric (even if all numbers reside in a character vector) and coerce to numeric, or check if the numbers are whole. It also offers, among many others, a highly versatile recoding routine and some more flexible alternatives to the base functions 'with()' and 'within()'. SOP simplification functions in this package use related minimization from package 'QCA', which is recommended to be installed despite not being listed in the Imports field, due to circular dependency issues. 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Furthermore, the method is extended to jointly estimate multiple graphical models. For more details, please see and . Package: r-cran-aftgee Architecture: arm64 Version: 1.2.1-1.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), 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_arm64.deb Size: 388678 MD5sum: d42a29223c1de048ae96e7282a55ec7a SHA1: 40d1c78d3745d9078ee5b12b8f222aa30fb1b509 SHA256: bf80142c1d7682aa606fb4cdbb5b2a26875335d675367f5fd10415e0400f848c SHA512: 6c04522b49036aae43fa7ac5859681dd5e60e761a29d58e116332a95246d0e6e132d029994eabcce5d2f2a780761d95f5127ecfa67084dbe70a98482a0e4ea68 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: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 578 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 221756 MD5sum: 4ef47af2751e983b689ee22f6e6ac5bc SHA1: 91acae79efb2c000ef6672281c30f6b2af8c73ff SHA256: 1557da2575e62f784c6a0ffd34eca8f86e94d484676cb1579efe8b229e4d80c5 SHA512: 8e340c201feea99afd6558cd56fcecd0b0028a2f5cd58cc4d1db1d24ae62871b27a390a5f36371d1f4c5815a318c33c9791d71b61c980389ca6cefccd96bf696 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: arm64 Version: 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 (>= 4.2.1), 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_arm64.deb Size: 157658 MD5sum: 810240ff2880feaceaa98b43f08c977f SHA1: a20a167e4541db7768c32e17cdd96f75b7de91f6 SHA256: e77a267445468a9e2efe0b722dd04efd0a85cd126baf40961f0a3f28db1e1b32 SHA512: 1f8e7c65a7b696d061c0463195265ac728a4b0b404f2cce6195490ed8a9ac36e465eda108db48ad156358383c11c030ee969576f8a15b0a0c85914a557b09596 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: arm64 Version: 4.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 570 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_arm64.deb Size: 234654 MD5sum: 53746894d9445837e39aa46081fa73b5 SHA1: 4ae160bea4e0ca80f6cc72d62d9b65557bd615fe SHA256: 8246ab60fb63b48c4ce5a3232ecff3142db38c969c851270a304a5abb457792c SHA512: 26a6e5c96f1dd5a08843a625150bac366830f65e54047d802a1438a3d123b9bf816244652e91616dcbb4fe3a53e50a280ccb5fc2537b97522c71fcde704a1427 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: arm64 Version: 0.6.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3824 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_arm64.deb Size: 1210872 MD5sum: 22a99e5ad33fa0798db86a634b652664 SHA1: fe320cb9260877de7c5b6e0fb6dfc1e31e59576f SHA256: e8746048fd55f5da2590140059db6d22f781b69c4acfde9b3dee1dce670d3810 SHA512: 3e1c2869c70c9a4d3b4cc118439b0fc9ccbbea7809dd95c9e9711fbd2339175bce0740da064f68472131efa48bcf996873d343cc58100413b908945ac315da24 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 (). Package: r-cran-agop Architecture: arm64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-igraph Filename: pool/dists/resolute/main/r-cran-agop_0.2.4-1.ca2604.1_arm64.deb Size: 306492 MD5sum: 8974f7a6e9fec90314d5715120e882e6 SHA1: af677e0c14bd1c5283dde6519fb813b90cdb4378 SHA256: ccc974e93f1395e67e418175d418a30a5ea322cc6156be18a0226cca9d58b6c8 SHA512: f303d7d8d522d24c87af9e8931df26dcbf700c5016127634016e11696d866ae60a3c0deff34c306e229fdef26e96d84199a0428662c618247ca544d4c497c745 Homepage: https://cran.r-project.org/package=agop Description: CRAN Package 'agop' (Aggregation Operators and Preordered Sets) Tools supporting multi-criteria and group decision making, including variable number of criteria, by means of aggregation operators, spread measures, fuzzy logic connectives, fusion functions, and preordered sets. 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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: arm64 Version: 1.15.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 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_arm64.deb Size: 417668 MD5sum: 483791be4feaf42c56db80b55534170f SHA1: 1075c73ade273b77a36251fb2da639eb5c4551fd SHA256: 8db1191609c6e0f641fe00dca85d05821cc54a20931bceb286cdf5e92da2895e SHA512: 2af1cbaa5c9edd71485d8de55a1c8d5d7cb2b5ad1dca78213db1d0c00518cf734cd94096922740d6b364405fcd042c80beeecc2c85a2b2bd929defc1a6030d5c 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: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 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_arm64.deb Size: 71374 MD5sum: 9f935fd7fe45b18eaadea6ebae3f9d3d SHA1: d31567b6bd33225a6ed40459b7f3e03d7853c11b SHA256: 35c7fd79b65b80aba32bfbfa5064e1bca7b5d6c992b99868526c52fd5fb13187 SHA512: b33204c9f1b353d2a29559958bded8d22f71d4075f457f32c9059d964ce81f63b949f548ab6c1ef09c033c5e945a30548bd663b113c64b34b672f671c0971a09 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: arm64 Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3194 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_arm64.deb Size: 2534398 MD5sum: 2a05f29e854f0e302b06612065ab6b7a SHA1: 2312436da8e2f16267b46be4f6aa60a95b49bbcd SHA256: da1aa1249e64fad87f6bb12fff24beb112d4adedc6abac5f504b93e8d1306e49 SHA512: be135afa3fd88d556b439705c55e7537f29ba687000fdebe4aeb501c570d6e202a71c47a8e7423cdc85cf00a33a1434b901765bcecd255754af639a73d8c06bb Homepage: https://cran.r-project.org/package=aifeducation Description: CRAN Package 'aifeducation' (Artificial Intelligence for Education) In social and educational settings, the use of Artificial Intelligence (AI) is a challenging task. Relevant data is often only available in handwritten forms, or the use of data is restricted by privacy policies. This often leads to small data sets. Furthermore, in the educational and social sciences, data is often unbalanced in terms of frequencies. To support educators as well as educational and social researchers in using the potentials of AI for their work, this package provides a unified interface for neural nets in 'PyTorch' to deal with natural language problems. In addition, the package ships with a shiny app, providing a graphical user interface. This allows the usage of AI for people without skills in writing python/R scripts. The tools integrate existing mathematical and statistical methods for dealing with small data sets via pseudo-labeling (e.g. Cascante-Bonilla et al. (2020) ) and imbalanced data via the creation of synthetic cases (e.g. Islam et al. (2012) ). Performance evaluation of AI is connected to measures from content analysis which educational and social researchers are generally more familiar with (e.g. Berding & Pargmann (2022) , Gwet (2014) , Krippendorff (2019) ). Estimation of energy consumption and CO2 emissions during model training is done with the 'python' library 'codecarbon'. Finally, all objects created with this package allow to share trained AI models with other people. Package: r-cran-aihuman Architecture: arm64 Version: 1.0.1-1.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_arm64.deb Size: 1509772 MD5sum: b9057b0d62a255a5bca7dad97d28439f SHA1: b068dce343809ee871e216908e5d824712dffbca SHA256: 7cfabb42771e606d72bd9fcae0c48b33ca0a674bdb7e8b4e70e9d89331bd6918 SHA512: b2f6fed98c82def8497d35141ec7c5d5ee0d67dbe4a2b8ded9fb6b572a3edd094b7b9059ebf38886652ba3b1d068dc083d75f2a3f475b287223ac368fa6fe668 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: arm64 Version: 1.7.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3801 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_arm64.deb Size: 3183974 MD5sum: 0a8cb38ca530b924412619e8b9507649 SHA1: 7b75751ccf5ad5cec02188f0d854bb5fc3578a04 SHA256: 2f751e4b77d4a00e0e696262fd8816cb19f338fcc4008dfb87e1eb8fe933893a SHA512: 4fb550a9de9c87ab890ab4a74c669fa847729b01bd09062c9e520eb3f4ca18f58ea2f0ad3a9ea427a4d2e6d718de12868b558d0ea6e3f52f3ad8d9fb0cd1b031 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: arm64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1160 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-airthermo_1.2.2-1.ca2604.1_arm64.deb Size: 1084404 MD5sum: fe3173896df4d9c674df9aff81ba6b20 SHA1: c1a69cc354cf4806d1c0bbe74abb57d751a08b9f SHA256: f9be781017efdcfe966a262309681a381b8cb165c12e70034f8098e26f108c4a SHA512: 9858fadcebd7fe01db750bedf02074ce88b2afda234dbdcea338569115eb915138426d6ff78c7e99a1acf02eac85a1d7cf89a84b36f9e78f81495f33f3f01df2 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: arm64 Version: 0.6-3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 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_arm64.deb Size: 160784 MD5sum: 7f4480fc42291fc07030fbd148417a6f SHA1: e780d6c329f80ac451ca9fa50693ec4c5ace3aa9 SHA256: c14cdee73802c16d7198ec50f3a51a1710dec42c894ee6c7d9fb1975774cd32b SHA512: c79d8a7a443f548d528b2d64096560d730108360467ab8ce1f3ccd5ebefb6a1cdd20b19255829829e49dbba9d0671281880b4819188714690b3e8b868d311480 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: arm64 Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2338 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-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_arm64.deb Size: 1948334 MD5sum: 2868f4554eb98c61dbeb5d15c8d221a5 SHA1: f8df29dfcf9cad33340068ef7137a04fbfcf8576 SHA256: 6ff3049404ca9b604e3e4cc191b5f0f57038174cf51e4fe1124750cc2b103a9c SHA512: 1a2602e182a864f7a9ade2d200a88c17dd8f4270e16ff1c88fb453ca98d53a3ba2f3abc41bcc89fb4831bd4be793501351054ff43990448dbef3a2b56bad8aac 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: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 123636 MD5sum: 6e495d042197bce14dcb88b9d5a81d9a SHA1: db2746439229e8ccefb19cfb1c779a13b4d0541a SHA256: d72ab224bdbd88f463a3fdc8ab6f973cb31453d3d5d56d3166ecc92999e2f529 SHA512: 3288897786f1ba2e5fc141bcff648312675682a87b942daef7098e6a383bdf0c9e09b87d35063c26c524d5a22e3c44d93c3c3652a29dc0fdcb22c6ba3fa22d60 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: arm64 Version: 0.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3354 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_arm64.deb Size: 1368932 MD5sum: ab57285e4f369dfd0d3f81b6556fe5d1 SHA1: 03ac430ff2b192672acfa070dfb47215154f12e6 SHA256: e2ac2302fbaa47731136b174fc5352486f55c90ef0f64589e3449ba6afd6711e SHA512: 72bc932fa9e95856ed813f382500f0888efd69b1d7ca38956cd52cf8bc6b7d7794bffa2d41f55746ab378b605aca4b3603e3047241a1ec6021f27787c9d1e67b 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: arm64 Version: 4.2.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 867 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_arm64.deb Size: 422516 MD5sum: 36601f5874ed3093409b4dfbb11b5f00 SHA1: 0ab174e1c0e6f0ff7cb2bffe095d3dba480ee791 SHA256: 52fe0298cf0496bdd0438899aa39dc3af2be0611003e39f44925fbf1744f1baf SHA512: 846975b339b36c123b6d93a994981d7ac720ee83555a38dfe2a4956f3a042bd8ec1841dc5d5bac1915b6b757ccdb80417f06c3b6a3d518c84760f442ba6b40b4 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: arm64 Version: 1.2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 760 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 562980 MD5sum: 6712f2500d0706b53a54db07131105c6 SHA1: 918d4a357489e6d0dafab72f84e445412d32b898 SHA256: ce6bdd7133b62235d0e2b28988484eb45ac5b9713c8883960193e0e4f3e4acf8 SHA512: e5f95d3c2ffe75a9fc27f925e4e2c6ae06e3b2be0d27c5a524477273a58b7f0c699199add83c42ab84f230813cf4e530f5a41bb6c31479c586b698293774c41e Homepage: https://cran.r-project.org/package=AlgDesign Description: CRAN Package 'AlgDesign' (Algorithmic Experimental Design) Algorithmic experimental designs. Calculates exact and approximate theory experimental designs for D,A, and I criteria. Very large designs may be created. Experimental designs may be blocked or blocked designs created from a candidate list, using several criteria. The blocking can be done when whole and within plot factors interact. Package: r-cran-allelicseries Architecture: arm64 Version: 0.1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 852 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 400564 MD5sum: c4ee223f9ab6ce92510385fd828ffa47 SHA1: 7073abbf6e060bc4100782b11b9540613c42e447 SHA256: fc4764da4bc0e82be5dfe39c7895961885e5f0f9194f96f7dba26e4383d4a9bd SHA512: 1fa61e1dbd84ba616ee68ea0bce55b252d144205ef119e83082bb2fefb066e85f06049a868d21e574dd1e874787bc90671bd590faabe0c50c7dba6f98cda8e8b Homepage: https://cran.r-project.org/package=AllelicSeries Description: CRAN Package 'AllelicSeries' (Allelic Series Test) Implementation of gene-level rare variant association tests targeting allelic series: genes where increasingly deleterious mutations have increasingly large phenotypic effects. The COding-variant Allelic Series Test (COAST) operates on the benign missense variants (BMVs), deleterious missense variants (DMVs), and protein truncating variants (PTVs) within a gene. COAST uses a set of adjustable weights that tailor the test towards rejecting the null hypothesis for genes where the average magnitude of effect increases monotonically from BMVs to DMVs to PTVs. See McCaw ZR, O’Dushlaine C, Somineni H, Bereket M, Klein C, Karaletsos T, Casale FP, Koller D, Soare TW. (2023) "An allelic series rare variant association test for candidate gene discovery" . Package: r-cran-almanac Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 865 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 430732 MD5sum: 38cd45e1fe95873f79640e1b84a2c280 SHA1: 0e30d975846d352afc6e183b81e32d694bc90836 SHA256: 344b31e967e77a1bdb0af1c9d751678cf0f6b1b34df0526659b43a01a5310e79 SHA512: ab92e3c9160c21812470eb67cbe6485ad66139b0006748a687a0214a7c238783fd4524381608ab7ec21baadfb6470b47c2baf7b58ee52b47a8d67239e10d4a4d Homepage: https://cran.r-project.org/package=almanac Description: CRAN Package 'almanac' (Tools for Working with Recurrence Rules) Provides tools for defining recurrence rules and recurrence sets. Recurrence rules are a programmatic way to define a recurring event, like the first Monday of December. Multiple recurrence rules can be combined into larger recurrence sets. A full holiday and calendar interface is also provided that can generate holidays within a particular year, can detect if a date is a holiday, can respect holiday observance rules, and allows for custom holidays. Package: r-cran-alpaca Architecture: arm64 Version: 0.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 548 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 200630 MD5sum: dc5e051041c12ab6e7876419b9a3f624 SHA1: 2800555423f4be17c74e5f7fbfd7847b893a5926 SHA256: 6f01cd74b7eb5576d1a4efc6d23a12d5cd9e85da713d72b02f8ee8ecf8a5cc64 SHA512: 927b5cc97db8d46c41d39e919af554c0cf011a2ec238d9941bbb87d8430a67733109f4b4b2356ff96fdd36aa4bdfa951f2d2d5209c062e0e07c9694ef4281c38 Homepage: https://cran.r-project.org/package=alpaca Description: CRAN Package 'alpaca' (Fit GLM's with High-Dimensional k-Way Fixed Effects) Provides a routine to partial out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is based on the algorithm described in Stammann (2018) and is restricted to glm's that are based on maximum likelihood estimation and nonlinear. It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides analytical bias corrections for binary choice models derived by Fernandez-Val and Weidner (2016) and Hinz, Stammann, and Wanner (2020) . Package: r-cran-alphabetr Architecture: arm64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 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_arm64.deb Size: 172700 MD5sum: 55e05ae58da2c68905cdfda28ca2dbe9 SHA1: 9052779cf7038d7fa6902772a9b335ce01d10d6f SHA256: 9ebbc95e6936b09e19847e84e720acd8531cea06e608e8427c377d0860344683 SHA512: 1dc67566d6fea7b3b6fe0d9bb75959932f4d0fe09852b9e279f162c1f319e8450511393670513ae2c6d6fed8161cd457891afed5e96720648034dc267ca6a319 Homepage: https://cran.r-project.org/package=alphabetr Description: CRAN Package 'alphabetr' (Algorithms for High-Throughput Sequencing of Antigen-Specific TCells) Provides algorithms for frequency-based pairing of alpha-beta T cell receptors. Package: r-cran-alphapart Architecture: arm64 Version: 0.9.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2662 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2105872 MD5sum: f6b6009e6687ab62a0c5ef170d4c88f2 SHA1: 56f964e14841863986ad56d4883c4cb008c5e1c6 SHA256: 259e1e18f7e948e50821913b520328d0fb97e4449c61151414ada83d21eed1f2 SHA512: 42c094c038f29634743ccbe605d910d06570d9942786a26e56d82253c3bd13ddd2cdb505f4525c2d1b2c8c7412cab11f52982be148547dce04ad2da034d11a95 Homepage: https://cran.r-project.org/package=AlphaPart Description: CRAN Package 'AlphaPart' (Partition/Decomposition of Breeding Values by Paths ofInformation) A software that implements a method for partitioning genetic trends to quantify the sources of genetic gain in breeding programmes. The partitioning method is described in Garcia-Cortes et al. (2008) . The package includes the main function AlphaPart for partitioning breeding values and auxiliary functions for manipulating data and summarizing, visualizing, and saving results. Package: r-cran-alphashape3d Architecture: arm64 Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-geometry, r-cran-rgl, r-cran-rann Suggests: r-cran-alphahull Filename: pool/dists/resolute/main/r-cran-alphashape3d_1.3.3-1.ca2604.1_arm64.deb Size: 89540 MD5sum: b2e3aac06f50b0a3386a112dc8e3c472 SHA1: 3c48a241b1340b1446db65fde64ee2c41bacf6ae SHA256: 227e79b7fa8930f8972faaefdf3acdcef0f4505b88bffa2296a74e6899b44752 SHA512: f30207b0772253adc81c824c117961c639d787b7ac90d65fdc33c52c55a13fd4d56402b4d92144e502561ac36b668b55648e7103e5090ee8a7eece30b0a2e15f Homepage: https://cran.r-project.org/package=alphashape3d Description: CRAN Package 'alphashape3d' (Implementation of the 3D Alpha-Shape for the Reconstruction of3D Sets from a Point Cloud) Implementation in R of the alpha-shape of a finite set of points in the three-dimensional space. The alpha-shape generalizes the convex hull and allows to recover the shape of non-convex and even non-connected sets in 3D, given a random sample of points taken into it. Besides the computation of the alpha-shape, this package provides users with functions to compute the volume of the alpha-shape, identify the connected components and facilitate the three-dimensional graphical visualization of the estimated set. Package: r-cran-alphasimr Architecture: arm64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2620 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_arm64.deb Size: 1610228 MD5sum: a907020f613fa209334151d58a318c69 SHA1: a5f93b9152235bcf20b6b4ddbe407c8fe32c9b54 SHA256: cc87b75c3751b921d6488d357dbc02de3721d590de5bfe2b007bd6d59707ee9d SHA512: 78bc3a433dc9d9bd71671d07b7e77bb96b2a8a5eaf0c4de2059f311b8d695b412eaa70b21a1df152a36261789f2899ccaec67fd109e07b4c1fc076dfba594b10 Homepage: https://cran.r-project.org/package=AlphaSimR Description: CRAN Package 'AlphaSimR' (Breeding Program Simulations) The successor to the 'AlphaSim' software for breeding program simulation [Faux et al. (2016) ]. Used for stochastic simulations of breeding programs to the level of DNA sequence for every individual. Contained is a wide range of functions for modeling common tasks in a breeding program, such as selection and crossing. These functions allow for constructing simulations of highly complex plant and animal breeding programs via scripting in the R software environment. Such simulations can be used to evaluate overall breeding program performance and conduct research into breeding program design, such as implementation of genomic selection. Included is the 'Markovian Coalescent Simulator' ('MaCS') for fast simulation of biallelic sequences according to a population demographic history [Chen et al. (2009) ]. Package: r-cran-alqrfe Architecture: arm64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 93962 MD5sum: 42fb90cc1bc4e3a45296f6b61fdc7ae9 SHA1: 04a7196378ff1d07ec9838b276a238d0882ac0df SHA256: 0285f8fd5a6665a1d5318d5a9f96e44d4ed00e1c3d7d1bb9676f9adb78d6e205 SHA512: 0281e4ea1cdfab7c6c7932a8b63c584112f2aa1da8baea124c5f615c8288e17d13940ac552b82a1601de2fc8c3e232c95c353ecb6c4c8bb658dfd24ab261b9b5 Homepage: https://cran.r-project.org/package=alqrfe Description: CRAN Package 'alqrfe' (Adaptive Lasso Quantile Regression with Fixed Effects) Quantile regression with fixed effects solves longitudinal data, considering the individual intercepts as fixed effects. The parametric set of this type of problem used to be huge. Thus penalized methods such as Lasso are currently applied. Adaptive Lasso presents oracle proprieties, which include Gaussianity and correct model selection. Bayesian information criteria (BIC) estimates the optimal tuning parameter lambda. Plot tools are also available. Package: r-cran-alternativeroc Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 121016 MD5sum: 4c1b1aceabc47098868134806b81b9de SHA1: dea84fe69338a0377cdd6b98227e80d8ec671585 SHA256: 737246581020c142a1c617c3b62941769253ad27fb7c3319eb4095a88bfdef35 SHA512: e662858e57aa2252af85ba8d96552098f2d6f373213394b5e96426c1fd038e6e01907cf61b74b841a1cecef57dc4bf03153aa5d0899b34f19d3c22112723ef9c Homepage: https://cran.r-project.org/package=alternativeROC Description: CRAN Package 'alternativeROC' (Alternative and Fast ROC Analysis) Alternative and fast algorithms for the analysis of receiver operating characteristics curves (ROC curves) as described in Thomas et al. (2017) and Thomas et al. (2023) . Package: r-cran-alues Architecture: arm64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3251 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_arm64.deb Size: 1807222 MD5sum: bfdda1fe119e5b64af3bed4ddc27b932 SHA1: f4df0da9f5b16faca38526d038223f701d831bb6 SHA256: 6d40cbdee8a80fba33824cdcd129cd12758b040693e2b2a53648dc5711cf7262 SHA512: d3a1ea71c4fc16b4f69d0cf15a2256b83b22422ea1d17b28206cd7bed73f91322c8d66657b2b1f29659f535d6c0198d5c960a408d76cf70d6abbe8403b781b83 Homepage: https://cran.r-project.org/package=ALUES Description: CRAN Package 'ALUES' (Agricultural Land Use Evaluation System) Evaluates land suitability for different crops production. The package is based on the Food and Agriculture Organization (FAO) and the International Rice Research Institute (IRRI) methodology for land evaluation. Development of ALUES is inspired by similar tool for land evaluation, Land Use Suitability Evaluation Tool (LUSET). The package uses fuzzy logic approach to evaluate land suitability of a particular area based on inputs such as rainfall, temperature, topography, and soil properties. The membership functions used for fuzzy modeling are the following: Triangular, Trapezoidal and Gaussian. The methods for computing the overall suitability of a particular area are also included, and these are the Minimum, Maximum and Average. Finally, ALUES is a highly optimized library with core algorithms written in C++. Package: r-cran-amap Architecture: arm64 Version: 0.8-20-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 4.1.1), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-bioc-biobase Filename: pool/dists/resolute/main/r-cran-amap_0.8-20-1.ca2604.1_arm64.deb Size: 280276 MD5sum: f8c140009c6d6634b66d3db70e62671d SHA1: 540bb2d429ac093e15b90f16382bc2462e359d0d SHA256: 2149999d78ff30deacb031f03d32825454bd77dee67aec94589b5992c568f611 SHA512: 383c54ede71553f6d8790ef8802a97d8eccf8ec3e1bca8c96259f9b618f2df57c6872837d52daf2b2154b457576a9c193e49bddb4787206869ed3d3ebf33323d Homepage: https://cran.r-project.org/package=amap Description: CRAN Package 'amap' (Another Multidimensional Analysis Package) Tools for Clustering and Principal Component Analysis (With robust methods, and parallelized functions). Package: r-cran-ambient Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1025 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-cpp11 Suggests: r-cran-covr Filename: pool/dists/resolute/main/r-cran-ambient_1.0.3-1.ca2604.1_arm64.deb Size: 842396 MD5sum: 8b8cbe197ee35ddae91c9c622ec27c0c SHA1: 70a35df28ecbf68f312c54289422b0912928e4fd SHA256: 3c7d98e98ec0211c72bcf0de4e31add531d878165d75b12ce33b124830eabe2e SHA512: 3695eb5418a64b8884a4ebcad60e20bfba771858a55e2502bf4e7fa32a5cf7375823c88fa9e905d54272654364e9232169c238fdd041881a3b6d9a65fbff0e0a Homepage: https://cran.r-project.org/package=ambient Description: CRAN Package 'ambient' (A Generator of Multidimensional Noise) Generation of natural looking noise has many application within simulation, procedural generation, and art, to name a few. The 'ambient' package provides an interface to the 'FastNoise' C++ library and allows for efficient generation of perlin, simplex, worley, cubic, value, and white noise with optional perturbation in either 2, 3, or 4 (in case of simplex and white noise) dimensions. Package: r-cran-ambit Architecture: arm64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1915 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 534374 MD5sum: 7239cec273e5e098878ff0607a193330 SHA1: 046690c5f68a8e40cec28627bf66f6c78e7c9706 SHA256: ba679a7726a9660c5703ca78477ed17e65184680a09e8fe84a4cbf44eb88798a SHA512: 6c44bba1fcede39e8494430f38a8f333497937108f43e452d4bd4d6a6ec31e6e1b6f2eb48d9f8a7164edaa6edc55f17077c7de5c06850b010c0c2cbe541630c1 Homepage: https://cran.r-project.org/package=ambit Description: CRAN Package 'ambit' (Simulation and Estimation of Ambit Processes) Simulation and estimation tools for various types of ambit processes, including trawl processes and weighted trawl processes. Package: r-cran-amelia Architecture: arm64 Version: 1.8.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2196 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 1434236 MD5sum: d98015a95e12955c025ba01d10395f1e SHA1: 3af58c9170c2e27563db9d265c7158a2dd21debe SHA256: a7c988cf726e34ab7e3b8377a791551bc5d851f3a3b29400ce8fff33be9d1cc1 SHA512: 54dff066d4833c84ae0ef26c9202e3c58515e3f38226aee81900f0f117aedff58ca2f6c7ecf045a9d0822702a7c04f460914f14bdf18a498ba85d043c9b88074 Homepage: https://cran.r-project.org/package=Amelia Description: CRAN Package 'Amelia' (A Program for Missing Data) A tool that "multiply imputes" missing data in a single cross-section (such as a survey), from a time series (like variables collected for each year in a country), or from a time-series-cross-sectional data set (such as collected by years for each of several countries). Amelia II implements our bootstrapping-based algorithm that gives essentially the same answers as the standard IP or EMis approaches, is usually considerably faster than existing approaches and can handle many more variables. Unlike Amelia I and other statistically rigorous imputation software, it virtually never crashes (but please let us know if you find to the contrary!). The program also generalizes existing approaches by allowing for trends in time series across observations within a cross-sectional unit, as well as priors that allow experts to incorporate beliefs they have about the values of missing cells in their data. Amelia II also includes useful diagnostics of the fit of multiple imputation models. The program works from the R command line or via a graphical user interface that does not require users to know R. Package: r-cran-ameras Architecture: arm64 Version: 0.3.0-1.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_arm64.deb Size: 1300022 MD5sum: 9ec3176e5b98d973534ab6282f351110 SHA1: da373209deff4600d2fc59ad1390dc8f7a947841 SHA256: 949f88648d39bc39e3d5aa23f03ba862ecf0485e41cb3db0a20d23366825a7b2 SHA512: 06045c35bb6a20db43c00d20cb5d4b4f67467fe05c2567851a87fe5971a3749315f6b7a853b4077c18b4151ef02f730c4fc179a1d2e3a9eb4ac9244747d7d690 Homepage: https://cran.r-project.org/package=ameras Description: CRAN Package 'ameras' (Analyze Multiple Exposure Realizations in Association Studies) Analyze association studies with multiple realizations of a noisy or uncertain exposure. These can be obtained from e.g. a two-dimensional Monte Carlo dosimetry system (Simon et al 2015 ) to characterize exposure uncertainty. The implemented methods are regression calibration (Carroll et al. 2006 ), extended regression calibration (Little et al. 2023 ), Monte Carlo maximum likelihood (Stayner et al. 2007 ), frequentist model averaging (Kwon et al. 2023 ), and Bayesian model averaging (Kwon et al. 2016 ). Supported model families are Gaussian, binomial, multinomial, Poisson, proportional hazards, and conditional logistic. Package: r-cran-amisforinfectiousdiseases Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1038 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_arm64.deb Size: 555722 MD5sum: 2bb2339509fe4d3b029b090ba4079016 SHA1: 0b2211bf0f23e0bcd1597a685e969436837c64b3 SHA256: d275be9a4962693f8cfc0915be6c4284bb13722121b7d99344fb4ba221df9273 SHA512: 09634b03b18ba885e8ac45770dab6de3b1d92fcdf6fb5a2550f0abd113d7ee4e0d1ba39be1b269ff45aeaf5ba3a5f85dcbe0df55016611fe4a94e1f682364609 Homepage: https://cran.r-project.org/package=AMISforInfectiousDiseases Description: CRAN Package 'AMISforInfectiousDiseases' (Implement the AMIS Algorithm for Infectious Disease Models) Implements the Adaptive Multiple Importance Sampling (AMIS) algorithm, as described by Retkute et al. (2021, ), to estimate key epidemiological parameters by combining outputs from a geostatistical model of infectious diseases (such as prevalence, incidence, or relative risk) with a disease transmission model. Utilising the resulting posterior distributions, the package enables forward projections at the local level. Package: r-cran-ampir Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1894 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1726652 MD5sum: f29add2e5a4448cac359653824db1bc5 SHA1: b0ba2549e0cd3ce681610d188815845a938cc2d7 SHA256: 3f785edc870e2f793199d9bb108bc51eb76d95a73b9414c97837df957a9efbc2 SHA512: 5b89f226b038320fc74c240c24196644a5e0773254f0252b073ca8beafee263e0f4b98954b945ab5aa5033d697c17678dce2a319c89c883dfa342691204fd26c Homepage: https://cran.r-project.org/package=ampir Description: CRAN Package 'ampir' (Predict Antimicrobial Peptides) A toolkit to predict antimicrobial peptides from protein sequences on a genome-wide scale. It incorporates two support vector machine models ("precursor" and "mature") trained on publicly available antimicrobial peptide data using calculated physico-chemical and compositional sequence properties described in Meher et al. (2017) . In order to support genome-wide analyses, these models are designed to accept any type of protein as input and calculation of compositional properties has been optimised for high-throughput use. For best results it is important to select the model that accurately represents your sequence type: for full length proteins, it is recommended to use the default "precursor" model. The alternative, "mature", model is best suited for mature peptide sequences that represent the final antimicrobial peptide sequence after post-translational processing. For details see Fingerhut et al. (2020) . The 'ampir' package is also available via a Shiny based GUI at . Package: r-cran-amssim Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 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_arm64.deb Size: 76026 MD5sum: a303cd469c8f6b3866854a33febf6908 SHA1: f9896bad71bbebac533d31d946b893552e7f5b78 SHA256: 7b733df15dd61b392156e3a109e0d22bea4a7e70491c30b675c7383ce335db93 SHA512: 4910beaf97f26518e721efd4231d2ca7354eabf0b075baa2464d94c58e2d9916c475ce2e437cbcc2b82b1b5607860068e2edbe5da80b2d7315bb2f50e56eaf87 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: arm64 Version: 0.18.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1751 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_arm64.deb Size: 1512176 MD5sum: f83ccc0549cbed4946c0963590cc055c SHA1: 8df489751ccc80661ae59853a7820221c95d2c8e SHA256: 33103e0ab7bb6387b5f3ffde5c0e88df72d83e6d78ab46eedefd3f04fc49f39d SHA512: c63a30c831516fa590da567c0ca00e177f9d469738a06c0e43d421a2db6d552c8a734d25b101120c46802a01c507d1e74554b42ad1f15c0519d9410dd1d347f5 Homepage: https://cran.r-project.org/package=analogue Description: CRAN Package 'analogue' (Analogue and Weighted Averaging Methods for Palaeoecology) Fits Modern Analogue Technique and Weighted Averaging transfer function models for prediction of environmental data from species data, and related methods used in palaeoecology. Package: r-cran-analyzefmri Architecture: arm64 Version: 1.1-25-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 983 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-r.matlab, r-cran-fastica Suggests: r-cran-tkrplot Filename: pool/dists/resolute/main/r-cran-analyzefmri_1.1-25-1.ca2604.1_arm64.deb Size: 599442 MD5sum: a5bfe69bba170546eb85fbc5df015953 SHA1: 5cd002bce3e4b8481092eae7815ca8589abebe2b SHA256: 7ba2ab55dfa876f0f4e1cd1b3bd32af5c285aad1d3e76407e44b9c6acd335ac7 SHA512: 8b8e475aa9853e1798ceefb7a63d0d2dde11d92d0cb8539d3c21cf3fb5a1078a06bd5f2a1a0b704b49122ee2dc917c2cdd7eebdaaa98164d54925806c1094400 Homepage: https://cran.r-project.org/package=AnalyzeFMRI Description: CRAN Package 'AnalyzeFMRI' (Functions for Analysis of fMRI Datasets Stored in the ANALYZE or'NIFTI' Format) Functions for I/O, visualisation and analysis of functional Magnetic Resonance Imaging (fMRI) datasets stored in the ANALYZE or 'NIFTI' format. Note that the latest version of 'XQuartz' seems to be necessary under MacOS. Package: r-cran-animalsequences Architecture: arm64 Version: 0.2.0-1.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_arm64.deb Size: 142192 MD5sum: f58e7d7875f26b56be6b40b40a61c851 SHA1: fac11289a2e15ddee7a79aaaa8cd263dba9ee55b SHA256: a8957f741b85719eaf8e2e628bde7a3ff422879ed5c26c5addce168f0ba539dd SHA512: b9da6b4f0a752b90e8865eb7e891fe4f12afb96eab7b596effde2c3af7fe29457ec58be37c832e7d48e2871f00b2bc934fd95377dd538a2e2cac0292cc29142e Homepage: https://cran.r-project.org/package=AnimalSequences Description: CRAN Package 'AnimalSequences' (Analyse Animal Sequential Behaviour and Communication) All animal behaviour occurs sequentially. The package has a number of functions to format sequence data from different sources, to analyse sequential behaviour and communication in animals. It also has functions to plot the data and to calculate the entropy of sequences. Package: r-cran-anisna Architecture: arm64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1867 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1748126 MD5sum: d1e2a0f735e7bf610151d71d010e27f6 SHA1: 222af863c78839d0b004bc251d9cd1ebf6c85797 SHA256: e3104723240e07ef17c3dc56e79219185e8015995da00f433970b6a64b1d15f1 SHA512: a2b6bf4a641d66996611e20f78ed887f4b43ffb874742c55521939d1226510430746679ad3a5ff8222bf4aec643b6817e05e799a709e30f289250b6b0eaefc62 Homepage: https://cran.r-project.org/package=aniSNA Description: CRAN Package 'aniSNA' (Statistical Network Analysis of Animal Social Networks) Obtain network structures from animal GPS telemetry observations and statistically analyse them to assess their adequacy for social network analysis. Methods include pre-network data permutations, bootstrapping techniques to obtain confidence intervals for global and node-level network metrics, and correlation and regression analysis of the local network metrics. Package: r-cran-anmc Architecture: arm64 Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 150686 MD5sum: f138aa81bf78b8d84d0eae1852b10e0a SHA1: 8683f41d84c93f54c30f238c4ef0b38ea99cdcbd SHA256: c373cd13c8c81d84c22be03fc6d7f1f634ff3d9d484a6ce5f64fe593069524af SHA512: a791e28e94f27bea5895aa025ed31e9ae783483d5494b14a449b5b2f4b3a8f04da59f3bff2666fea4e01a9ba367940489bfd2e319d9e8bae70fd646e33bd10fb Homepage: https://cran.r-project.org/package=anMC Description: CRAN Package 'anMC' (Compute High Dimensional Orthant Probabilities) Computationally efficient method to estimate orthant probabilities of high-dimensional Gaussian vectors. Further implements a function to compute conservative estimates of excursion sets under Gaussian random field priors. Package: r-cran-ann2 Architecture: arm64 Version: 2.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3062 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 670470 MD5sum: 8f3fdb9a7b0f754493928cb7c6cff1a8 SHA1: d81651d60d8dcf0a7b733d6c15550a429ddf7823 SHA256: 0806ed757496a8fb3a6f9ce8d713281500cec7fbe1871b5f3204960848a4972f SHA512: b3a3b143b6e0b139838af5306b3c6b918c89e33816a66ce44967f45b85f4d04a44861384d763b2ca47e2c4d03353ed0f1ccb11aaa7d2da9f496dd12d4d635fbe Homepage: https://cran.r-project.org/package=ANN2 Description: CRAN Package 'ANN2' (Artificial Neural Networks for Anomaly Detection) Training of neural networks for classification and regression tasks using mini-batch gradient descent. Special features include a function for training autoencoders, which can be used to detect anomalies, and some related plotting functions. Multiple activation functions are supported, including tanh, relu, step and ramp. For the use of the step and ramp activation functions in detecting anomalies using autoencoders, see Hawkins et al. (2002) . Furthermore, several loss functions are supported, including robust ones such as Huber and pseudo-Huber loss, as well as L1 and L2 regularization. The possible options for optimization algorithms are RMSprop, Adam and SGD with momentum. The package contains a vectorized C++ implementation that facilitates fast training through mini-batch learning. Package: r-cran-anomaly Architecture: arm64 Version: 4.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1558 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 1293512 MD5sum: d56635aa0f9dc3cd84ac7c9cb6de6674 SHA1: 77404058674db47df84fb09199c63f656db24b4f SHA256: e59af8b56704ad108b545bce66e30b8afbdc084b7efa16ba6af5feb6319f5bd7 SHA512: 9c0872274320d73c305d8d7aa4b9f43be4aea4152874472600be24ab70b881331515ee9aa6708e0f935dcf2d6b34659872047d064a1c747d94d601973701642b Homepage: https://cran.r-project.org/package=anomaly Description: CRAN Package 'anomaly' (Detecting Anomalies in Data) Implements Collective And Point Anomaly (CAPA) Fisch, Eckley, and Fearnhead (2022) , Multi-Variate Collective And Point Anomaly (MVCAPA) Fisch, Eckley, and Fearnhead (2021) , Proportion Adaptive Segment Selection (PASS) Jeng, Cai, and Li (2012) , and Bayesian Abnormal Region Detector (BARD) Bardwell and Fearnhead (2015) . These methods are for the detection of anomalies in time series data. Further information regarding the use of this package along with detailed examples can be found in Fisch, Grose, Eckley, Fearnhead, and Bardwell (2024) . Package: r-cran-anominate Architecture: arm64 Version: 0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2921 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), 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_arm64.deb Size: 2885148 MD5sum: 1dcb3b19b7006abe75ad857dca83fbf4 SHA1: 7eca715ff6963351af72c135b5ba1b4c74845875 SHA256: 441582696b2b5c1cd85f1f6ce58067ff3d63474e1e2ade4805c65e3f36b7e5ae SHA512: 86fa2bd6b9a020dc75b3a25c936b15141a9203dd8a7b272a95ec8fc3a69e00b4aa5342ee186d98742ec49c19432f81df16aba5b8b4aa0e7bb47e6b0b50e43f15 Homepage: https://cran.r-project.org/package=anominate Description: CRAN Package 'anominate' (Alpha-NOMINATE Ideal Point Estimator) Provides functions to estimate and interpret the alpha-NOMINATE ideal point model developed in Carroll et al. (2013, ). alpha-NOMINATE extends traditional spatial voting frameworks by allowing for a mixture of Gaussian and quadratic utility functions, providing flexibility in modeling political actors' preferences. The package uses Markov Chain Monte Carlo (MCMC) methods for parameter estimation, supporting robust inference about individuals' ideological positions and the shape of their utility functions. It also contains functions to simulate data from the model and to calculate the probability of a vote passing given the ideal points of the legislators/voters and the estimated location of the choice alternatives. Package: r-cran-anthropometry Architecture: arm64 Version: 1.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1951 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-shapes, r-cran-rgl, r-cran-archetypes, r-cran-nnls, r-cran-ddalpha, r-cran-fnn, r-cran-icge, r-cran-cluster Suggests: r-cran-knitr, r-cran-calibrate, r-cran-mvtnorm, r-cran-rcolorbrewer, r-cran-plotrix, r-cran-abind Filename: pool/dists/resolute/main/r-cran-anthropometry_1.21-1.ca2604.1_arm64.deb Size: 1756070 MD5sum: 07ef84b7a188733948a5476e3488e610 SHA1: 2fed000e9e045bfb9da8339f39e27eaf7e227c97 SHA256: 05c498717ae94b40e256795b565d50c151e4680ec72d6b341127013c0541dce9 SHA512: a3fc41155e7558fa35b914fe4f6e2d4ffbd917acb9bf870b51fedf1344c5876fd6bbe2f157e6879dddc3d0801f07e5fc00c54c0c72df29fe94287357a66dcd85 Homepage: https://cran.r-project.org/package=Anthropometry Description: CRAN Package 'Anthropometry' (Statistical Methods for Anthropometric Data) Statistical methodologies especially developed to analyze anthropometric data. These methods are aimed at providing effective solutions to some commons problems related to Ergonomics and Anthropometry. They are based on clustering, the statistical concept of data depth, statistical shape analysis and archetypal analysis. Please see Vinue (2017) . Package: r-cran-anticlust Architecture: arm64 Version: 0.8.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1285 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rann, r-cran-lpsolve Suggests: r-cran-knitr, r-cran-mass, r-cran-rglpk, r-cran-rmarkdown, r-cran-rsymphony, r-cran-tinytest, r-cran-tableone, r-cran-palmerpenguins Filename: pool/dists/resolute/main/r-cran-anticlust_0.8.14-1.ca2604.1_arm64.deb Size: 754822 MD5sum: aca48d9e733d0d98bfcd1e88078029c7 SHA1: 05bc3cfa02dbddd61e0806b934a48280ec4d7b76 SHA256: 052cda55b30a914e64e716541947876457a57b980ef39cfd321dcb12889251c4 SHA512: c209ddd4ccac139fd83e810689ec75532a42f7e5395d6b56503015d934137b3cd3320a49083528115017e4a1c2d138525f97044e62b659c56a19f93ae5a5b46d Homepage: https://cran.r-project.org/package=anticlust Description: CRAN Package 'anticlust' (Subset Partitioning via Anticlustering) The method of anticlustering partitions a pool of elements into groups (i.e., anticlusters) with the goal of maximizing between-group similarity or within-group heterogeneity. The anticlustering approach thereby reverses the logic of cluster analysis that strives for high within-group homogeneity and clear separation between groups. Computationally, anticlustering is accomplished by maximizing instead of minimizing a clustering objective function, such as the intra-cluster variance (used in k-means clustering) or the sum of pairwise distances within clusters. The main function anticlustering() gives access to optimal and heuristic anticlustering methods described in Papenberg and Klau (2021; ), Brusco et al. (2020; ), Papenberg (2024; ), Papenberg, Wang, et al. (2025; ), Papenberg, Breuer, et al. (2025; ), and Yang et al. (2022; ). The optimal algorithms require that an integer linear programming solver is installed. This package will install 'lpSolve' () as a default solver, but it is also possible to use the package 'Rglpk' (), which requires the GNU linear programming kit (), the package 'Rsymphony' (), which requires the SYMPHONY ILP solver (), or the commercial solver Gurobi, which provides its own R package that is not available via CRAN (). 'Rglpk', 'Rsymphony', 'gurobi' and their system dependencies have to be manually installed by the user because they are only suggested dependencies. Full access to the bicriterion anticlustering method proposed by Brusco et al. (2020) is given via the function bicriterion_anticlustering(), while kplus_anticlustering() implements the full functionality of the k-plus anticlustering approach proposed by Papenberg (2024). Some other functions are available to solve classical clustering problems. The function balanced_clustering() applies a cluster analysis under size constraints, i.e., creates equal-sized clusters. The function matching() can be used for (unrestricted, bipartite, or K-partite) matching. The function wce() can be used optimally solve the (weighted) cluster editing problem, also known as correlation clustering, clique partitioning problem or transitivity clustering. Package: r-cran-antiword Architecture: arm64 Version: 1.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sys Filename: pool/dists/resolute/main/r-cran-antiword_1.3.5-1.ca2604.1_arm64.deb Size: 129086 MD5sum: 893644f020b90b9934d1500337d249b9 SHA1: cb4b8bf116d5076ec6d604bd8791145fb835438e SHA256: 800510ff85ae23e2fa0ad628a45e76299074a73fb02f3a57511cde67e6ebc969 SHA512: b449a7e71f6fc2559a0b64b9b836e3dc953a905ab6172d27c56457cc2118dee32a050a1ca1fd96d3afddb5df204b4f84eb881ca2c57f59134f3476b4f2e026f1 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: arm64 Version: 0.3.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 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_arm64.deb Size: 249722 MD5sum: e93436b7c1d3a5f5ebd64bacc37888e7 SHA1: 5f39a207a4eeadb5d5f50f9a8a20b9486c007fc3 SHA256: ee33fe651e74b86ae2f3e5f7687f370620ce6106860e817d608c93b819485c7e SHA512: 528077535709afd64957c26326f59b7cc3cca1249261747e9664030cc82fc6277c28c697f45fb68417ad746a1ddc4f8a49661c3f8da752093255b89703b840f1 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: arm64 Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2121 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_arm64.deb Size: 1188470 MD5sum: 3c6eff8479bf6c534cf00ce06986bae1 SHA1: e61249fdd0f643a9dff4cebd0c215a030d7edf33 SHA256: 58e26ca63f7012cdb3b73b03ac3188cbc0ae7e388eed268b33fa3a50c62b4e85 SHA512: ce13b8f1bf3764d56712c2e0c861943351acf7f2e9ec2b32c71c34d50ea062c33db9ffe65347db6201c3b493c19463ddea9a8658e6fd55ce4474e300e33160e9 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: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1391 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_arm64.deb Size: 530650 MD5sum: 71dd98f3020597e0d4a12e8e9c8993ac SHA1: 7bd145228b1deb96bebcc16d70bf40f283ef2c3b SHA256: 4c862b6c907fd8da7339407bc818e3a4cdb3bbd210e6ef43a7942299ce0dcf4d SHA512: 8cae493ee3f89664e9b512daaf7f70370596d5abeb2889519d170a8de5328dac0f4b5e021609a8ba8b90d7449c15739cc13ae7e44833f4ad276b83912bc3b693 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: arm64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2442 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_arm64.deb Size: 2002388 MD5sum: 3d11e9fe836500c7c31df68d65019f72 SHA1: 36a947c1bdfa0ac2574e7f6efd02732c6b5acc4e SHA256: 92d13ef679f9ec7c2751ebf53dc198dbbb2e1b7fb4ddc613be8fcc4806049f3a SHA512: bcd4e8414fee84ad30469686be7a1ee51e39ca8b12f7a6fcdc21afb3995f2bf3552e703b7db30b813858437375eac1f066c03ea20a0892c999dd5ba7b739a7cd 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: arm64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 685 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 431018 MD5sum: 0a9569b567ae9ab4ee0d0e96b663faf6 SHA1: d4db74298c1fe9e620bb230da1aea0ad7f015cf8 SHA256: f53c2fc13d7a7ea58be6a270650a97d699ad81350748df6a0a54d19ce4ec34b3 SHA512: 22f3f4593ae8b4fe197594a6935d60d00634c90d672f40a571e36349172bb9a209ae7585a478d628c66d0282d007c56b85fad2d1e92becbb86cb4199bf5361ff 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: arm64 Version: 1.4.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2132 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1471858 MD5sum: 49c86b14957269df7bd9abbc32ea5f79 SHA1: 531b1bf679220f4e7717feb96f35506f8b17ff76 SHA256: db38891f3f514ea8a2148fb35f9f3f6390358bcf9430161f3dff7b81640a023c SHA512: 61b1ba5c43ff9171b352e0881455a36661e983cc48850eca1221400d17fa77ef7acac7a19e2160b9817270018cea371dd631dc8ee5636857fcac03c2ed4526be 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: arm64 Version: 5.8-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3333 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_arm64.deb Size: 2896732 MD5sum: 9b05c592f3547cfe0affa42a8e362dd1 SHA1: 5b945b89b11811ba154a9b51c9e8608f454d0034 SHA256: 6c38ea0647ddc7778fe9efe57db7325de4f7c23ee5f936b061ab11459beab369 SHA512: 31469f0d3cdfde680b374f49259c4491118121a67b6ec25dad144f438702dc3dd66abf3dfbd17cc6e1566cde3e990dd1412076a05d4194b3867ce8ef0d5b735b 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: arm64 Version: 1.3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1011 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_arm64.deb Size: 659084 MD5sum: ef655f516d4bbe3d12560921e73741d3 SHA1: 7392a2de25726deed244d55fb908678327c2cebb SHA256: 81fa297657380a0904878c4a8d5350922f52b862e5286569d823ed054f92fd76 SHA512: cef5be17d8f80b55feb21d2c9daf6e7a6aebe5327446c7c65bf4316e0542657bd82eb7c20122055998f071c7d07410af68ce5d0b3032307d710e49c9f4963967 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: arm64 Version: 0.3-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2118 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_arm64.deb Size: 1191288 MD5sum: 6647722f02448eee4281132bb969e65f SHA1: 1037e5ae8a3c3f556de0f7a0d40052c9e085d419 SHA256: d80ac23a21bb63d81aeb0b5b91b7ec5949c0b641c81e8111b5ea0dde8e0ff823 SHA512: a6b26cdd5b5188ed3a5fcf84743111fe07b8085953dd5d1543b464cda0402824d4f0a67310deaebc653a65e3e88edb0317734ec8f05eadcbbec822fa4a879a21 Homepage: https://cran.r-project.org/package=aphylo Description: CRAN Package 'aphylo' (Statistical Inference and Prediction of Annotations inPhylogenetic Trees) Implements a parsimonious evolutionary model to analyze and predict gene-functional annotations in phylogenetic trees as described in Vega Yon et al. (2021) . Focusing on computational efficiency, 'aphylo' makes it possible to estimate pooled phylogenetic models, including thousands (hundreds) of annotations (trees) in the same run. The package also provides the tools for visualization of annotated phylogenies, calculation of posterior probabilities (prediction) and goodness-of-fit assessment featured in Vega Yon et al. (2021). Package: r-cran-apis Architecture: arm64 Version: 2.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 658 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 557664 MD5sum: f712e55ad400999f212cf6d824fa5994 SHA1: f4f92be3fb3a6b54461df4babfd3a7af029a8fec SHA256: 09387258de11111986ee3549bba6bfe70da34f1d6500a3e0c49a6296ecba951b SHA512: 64e7ba7c0cc07c474e7aa20e8278dc0bfa439ffd93086bda708b7206e12df9ce06aaf69f52c9f36bd3e26d338373c1475bd6137bb40cc7259693cdd6f2abe579 Homepage: https://cran.r-project.org/package=APIS Description: CRAN Package 'APIS' (Auto-Adaptive Parentage Inference Software Tolerant to MissingParents) Parentage assignment package. Parentage assignment is performed based on observed average Mendelian transmission probability distributions or Exclusion. The main functions of this package are the function APIS_2n(), APIS_3n() and launch_APIShiny(), which perform parentage assignment. Package: r-cran-apoderoides Architecture: arm64 Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 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_arm64.deb Size: 134216 MD5sum: 1703a31725cc55a360d3e145b0c4c1d7 SHA1: 470dbaf0d3416b41846b627376c89d9540da9eed SHA256: 24763c20f27ceb0bc082a7eecf3f24d129e5d6d909e0a47f1dafdd4f74ecc5f4 SHA512: 65f0b6c386dcf94bc0d2f7b8d0fda8411321df566bae3bd17e7c98e6512ce0a6c86f1ddee6f14ebea184fb30800967ac8af29fd157860dcc9bbee2c48a8a42dc Homepage: https://cran.r-project.org/package=Apoderoides Description: CRAN Package 'Apoderoides' (Prioritize and Delete Erroneous Taxa in a Large PhylogeneticTree) Finds, prioritizes and deletes erroneous taxa in a phylogenetic tree. This package calculates scores for taxa in a tree. Higher score means the taxon is more erroneous. If the score is zero for a taxon, the taxon is not erroneous. This package also can remove all erroneous taxa automatically by iterating score calculation and pruning taxa with the highest score. Package: r-cran-apollo Architecture: arm64 Version: 0.3.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2366 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_arm64.deb Size: 2043976 MD5sum: b65eba6e9910cf2c7ff301379d9ad676 SHA1: 11af45c89ee552c931af240c046667490e15a3db SHA256: b1c5bdc7939bdd54264e3e6c48b1d57951f187127f7091fb89f7609b7586f391 SHA512: f3920598615a694cac9bc12ad86287c101230d123d8c91985e191c07b819cc821a7840127ff4932415e2027ce5f73fab572275847b6c7ca194b64cc6af4bffbd 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: arm64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 696 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_arm64.deb Size: 262192 MD5sum: 333d3ee852906cde74d6c8a8295a6aa2 SHA1: 87dbeeacecaf6d585594fe83c4debc28b235c2d8 SHA256: 88b47c4b3a344720e9526abbde4859f48c0014b31a43fea6092dfc36915d83ec SHA512: b13fe648402deab5ba48bb5830abd684a24aefa899e01a6ce0f8ca5c36663882723ddc452e284fbce4befe9569195bde27abda6121077ceb46b9ccec29c36f1a Homepage: https://cran.r-project.org/package=approxOT Description: CRAN Package 'approxOT' (Approximate and Exact Optimal Transport Methods) R and C++ functions to perform exact and approximate optimal transport. 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Package: r-cran-arcensreg Architecture: arm64 Version: 3.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 474 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 308342 MD5sum: 2a4477aec5398581d73a54b0e248b77f SHA1: 3cacdbb28141ec34ef34ae48f00531425d0552a3 SHA256: 173913ccca663cce02a329e0fce21034b2bf37f4b70c9e7e98fc9caaa82db392 SHA512: c260902bb0ad1a2bf4193716d6c4beedcbc3293373cff9b8aba5704aa40fed576be76c63fe78d6aeea1c2f7d4f2de8f0769af0ab58a033e9fe45e3ce91ddbf6e Homepage: https://cran.r-project.org/package=ARCensReg Description: CRAN Package 'ARCensReg' (Fitting Univariate Censored Linear Regression Model withAutoregressive Errors) It fits a univariate left, right, or interval censored linear regression model with autoregressive errors, considering the normal or the Student-t distribution for the innovations. It provides estimates and standard errors of the parameters, predicts future observations, and supports missing values on the dependent variable. References used for this package: Schumacher, F. L., Lachos, V. H., & Dey, D. K. (2017). Censored regression models with autoregressive errors: A likelihood-based perspective. Canadian Journal of Statistics, 45(4), 375-392 . Schumacher, F. L., Lachos, V. H., Vilca-Labra, F. E., & Castro, L. M. (2018). Influence diagnostics for censored regression models with autoregressive errors. Australian & New Zealand Journal of Statistics, 60(2), 209-229 . Valeriano, K. A., Schumacher, F. L., Galarza, C. E., & Matos, L. A. (2024). Censored autoregressive regression models with Student‐t innovations. Canadian Journal of Statistics, 52(3), 804-828 . Package: r-cran-arcgisgeocode Architecture: arm64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1154 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_arm64.deb Size: 486674 MD5sum: b9cb76ee9960fcbfe3de93c91aa74e8a SHA1: e49ef3eb49b3fb56df42b51c8f7819801ca1bafe SHA256: ed89737098b9e45f5a43f0758a1b17989ee5d56037224bf5549d678d5cb3890f SHA512: 65433e214d18fa8a7776fcb77de5251dad81def08358ec807e3e0af1a97dd10e5235c27b0861cc407ce860ac3cd97f9937a566dd36e196abc2c618990ebd958e Homepage: https://cran.r-project.org/package=arcgisgeocode Description: CRAN Package 'arcgisgeocode' (A Robust Interface to ArcGIS 'Geocoding Services') A very fast and robust interface to ArcGIS 'Geocoding Services'. 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Package: r-cran-arcgisplaces Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2121 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_arm64.deb Size: 885662 MD5sum: 09da714a731fa8f8992f0199fd43bfb9 SHA1: a17535fcd9021e6d7bf09ff4f3d387f1b09dfbb1 SHA256: 97e5b3533b86c87c85c2b9d4f42ed8ee0003d90819593fd1ad407d8daac8e6eb SHA512: b17b49412cfea3a14233b8f66f6bd8a1983fa9c609c07189b5f8194809554f1ce9dda06d299d3dc2a47e6ee658157b79c294123af04f8207aa0aa18a840205dd 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: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 699 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 360794 MD5sum: c3886d575d8ce51e1b3fe813f9a7b07e SHA1: 9175f82a62b1ce7d90b528b31e9d73039a695b36 SHA256: 23e4f0673f1e800f21a03d5d96402270fe726cb4fae99db25b2ee347b61e2793 SHA512: 780788faef54526d9dda47bcab817b888a0e1d3f22ad91dc89fb34940e24f491bec72a6487810d84b66dccedcd7bce72a234f5becd8dfa75b363c6e4c0f356bb Homepage: https://cran.r-project.org/package=ARCokrig Description: CRAN Package 'ARCokrig' (Autoregressive Cokriging Models for Multifidelity Codes) For emulating multifidelity computer models. The major methods include univariate autoregressive cokriging and multivariate autoregressive cokriging. The autoregressive cokriging methods are implemented for both hierarchically nested design and non-nested design. For hierarchically nested design, the model parameters are estimated via standard optimization algorithms; For non-nested design, the model parameters are estimated via Monte Carlo expectation-maximization (MCEM) algorithms. In both cases, the priors are chosen such that the posterior distributions are proper. Notice that the uniform priors on range parameters in the correlation function lead to improper posteriors. This should be avoided when Bayesian analysis is adopted. The development of objective priors for autoregressive cokriging models can be found in Pulong Ma (2020) . The development of the multivariate autoregressive cokriging models with possibly non-nested design can be found in Pulong Ma, Georgios Karagiannis, Bledar A Konomi, Taylor G Asher, Gabriel R Toro, and Andrew T Cox (2022) . Package: r-cran-arcopt Architecture: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: libc6 (>= 2.17), 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-covr, r-cran-marqlevalg, r-cran-trust Filename: pool/dists/resolute/main/r-cran-arcopt_0.3.0-1.ca2604.1_arm64.deb Size: 209372 MD5sum: 5800e4d39eabceeb7f71e6c12bde4dd4 SHA1: b09273876c05036b5dea573a9319b78c52144c59 SHA256: 053a6e9c20089b35a86e4128171624b83abf0c6dfd562de619ebd6b1a09db63f SHA512: 4fe4b901e8830e5024c283430a61f5db2838d286ac1f3368033e94ec4aa7610cecfa43edd9bfc260ccc01facfe9303d4fe43009450c2edaae7fd8012437ff40f Homepage: https://cran.r-project.org/package=arcopt Description: CRAN Package 'arcopt' (Adaptive Regularization using Cubics for Optimization) Implements cubic regularization methods (ARC) for local optimization problems common in statistics and applied research. 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Package: r-cran-arcpbf Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2088 Depends: libc6 (>= 2.39), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-arcgisutils, r-cran-rlang Suggests: r-cran-httr2, r-cran-sf, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-arcpbf_0.2.0-1.ca2604.1_arm64.deb Size: 703282 MD5sum: 3b8b8acaad8b9e4db9b2971585daa885 SHA1: 7f2c60fba709906bff7d7939e85d3323873bfbe2 SHA256: 05d0e421b2091f0b2b6f08a44888d4ba9eccb44e9a8cf4438735bc433065a07c SHA512: c832551e2a27e92c33b8c92915ac5391b2b9cd6006844b83a4cbaa62e2301abe0d5898374740ab858c12c786117e2aed756947bebcc035c4dd801cf6b0058ba2 Homepage: https://cran.r-project.org/package=arcpbf Description: CRAN Package 'arcpbf' (Process ArcGIS Protocol Buffer FeatureCollections) Fast processing of ArcGIS FeatureCollection protocol buffers in R. It is designed to work seamlessly with 'httr2' and integrates with 'sf'. Package: r-cran-area Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-area_0.2.0-1.ca2604.1_arm64.deb Size: 123472 MD5sum: e8b7c6824259db7d54072268823f4803 SHA1: 0334bb77a737e86e7f3273132d19f1d198aacc9e SHA256: 64520bb8e0755b546cddd2bac0feb3c74e34c421c8542f00f94de6f674b06f10 SHA512: 10e4d4bcf1c42987f6b93e1064da288759d49120cf4eae0bfec8278ad554cf57fc4df1e5f623c0022ebc410e827079a4dd5fe7ff5b34bff12efa001929ac04f2 Homepage: https://cran.r-project.org/package=area Description: CRAN Package 'area' (Calculate Area of Triangles and Polygons) Calculate the area of triangles and polygons using the shoelace formula. Area may be signed, taking into account path orientation, or unsigned, ignoring path orientation. The shoelace formula is described at . Package: r-cran-arfima Architecture: arm64 Version: 1.8-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 489 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-ltsa Filename: pool/dists/resolute/main/r-cran-arfima_1.8-2-1.ca2604.1_arm64.deb Size: 395092 MD5sum: 44ce6f79638601e478f70e04b2ef380e SHA1: 4d48b515175a4bc05433aa09a7c45dad2ab6817b SHA256: 66c6e85b8d182d019d2b11d2190214f62d42ee3d7c7a1609a4269b40d35b5efe SHA512: 7ab4f80c9e2cbc47948978d410b9d4354ad97775683560164dad66aed188f4b13e52d5e4cf248092b06408eee1c9a046491164c0b04c8b6c77b15bedca068bd4 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: arm64 Version: 1.1.0-1.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 (>= 4.6.0), 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_arm64.deb Size: 95768 MD5sum: 57157275126bc15e49573ab79868f180 SHA1: 35048333f9c72fdcd5d53573e4b63f293badab31 SHA256: 0a0ee037cee6f5b650667972e75a7c22622a4e8ab1587126461f0cf9eaf8e77d SHA512: 83054cb3f40e7553db21d9b12b82f5901a3c2999afa3d5953146d293fcaa7240b47cf3852132539e0f34c417d047372e1d3e6750e6f5c23dd4a32c66dd9a3c30 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. Available measures include adjusted Rand index (ARI), normalized information distance (NID), normalized mutual information (NMI), normalized variation information (NVI) and entropy, as described in Vinh et al (2009) . Include AMI (Adjusted Mutual Information) since version 0.1.2, a modified version of ARI (MARI), as described in Sundqvist et al. and simple Chi-square distance since version 1.0.0. Package: r-cran-arima2 Architecture: arm64 Version: 3.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-arima2_3.4.3-1.ca2604.1_arm64.deb Size: 192926 MD5sum: 2243338e384be872e11307c252914265 SHA1: b869b9bbe17392ec65da0a3ed0af5c72d4e2f188 SHA256: d895ce74768a5b5b5149f2ba8542adfa8b8e4df73debcf66f9f0083e32e67fad SHA512: c2b75a3518c24e39cb62db19621fb32d03e2165826c3031279d2e547b3a9430e007019c8ab3514623093c016064ef1b1d930358a40eb1df9e59f1e052c954d16 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. This approach frequently leads to models that have model likelihood greater than or equal to that of the likelihood obtained by fitting the same model using the arima() function from the 'stats' package. This package enables proper optimization of model likelihoods, which is a necessary condition for performing likelihood ratio tests. This package relies heavily on the source code of the arima() function of the 'stats' package. For more information, please see Jesse Wheeler and Edward L. Ionides (2025) . Package: r-cran-arkhaia Architecture: arm64 Version: 0.5.5-1.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), 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_arm64.deb Size: 194714 MD5sum: c9d2dfc1fabf83278548ec93fa825b85 SHA1: 019bffe90fef87c380c3b65e6a9e6147eade0a3a SHA256: 4b77ff8b3cbdb97f7618232dec28a3fd45bfe4077253599acc825638425b91b4 SHA512: 43b3b87a2260ae6a613cf1a253ee30e022f068aff47c131637eeae675e12974b06d129ccc5c696cf073614b97f087132b6d04aa2c71671c0fbb88bf10b174bf2 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. Methods include effect sizes for measuring homogeneity, simulation from a truncated Poisson distribution for random right-censoring of count data, and least-squares spectral analysis by lowest frequency iteration for model fitting. Collins-Elliott (2026) . Package: r-cran-armspp Architecture: arm64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 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_arm64.deb Size: 127060 MD5sum: b7afc182c504707bc943cf131d22b039 SHA1: fc9add058c668c57cf62a54147bf60559f6f43a9 SHA256: baa3f385bff8ef721da81a81e3ca4954aca08add19ff36de932ee468d150c352 SHA512: 162e5a4c02888d7baf60ef0dc70906ed3d62d50ce36d808c67ee4adbb8dabc47bd42913a7c795856d1c8ad1d2c55e93e92df6c696a25943919e28ec75175eac9 Homepage: https://cran.r-project.org/package=armspp Description: CRAN Package 'armspp' (Adaptive Rejection Metropolis Sampling (ARMS) via 'Rcpp') An efficient 'Rcpp' implementation of the Adaptive Rejection Metropolis Sampling (ARMS) algorithm proposed by Gilks, W. R., Best, N. G. and Tan, K. K. C. (1995) . This allows for sampling from a univariate target probability distribution specified by its (potentially unnormalised) log density. Package: r-cran-arrangements Architecture: arm64 Version: 1.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 491 Depends: libc6 (>= 2.17), libgmp10 (>= 2:6.3.0+dfsg), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gmp, r-cran-r6 Suggests: r-cran-foreach, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-arrangements_1.1.10-1.ca2604.1_arm64.deb Size: 259980 MD5sum: 4ecdfc2689ef807724177532c6e8791e SHA1: eba4f60c56df01b2c169f118e2cbbf5cb4e4fbde SHA256: 55a3072954da52464ba9d0744fea72985791fb393b670bf67a50240fdd5b5834 SHA512: 3735e4ac7d07ba7f2ab3acddc9028cc113a97572086bfe9eba3fe7e643787eb8630bea5c5956465bdf0e10b1cf28d86220c3f40a6f4bc457c492aa4397c51469 Homepage: https://cran.r-project.org/package=arrangements Description: CRAN Package 'arrangements' (Fast Generators and Iterators for Permutations, Combinations,Integer Partitions and Compositions) Fast generators and iterators for permutations, combinations, integer partitions and compositions. 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Package: r-cran-arrapply Architecture: arm64 Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 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_arm64.deb Size: 88496 MD5sum: d2ec9097e80e3760a3bf8069e7f7527c SHA1: abaeded9c8a7c6d76921a1a7940c10e18d6ffbec SHA256: 10073329183023dd0cfac60e7bf52f786e7fd705944662082fcc355571a139a5 SHA512: 9670d501658e9283dca30d4250be9e519fa460c9f6e2c2fd48db5b894276834c0aadccc9df39ed0c626c8e35544b99ff4c3c8f4401077c1fa04f92bc0a08ffae Homepage: https://cran.r-project.org/package=arrApply Description: CRAN Package 'arrApply' (Apply a Function to a Margin of an Array) High performance variant of apply() for a fixed set of functions. Considerable speedup of this implementation is a trade-off for universality: user defined functions cannot be used with this package. However, about 20 most currently employed functions are available for usage. They can be divided in three types: reducing functions (like mean(), sum() etc., giving a scalar when applied to a vector), mapping function (like normalise(), cumsum() etc., giving a vector of the same length as the input vector) and finally, vector reducing function (like diff() which produces result vector of a length different from the length of input vector). Optional or mandatory additional arguments required by some functions (e.g. norm type for norm()) can be passed as named arguments in '...'. Package: r-cran-arrow Architecture: arm64 Version: 24.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 40146 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), zlib1g (>= 1:1.2.0), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-bit64, r-cran-glue, r-cran-purrr, r-cran-r6, r-cran-rlang, r-cran-tidyselect, r-cran-vctrs, r-cran-cpp11 Suggests: r-cran-blob, r-cran-curl, r-cran-cli, r-cran-dbi, r-cran-dbplyr, r-cran-decor, r-cran-distro, r-cran-dplyr, r-cran-duckdb, r-cran-hms, r-cran-jsonlite, r-cran-knitr, r-cran-lubridate, r-cran-pillar, r-cran-pkgload, r-cran-reticulate, r-cran-rmarkdown, r-cran-stringi, r-cran-stringr, r-cran-sys, r-cran-testthat, r-cran-tibble, r-cran-tzdb, r-cran-withr Filename: pool/dists/resolute/main/r-cran-arrow_24.0.0-1.ca2604.1_arm64.deb Size: 11864964 MD5sum: 46642f07bdc0aef9e6fbb8c03c3f1035 SHA1: 885732200dd98502aceca588861fc57b9c09f98b SHA256: 35b6f12e6dc01c6b4b3617d45648851ebbc531a8af39dd1b77e9ec4c4429ff98 SHA512: d4e85434554aaf3f68c9a45bf1a43958ed82bc9918e66557e6c951824da67149714b54b312fbe963a09536b4d3e204664d6f31f531c7af3936fd59ed35cb457c Homepage: https://cran.r-project.org/package=arrow Description: CRAN Package 'arrow' (Integration to 'Apache' 'Arrow') 'Apache' 'Arrow' is a cross-language development platform for in-memory data. 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Package: r-cran-artma Architecture: arm64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1208 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 347550 MD5sum: 3a91db6b1a09e5c97957ee908f90cbe8 SHA1: ffb00dcb640956b9c9b6dbfb1353234ffbdc6d80 SHA256: e0f4381cac49c93211019aa24eee084684824eb50e22121ed7a7b47a0619aae3 SHA512: a921be24619ef4d27fe5a2f3988c4e544493179999c65e9eed3a34b6644e870339e7783a75be6e4c9b4873bbcb77371cd4795e20991a70c7f40fe2cec2d4b7a4 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: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1540 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_arm64.deb Size: 1094842 MD5sum: 3e278829b04723c8afd4ff87c06001df SHA1: 1cea238c3b9eb5297d78f122b64b0151587680fd SHA256: e1181776ba4cc47b05af94e73bd3da5d4a439ffb19245fc6a20df69f13486b57 SHA512: ec2370b5a49e70d0890118f3a2cf0034f510b30bb952b64f643c517a1acd11d94fd037112346976ac93aecf9f4b303d55952253d9e13069ca388098086a438af 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: arm64 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_arm64.deb Size: 2543978 MD5sum: 28cc525b4a52211a5f86cdfe0366e767 SHA1: 04b5680bdedcadd45ea0f096c373431388bc563d SHA256: 2ea307ea69301b171ded19a74e201f1bfc4412eca878901b71576592a1dcdd0f SHA512: 183e4df3ce2b46005abb574d062804b94f81ea8cd8fb3dbe8c75737f4632c96dd985c56da22e7bf5cf08e89e5b1691279ab1a5389cf4044d96aa58528023edc3 Homepage: https://cran.r-project.org/package=arules Description: CRAN Package 'arules' (Mining Association Rules and Frequent Itemsets) Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). 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Hahsler et al (2019) . Package: r-cran-arulessequences Architecture: arm64 Version: 0.2-32-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3451 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_arm64.deb Size: 1101134 MD5sum: e72edce6af21a9622de52232f10dcb99 SHA1: 759f75abb50932d728596db05aa23d0a318965a9 SHA256: 570e515f2028de5377e5ae2d717abcda7c1e9cb525f0bff31f989b0e436068cd SHA512: a6944bb8cafcb655ebd87c276725796bc62d22152c9df1b982ac6766b93233f366eab6fc8505373b9a4558bf415b15be585e2eb6de7a4a1a2cb38e2ff001b807 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-asianoption Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 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-asianoption_0.2.0-1.ca2604.1_arm64.deb Size: 210550 MD5sum: 5fa7f28f33b8b3eec8364361e83ac6c1 SHA1: 08f83abaf308ee3e5984e62c741ab397021e38e0 SHA256: f3d763d5682a070ef1de1d126d4eb151f687cf0d052aeeafdd1250dd1198765f SHA512: 0878d1bc872f74cfb3c16d1d4244e22939834b6160957b4b35edddcd5ada3d8e17852d074d6dd945ce3936cbe08319819e813898c42435c987ab2d67a35700fe Homepage: https://cran.r-project.org/package=AsianOption Description: CRAN Package 'AsianOption' (Asian Option Pricing under Price Impact) Implements the framework of Tiwari and Majumdar (2025) for valuing arithmetic and geometric Asian options under transient and permanent market impact. 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Package: r-cran-askpass Architecture: arm64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sys Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-askpass_1.2.1-1.ca2604.1_arm64.deb Size: 23340 MD5sum: d64c194aada37373bc14c168d58ad43a SHA1: 46f5c1f01e5365d3d0578e24e5021c09aa447d5f SHA256: fab05ee8fa478e483742d43e1fcc6317dedf753836e525c34205dfa1fab42c21 SHA512: 68c97ae577030c786b869583a52528a2aefbb6aa37f3093ba68d4f37b92c2d779e76cd59ecb5da2f01f0f420a44c2f42c834e13430cad1a47c5a788b7c8a5115 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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Package: r-cran-asmap Architecture: arm64 Version: 1.0-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2589 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-qtl, r-cran-lattice, r-cran-fields, r-cran-rcolorbrewer, r-cran-gtools Suggests: r-cran-knitr, r-cran-formatr, r-cran-digest Filename: pool/dists/resolute/main/r-cran-asmap_1.0-8-1.ca2604.1_arm64.deb Size: 2345776 MD5sum: 5b6fad9f79f4507914b78802e35e61b2 SHA1: ad0b36944c38c3f61954e43c6d45ebb361958003 SHA256: 69c72b0797086f2548d02f5bb3764029448024fbfbbe3687ba60175c4e51031e SHA512: 3ae79219dd10fd9812db6a0485e968aa0b74ca0d93b8d083c5c1d48285a0e75f9c8bac15360f40c0ec6395be5ac7b5cc90a53cb3ee2c7f27b7e0383105a968b0 Homepage: https://cran.r-project.org/package=ASMap Description: CRAN Package 'ASMap' (Linkage Map Construction using the MSTmap Algorithm) Functions for Accurate and Speedy linkage map construction, manipulation and diagnosis of Doubled Haploid, Backcross and Recombinant Inbred 'R/qtl' objects. 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Package: r-cran-asmbpls Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2927 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-asmbpls_1.0.0-1.ca2604.1_arm64.deb Size: 1792076 MD5sum: 34b431db41d2526d02403e76e0a60469 SHA1: cb1d42e1f0b1e5abcec8d53ecb551d45a3607745 SHA256: 27d571f0fc118d657f7ccc3252b7db7600c48c8798bd1937f04de723a9e685c3 SHA512: 73e09ec8b87dec7b914c79bbac7bf89316ef2bf1bfbd6caee6ae07bdf6bed0b40e9fd39edfc0ddc02a9e868237e82b9f99d820c50a6fca0aaaa5a33a7dfce546 Homepage: https://cran.r-project.org/package=asmbPLS Description: CRAN Package 'asmbPLS' (Predicting and Classifying Patient Phenotypes with Multi-OmicsData) Adaptive Sparse Multi-block Partial Least Square, a supervised algorithm, is an extension of the Sparse Multi-block Partial Least Square, which allows different quantiles to be used in different blocks of different partial least square components to decide the proportion of features to be retained. The best combinations of quantiles can be chosen from a set of user-defined quantiles combinations by cross-validation. By doing this, it enables us to do the feature selection for different blocks, and the selected features can then be further used to predict the outcome. For example, in biomedical applications, clinical covariates plus different types of omics data such as microbiome, metabolome, mRNA data, methylation data, copy number variation data might be predictive for patients outcome such as survival time or response to therapy. Different types of data could be put in different blocks and along with survival time to fit the model. The fitted model can then be used to predict the survival for the new samples with the corresponding clinical covariates and omics data. In addition, Adaptive Sparse Multi-block Partial Least Square Discriminant Analysis is also included, which extends Adaptive Sparse Multi-block Partial Least Square for classifying the categorical outcome. Package: r-cran-aspline Architecture: arm64 Version: 0.2.0-1.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-magrittr, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-splines2, r-cran-rcpp, r-cran-mgcv, r-cran-rlang Suggests: r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-aspline_0.2.0-1.ca2604.1_arm64.deb Size: 203448 MD5sum: c7144f812ebcdd050e0fc3dd25265aac SHA1: 37a56fb960fd6f5bc9ae3383a94dc84249cd094b SHA256: ae7199230764d17de54492fca5120809792386ba0b6173f55d57e767865acc17 SHA512: 4360441fc3d0eb4c04851637e60df26f1b229a499208d6bcdda5545771fd5d1fdf75556b23fc517196e6c302a8067ad9bfaabed061a7a2fa2514063752542ebf Homepage: https://cran.r-project.org/package=aspline Description: CRAN Package 'aspline' (Spline Regression with Adaptive Knot Selection) Perform one-dimensional spline regression with automatic knot selection. 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", . Package: r-cran-assa Architecture: arm64 Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-assa_2.0-1.ca2604.1_arm64.deb Size: 170784 MD5sum: ebb3ffd7fb9133e35508fb123cecb3a8 SHA1: 38939ed42a781b475b4c96361a87ad4ccd67dc7c SHA256: 9388607413a3f2d5d707d41f61c68ba891a1b1cf65e0940931ac7aa868a0c9ec SHA512: 63e514575ab57876aed12968296abb18f8e3a8e0792d4bc5a7ea5a52956fca0d5bc5319f15ce7f6177595dfae63c369d1c48aa3a95e0af50a8b23ffbe3598981 Homepage: https://cran.r-project.org/package=ASSA Description: CRAN Package 'ASSA' (Applied Singular Spectrum Analysis (ASSA)) Functions to model and decompose time series into principal components using singular spectrum analysis (de Carvalho and Rua (2017) ; de Carvalho et al (2012) ). Package: r-cran-assist Architecture: arm64 Version: 3.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1048 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), 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_arm64.deb Size: 801144 MD5sum: 07c826cd23e7048b78d01e73ffe019ef SHA1: 42e6516ba13ac78c9e7414e1a08b3e5b0fe09785 SHA256: e63d2fb2273bc081e93f4aeee6fab9aaf3887d0d33f9ae59edc0974d0242f6b5 SHA512: 058f6242bceaf4dd3c2f7b8b1c692d48ca81d50dca430971c752ceeed3817552385914dcd7cfb14a3697d28a9306ca6471a8e317e801a10bd131729ac189b3f4 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: arm64 Version: 0.3-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 193220 MD5sum: 26f60022bcf457cf2a057f1e2cb56808 SHA1: 7d656652955d08d5844518add3387a7b0be853d1 SHA256: c4d0f9f8155a8bf79253baa662ac7833fd53b4332e068649219a38f95b9aa6a2 SHA512: ede6a25b3a82f1ca23edc9a9473a7992556085dbb584838bb94497bbfd572150fc79cfb77152203b4b379e60da819f0afe03aa63790b7c33d7a1f020d0882b57 Homepage: https://cran.r-project.org/package=aster2 Description: CRAN Package 'aster2' (Aster Models) Aster models are exponential family regression models for life history analysis. They are like generalized linear models except that elements of the response vector can have different families (e. g., some Bernoulli, some Poisson, some zero-truncated Poisson, some normal) and can be dependent, the dependence indicated by a graphical structure. Discrete time survival analysis, zero-inflated Poisson regression, and generalized linear models that are exponential family (e. g., logistic regression and Poisson regression with log link) are special cases. Main use is for data in which there is survival over discrete time periods and there is additional data about what happens conditional on survival (e. g., number of offspring). Uses the exponential family canonical parameterization (aster transform of usual parameterization). Unlike the aster package, this package does dependence groups (nodes of the graph need not be conditionally independent given their predecessor node), including multinomial and two-parameter normal as families. Thus this package also generalizes mark-capture-recapture analysis. Package: r-cran-aster Architecture: arm64 Version: 1.3-7-1.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_arm64.deb Size: 2590574 MD5sum: 6aac23010af76885ed625436f25c9321 SHA1: 343b349f197d82f1284a1d5d44b3593c45471b61 SHA256: 0a6f96fa50d9a489599ba7ceebb4775aac77aa0f519b43cd4d174840ee958f40 SHA512: 7825ea6ae989c7506ce257ced55ba53ad7be362a549b28d716ee00b2220e420a3209a9ce82741d651b4f375bdcb2f1f67d8bcd79255b77ae0c04e5f65563754d Homepage: https://cran.r-project.org/package=aster Description: CRAN Package 'aster' (Aster Models) Aster models (Geyer, Wagenius, and Shaw, 2007, ; Shaw, Geyer, Wagenius, Hangelbroek, and Etterson, 2008, ; Geyer, Ridley, Latta, Etterson, and Shaw, 2013, ) are exponential family regression models for life history analysis. They are like generalized linear models except that elements of the response vector can have different families (e. g., some Bernoulli, some Poisson, some zero-truncated Poisson, some normal) and can be dependent, the dependence indicated by a graphical structure. Discrete time survival analysis, life table analysis, zero-inflated Poisson regression, and generalized linear models that are exponential family (e. g., logistic regression and Poisson regression with log link) are special cases. Main use is for data in which there is survival over discrete time periods and there is additional data about what happens conditional on survival (e. g., number of offspring). Uses the exponential family canonical parameterization (aster transform of usual parameterization). There are also random effects versions of these models. Package: r-cran-asterisk Architecture: arm64 Version: 1.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3565 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_arm64.deb Size: 1809048 MD5sum: 0560ae09f89e2c1ab48afebda4f08051 SHA1: 9db13aa22cf5f81230094d8568fceb924a1f6598 SHA256: ccd81e066fb4aa1cdc9be4c79e10ff357275f7f2651b2c6b79675eeebd973408 SHA512: c7683894173394302d01f0aae01dcb847989d4b34a80340a908b8e99a30ab466d1dcd17af83f220e8428b7a29303532b841880bab6ad32277fcefb89f841b40b Homepage: https://cran.r-project.org/package=asteRisk Description: CRAN Package 'asteRisk' (Computation of Satellite Position) Provides basic functionalities to calculate the position of satellites given a known state vector. The package includes implementations of the SGP4 and SDP4 simplified perturbation models to propagate orbital state vectors, as well as utilities to read TLE files and convert coordinates between different frames of reference. Several of the functionalities of the package (including the high-precision numerical orbit propagator) require the coefficients and data included in the 'asteRiskData' package, available in a 'drat' repository. To install this data package, run 'install.packages("asteRiskData", repos="https://rafael-ayala.github.io/drat/")'. Felix R. Hoots, Ronald L. Roehrich and T.S. Kelso (1988) . David Vallado, Paul Crawford, Richard Hujsak and T.S. Kelso (2012) . Felix R. Hoots, Paul W. Schumacher Jr. and Robert A. Glover (2014) . Package: r-cran-astgrepr Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4118 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_arm64.deb Size: 1114170 MD5sum: f18743773a781a25f4cc1f4923c75c02 SHA1: 7e65c9450ae7612defb710b85c630854ab12e7d2 SHA256: 576ab066e4be87e68fa5dacc880299051696f7c3eb250dbba779eb8307659873 SHA512: 2e0ce68940f0d6e38fc725b5cf0fa66fcfbdfba487b514bc730dfea8fdfd40db7165b2a905c588947a98e3c0ec336bb90ee9f55fa122c3c9f5d3a31892c01886 Homepage: https://cran.r-project.org/package=astgrepr Description: CRAN Package 'astgrepr' (Parse and Manipulate R Code) Parsing R code is key to build tools such as linters and stylers. This package provides a binding to the 'Rust' crate 'ast-grep' so that one can parse and explore R code. 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Package: r-cran-astronomyengine Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1208 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_arm64.deb Size: 464108 MD5sum: 3c44cf5e83cae47b53ce464707cdae21 SHA1: 6b28d2426eb3ed9e410c1ccacdb6c7185ec2a266 SHA256: 514ef6ade65cf7b09969d7a3b865bf673f89b38f80748f1a31809cb7c5ace363 SHA512: fd029e608c4ff6a41e0161dd8ef44eed6d41977c96fa88b65c3bb216aa260f5a8ead20a833851917ed435387848d69a442e28e8770fd2e1e02fcd8503abee6c6 Homepage: https://cran.r-project.org/package=astronomyengine Description: CRAN Package 'astronomyengine' (R Bindings to the 'Astronomy Engine' C Library) Provides access to the 'Astronomy Engine' C library () by Don Cross. The library calculates positions of the Sun, Moon, and planets, and predicts astronomical events such as rise/set times, lunar phases, equinoxes, solstices, eclipses, and transits. It is based on the 'VSOP87' planetary model and is accurate to within approximately one arcminute. This package bundles the single-file C source so that other R packages can link against it via 'LinkingTo' without shipping their own copy. Package: r-cran-asv Architecture: arm64 Version: 1.1.4-1.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), 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-freqdom, r-cran-rcpparmadillo, r-cran-rcppprogress Filename: pool/dists/resolute/main/r-cran-asv_1.1.4-1.ca2604.1_arm64.deb Size: 202774 MD5sum: d5a60167698b91dcd83ba0e555db8d7b SHA1: ee2d784f6afb19ea1b577a74503576423fcbd0de SHA256: c929a00377bf4e71b02bcdfbd6d4bb24bd30c5a4826483ff1dc307888601efed SHA512: 5c8cbbcc0a5d024048d9074e6f8d2fa79ce075bbfb5ac042575a920147e14799fcce17f3d10bc1121a861fe35ecdacec1bf504fcaaa65efacac1df45ec1b791e Homepage: https://cran.r-project.org/package=ASV Description: CRAN Package 'ASV' (Stochastic Volatility Models with or without Leverage) The efficient Markov chain Monte Carlo estimation of stochastic volatility models with and without leverage (asymmetric and symmetric stochastic volatility models). 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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-augsimex Architecture: arm64 Version: 3.7.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 502 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-formula, r-cran-nleqslv Filename: pool/dists/resolute/main/r-cran-augsimex_3.7.4-1.ca2604.1_arm64.deb Size: 265734 MD5sum: 8771d70789e9841989536e7eef0d3870 SHA1: aca3d4b880c7df9b721ee5bcb6794cc454dd1c1f SHA256: e9df323757d5f354399d9543b8f6779d29b2cae65faa9dd202f20a40336ea6ac SHA512: fbbbc4bf28518f7a0fbe6e78468c150981a628b9c1823224e3b9d141af6d37769a5b89d88f746c79762c8d7f311a46a8164900bd512d4a64c43f833e67c144db Homepage: https://cran.r-project.org/package=augSIMEX Description: CRAN Package 'augSIMEX' (Analysis of Data with Mixed Measurement Error andMisclassification in Covariates) Implementation of the augmented Simulation-Extrapolation (SIMEX) algorithm proposed by Yi et al. (2015) for analyzing the data with mixed measurement error and misclassification. The main function provides a similar summary output as that of glm() function. Both parametric and empirical SIMEX are considered in the package. Package: r-cran-aum Architecture: arm64 Version: 2024.6.19-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 219550 MD5sum: 8564fb705d5d6163cc4468a078720927 SHA1: 0779a7bf6db3f12aa4a2bcc148f53d2fa30a0529 SHA256: 0f0a9b4adb10057ab9eff23c9bde481b5d6a2c8280247fcd4095303ac33ca4b1 SHA512: 867926742bc195b304ccfb58be10bdba95fd8d872fe2abfecb71bafbf02947718ba7e37af2f05a69360c06e7d27120acb06fcf3bbdafc6498521f1785a7e7c36 Homepage: https://cran.r-project.org/package=aum Description: CRAN Package 'aum' (Area Under Minimum of False Positives and Negatives) Efficient algorithms for computing Area Under Minimum, directional derivatives, and line search optimization of a linear model, with objective defined as either max Area Under the Curve or min Area Under Minimum. Package: r-cran-autofrk Architecture: arm64 Version: 1.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 513 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_arm64.deb Size: 280454 MD5sum: 424c41fafd0e0087b78fa83d99fc60dd SHA1: 1f40d9ecef75e41febb7f1aeaacd98937051fd81 SHA256: 33b7056bb74afc5c7a5b3ffe430558c5e1e943be16b3010fd23bb8629747388a SHA512: bd45a6a522ad7f18209f3af5b9af8bacef674dad9e67d77e5a74d2153c12deb079cc4834511181925f7faffe3d36e49dc52d1de04b24fd01b94822e808b948d3 Homepage: https://cran.r-project.org/package=autoFRK Description: CRAN Package 'autoFRK' (Automatic Fixed Rank Kriging) Automatic fixed rank kriging for (irregularly located) spatial data using a class of basis functions with multi-resolution features and ordered in terms of their resolutions. The model parameters are estimated by maximum likelihood (ML) and the number of basis functions is determined by Akaike's information criterion (AIC). For spatial data with either one realization or independent replicates, the ML estimates and AIC are efficiently computed using their closed-form expressions when no missing value occurs. Details regarding the basis function construction, parameter estimation, and AIC calculation can be found in Tzeng and Huang (2018) . For data with missing values, the ML estimates are obtained using the expectation- maximization algorithm. Apart from the number of basis functions, there are no other tuning parameters, making the method fully automatic. Users can also include a stationary structure in the spatial covariance, which utilizes 'LatticeKrig' package. Package: r-cran-automerge Architecture: arm64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2880 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_arm64.deb Size: 1114828 MD5sum: dfcbb7bc85588eebbe622cd2655ea8a2 SHA1: f9dfc09044018b21b187aa996f1e35dcf53dc222 SHA256: edc3db79ef6e7a6f5c55160fe8ef1aab2d463cbbecf80b7ca552fb5fc8f2a57d SHA512: 17df773f5eb7b962507434e7d9992597184fb570fbb9e9b117e717049dc0730a58f34095c78781205cee79e9ade5c93540b7bad81255ee2ffd2211de637b1b8e Homepage: https://cran.r-project.org/package=automerge Description: CRAN Package 'automerge' (R Bindings for 'Automerge' 'CRDT' Library) Provides R bindings to the 'Automerge' Conflict-free Replicated Data Type ('CRDT') library. 'Automerge' enables automatic merging of concurrent changes without conflicts, making it ideal for distributed systems, collaborative applications, and offline-first architectures. The approach of local-first software was proposed in Kleppmann, M., Wiggins, A., van Hardenberg, P., McGranaghan, M. (2019) . This package supports all 'Automerge' data types (maps, lists, text, counters) and provides both low-level and high-level synchronization protocols for seamless interoperability with 'JavaScript' and other 'Automerge' implementations. Package: r-cran-autometric Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 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_arm64.deb Size: 197412 MD5sum: 6fffb7ba51743df672adcbd2d5d4a1c4 SHA1: fc92f771b053c1b8b670a40bc39fab0d81446e51 SHA256: e9214469549fc3c557d157db34cef3c93529dfb6f35603b2fcba94e34df7055c SHA512: 8fbc191ea4170c4757690c2731a9b8818fe194f4439cd43c4bcb259c1da8586cf39f3e5c77b3b68c6082af1a22876e67a5c54b527278f72be024190d0a9aee1b 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: arm64 Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1507 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_arm64.deb Size: 874686 MD5sum: 606833414ed836f6452aae37a70e0304 SHA1: d0690491ba01958c13d06be7d4eaee70ef769bee SHA256: a67d9dcba6d3dd0a07256b97596d52bb8d326f66b02341c8d9bb165770d6972a SHA512: 2342468b5569148591f369cb3137f77b76c2d5e0a2a1ba1b238b6144244170f12cbbe737991e3d297d52c65ababdc69abe13096787f7a60e58c429e34e3eafd6 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: arm64 Version: 0.9.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 876 Depends: libavfilter11 (>= 7:8.0.1), libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-curl, r-cran-testthat, r-cran-ps, r-cran-ggplot2, r-cran-gapminder Filename: pool/dists/resolute/main/r-cran-av_0.9.6-1.ca2604.1_arm64.deb Size: 801698 MD5sum: d22398f62360c8020243dd693dc1fd76 SHA1: 4f75b715c611b83eea376f2adc143ce3f05ff897 SHA256: d6c72a4ab5bba37be3d81ead88176a08b6fe9dfa82348645af86696eaf979cb2 SHA512: bb34f29bc503488cb75087ecd902135693ddc3e262e99c1b177033b06d2296b3b7a2c347e3a9dc28c6e3cf38c2fcf7cb093168fa8e59186bd55fd7765568df94 Homepage: https://cran.r-project.org/package=av Description: CRAN Package 'av' (Working with Audio and Video in R) Bindings to 'FFmpeg' AV library for working with audio and video in R. Generates high quality video from images or R graphics with custom audio. 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Package: r-cran-avar Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 682 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_arm64.deb Size: 439276 MD5sum: 348ae0a6f2633ff6986a78cdfae318d6 SHA1: 96ecab2308954c5965937ab4048e54e46b5a5111 SHA256: 7d423bacc442a01b43b0ba452ede1b88e1fcca224c04eb50b2a829fd4f88db7c SHA512: 5ba917c017e8b0f06363cabd4bd37b98a397942bdfad45d3aa0ca405707c6af5f35d2ba1ceec5bd9cbe73e8d4569b015451309e12c78aee1c5c20d24901cefa1 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-awdb Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4652 Depends: libc6 (>= 2.39), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-httr2, r-cran-rlang, r-cran-sf Filename: pool/dists/resolute/main/r-cran-awdb_0.1.3-1.ca2604.1_arm64.deb Size: 2676526 MD5sum: 7243ab9054e0ddd0c9da591698d365ad SHA1: 58d065d877a2642e022e621a27935dde5e52b768 SHA256: 811d8924bdb5c7784a110ad98e4ddc0ad924f4a6cf38b7dffedc60b141681dc8 SHA512: 1cc01797c4e2dac910887f3ef1e408b31c121070147f7901203bd597a021147440419a44a74c1b586ed0bfda3079db62464c1452ea17e11afefb1635db6e79f3 Homepage: https://cran.r-project.org/package=awdb Description: CRAN Package 'awdb' (Query the USDA NWCC Air and Water Database REST API) Query the four endpoints of the 'Air and Water Database (AWDB) REST API' maintained by the National Water and Climate Center (NWCC) at the United States Department of Agriculture (USDA). 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Package: r-cran-aws Architecture: arm64 Version: 2.5-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1506 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_arm64.deb Size: 1218154 MD5sum: 25187b22f5013eaeeb67a6604ca4088a SHA1: 5986f54d243f3b44beb66eeac37eafb63f965374 SHA256: 78494a03eb2b8fb0aeba8ce05f2826934269d44678072bd23b59ccca9ffc94fd SHA512: 7f16f9928aac65da13a16d15f0d3525a74d70fafb1b76898bf563aa566c8d1c0a53bba4fbe0a04fb2bebc0c2b55717d2dff3f7717d9ea38d1e440cb8dee55874 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: arm64 Version: 0.1.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1069 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_arm64.deb Size: 762942 MD5sum: 2c818e8a064b54ac3bada80422e4cd58 SHA1: cf5817dbc3bc584b2a909b7fd27fe3087be52877 SHA256: 953128137f2c1eef398ae6bf3bd9cf871ccee3c800c61e8533242d118acb8cc0 SHA512: cd2fee3b5917d62dfdfc72fbb9dc6b67c5d28a735fa3d8f822c1f718ce8a96bb578d1303a77c304a1c4b3629702b94a0f38c120711a53b306e867257aad2946b Homepage: https://cran.r-project.org/package=babelmixr2 Description: CRAN Package 'babelmixr2' (Use 'nlmixr2' to Interact with Open Source and CommercialSoftware) Run other estimation and simulation software via the 'nlmixr2' (Fidler et al (2019) ) interface including 'PKNCA', 'NONMEM' and 'Monolix'. While not required, you can get/install the 'lixoftConnectors' package in the 'Monolix' installation, as described at the following url . When 'lixoftConnectors' is available, 'Monolix' can be run directly instead of setting up command line usage. Package: r-cran-backbone Architecture: arm64 Version: 3.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2167 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1467908 MD5sum: d43ed2fc8f8e14014416aaeb7e4aacd3 SHA1: 344ada84c1f6ab75a8415e9793c0032e847f0fa6 SHA256: cd5b3e2022aca9acdbdae6c24f360413ee60d37bece6a061fe9d5ab45047b810 SHA512: 848161983c0c854950d52683fd764df18b6f9ea6eb9421ff61e7ceb0fd7d132024b146c1c049fd084290a596b578790db918f2f8e359216d968995f22d0b3ffb Homepage: https://cran.r-project.org/package=backbone Description: CRAN Package 'backbone' (Extracts the Backbone from Networks) An implementation of methods for extracting a sparse unweighted network (i.e. a backbone) from an unweighted network (e.g., Hamann et al., 2016 ), a weighted network (e.g., Serrano et al., 2009 ), or a weighted projection (e.g., Neal et al., 2021 ). Package: r-cran-backports Architecture: arm64 Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 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_arm64.deb Size: 114548 MD5sum: 7aac553f526e3eb25b6e2c8a57b72e76 SHA1: 6695a4d8e742037926a3585f1beba6166cf329c1 SHA256: 6ee32bd4198776b04035a4803630818b48be7c7a7bd94befb9e71e08e0ede0c8 SHA512: af5d1fa537fee58a36503c694ed3c66543db544be24d7fbfbf5d837db2faad29d02dbf12dd424f8e9ed678abd2c19efb24dad01426d8331d0377396a7b643ffb Homepage: https://cran.r-project.org/package=backports Description: CRAN Package 'backports' (Reimplementations of Functions Introduced Since R-3.0.0) Functions introduced or changed since R v3.0.0 are re-implemented in this package. The backports are conditionally exported in order to let R resolve the function name to either the implemented backport, or the respective base version, if available. Package developers can make use of new functions or arguments by selectively importing specific backports to support older installations. Package: r-cran-backshift Architecture: arm64 Version: 0.1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 750 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 464318 MD5sum: 23642ddcdb2bde18bdb43cf90b31092a SHA1: 7978c111695ad063e23ecee27471335e2bf3301d SHA256: 90a3a0d26cd964136d819dc9171da7d9b5efac87c71152745437d311d6dba4f5 SHA512: 8f805f678007fc0f2256de458d59544ac03b65ed9fc9df0f401a1b88a1ab4cf94d933b2265419be5bd0b6e08b5ef1969e1fbf7fc78fa845348a1b58501fe4730 Homepage: https://cran.r-project.org/package=backShift Description: CRAN Package 'backShift' (Learning Causal Cyclic Graphs from Unknown Shift Interventions) Code for 'backShift', an algorithm to estimate the connectivity matrix of a directed (possibly cyclic) graph with hidden variables. The underlying system is required to be linear and we assume that observations under different shift interventions are available. For more details, see . Package: r-cran-baclava Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 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_arm64.deb Size: 313130 MD5sum: bfeb6dc12b06466f8b368d1c5d5456d4 SHA1: 33f76c3375949a37f4d7a30bdeb2fe2601210c42 SHA256: 045d557149372b2fd69a81130cb1bc5c168c8f11751d40ff4ae2d20268db7c48 SHA512: dff44533f375f910ae397304313b699ffd894c82df5470cb4a850039550afc91d5e2466ca81057e0816d02982b2256b9230be64b6bcfc73e06e0361ad903b853 Homepage: https://cran.r-project.org/package=baclava Description: CRAN Package 'baclava' (Bayesian Analysis of Cancer Latency with Auxiliary VariableAugmentation) A novel data-augmentation Markov chain Monte Carlo sampling algorithm to fit a progressive compartmental model of disease in a Bayesian framework Morsomme, R.N., Holloway, S.T., Ryser, M.D. and Xu J. (2024) . Package: r-cran-bacontrees Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 458 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 308516 MD5sum: 6fe45a543410ddb83cbb5ea7578a55b7 SHA1: 3858bcbacfd82090824ba23af72269a006d6ff50 SHA256: e5e448952e5d3544a5d153bb17e8bc5b77e0edb5427a3f8f41d3b8432e0b182e SHA512: ff025ef14b06871d77a7360024a4b601adab28e5b794469c39aba879ec74c92d4991efee7e7d554100bb2e765084397b4e050642c6677704d5e3aff4a7a1cf47 Homepage: https://cran.r-project.org/package=bacontrees Description: CRAN Package 'bacontrees' (Bayesian Context Trees for Discrete Sequence Data) Models discrete sequential data using Bayesian Context Trees. Context trees, also known as Variable Length Markov Chains (VLMCs), are parsimonious Markov models where the order of dependence can vary with the observed past. Provides a generic 'R6' class structure that exposes the full tree for building custom algorithms, exact Bayesian inference via a bottom-up recursive algorithm (closed-form marginal likelihood, Maximum A Posteriori (MAP) tree, exact posterior probabilities, and exact sampling from the posterior), a frequentist estimator via the context algorithm with likelihood-ratio pruning, simulation utilities, and a Metropolis-Hastings sampler. See Paulichen and Freguglia (2026) . Package: r-cran-badp Architecture: arm64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2748 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 2192582 MD5sum: 6aad14553ac9739ef675df331b8ccfe9 SHA1: a794d435895b2b52732fcbfd2f0640770a692c80 SHA256: f15e94bd47517755af3284085a0a2090499c4616bbfbfd6d628948031f0a6692 SHA512: 1065c9d2cd03fff7f21a3a5ed41ac7a27cd1d13120332f3ed10c73236d9ffaaa423e76ef76bc9adb7ba988cb509112c6b7ade6a6df6ccc4ae226ded000ead41a 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: arm64 Version: 0.10.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9217 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_arm64.deb Size: 4848324 MD5sum: d6c7c3f563638bbb3aa3349f15b0f559 SHA1: 9202a5eb58a4ab4aa2eaa6590c1efa349e0ba0d5 SHA256: c1ca966ff56852b9a022269defc7a37ac4865bec21c3c016070b6512c97862b6 SHA512: 918d193c8ba6cda0d36a2a6bf24da70311bccfbe9e842c7d79023a05cb928639a2ca23a15f1874581292c0ac6da5b48098ee029bc57e2d6a4e676bd359a735c8 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: arm64 Version: 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 (>= 4.5.0), 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_arm64.deb Size: 294124 MD5sum: 8246a9e3fbaf957185b328b34c7571f7 SHA1: 08449c0676fce77843eb4d2e5094dc67c793ca6b SHA256: cb9e839e4fedf172f90bab1ea46acd36e53ff037611e7f3b87acaabb0362cb5e SHA512: 8db7072b9a58f2b828e19de348c4840493c7d133b0533006a444fba0e68037092fabe9535a69ebc1324970ce66bb7b56feef5f3e80f904b00054880e60dbe531 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: arm64 Version: 0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6734 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_arm64.deb Size: 2429434 MD5sum: 273d0acdcae78ff6aca7dd02f514cc25 SHA1: 89876b564c0f1f977603ab819ee34d0d9e88e9cc SHA256: 63c3b57463c67794948630c89041fbbf2b25f878f0012c58317ac040a52908db SHA512: 9b8f53f2299d46a084ecf29e28077d9c81124db90a309d700071fd46ee5d37612d691b679a3534f49443b41214d08afd9f22caf8912c92a9677caf0110a26252 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: arm64 Version: 0.2.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 952 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_arm64.deb Size: 621680 MD5sum: d7a9c6f19143c0f5fcc0f8cdca2a9e44 SHA1: 478e974f533bc34c5bce5414d9cde40e28d97538 SHA256: b07a064ee966d06ef87d416740b7c20daa1042e3af786d4a027df5359381dc6c SHA512: 1cded237f82fedadb8bc7b69c0405420836f5f1ae9553064c6572a28418a961472ee3bb49a7527ad3906e7017de47452e65e07d3d63d92a867fc3754b44c5909 Homepage: https://cran.r-project.org/package=bain Description: CRAN Package 'bain' (Bayes Factors for Informative Hypotheses) Computes approximated adjusted fractional Bayes factors for equality, inequality, and about equality constrained hypotheses. For a tutorial on this method, see Hoijtink, Mulder, van Lissa, & Gu, (2019) . For applications in structural equation modeling, see: Van Lissa, Gu, Mulder, Rosseel, Van Zundert, & Hoijtink, (2021) . For the statistical underpinnings, see Gu, Mulder, and Hoijtink (2018) ; Hoijtink, Gu, & Mulder, J. (2019) ; Hoijtink, Gu, Mulder, & Rosseel, (2019) . Package: r-cran-bakr Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6318 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_arm64.deb Size: 1960636 MD5sum: 744965c439ac780962233781d0d576f8 SHA1: a4fc47cf4b90a5b84ed210e659702ba680701f5f SHA256: 70d4d86f2e9ffce1754e2f0a7b6e1ffd0e370a40987b95b461718106b69eb9fb SHA512: cca4a37162ec52d76a9f318f382cf967424145330f42948a3cd053d25a914f6e95a4e595e2529310fe0a0c021f175e71880303b26c0b610aabc0d1747a9c8a1a Homepage: https://cran.r-project.org/package=bakR Description: CRAN Package 'bakR' (Analyze and Compare Nucleotide Recoding RNA Sequencing Datasets) Several implementations of a novel Bayesian hierarchical statistical model of nucleotide recoding RNA-seq experiments (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.) for analyzing and comparing NR-seq datasets (see 'Vock and Simon' (2023) ). NR-seq is a powerful extension of RNA-seq that provides information about the kinetics of RNA metabolism (e.g., RNA degradation rate constants), which is notably lacking in standard RNA-seq data. The statistical model makes maximal use of these high-throughput datasets by sharing information across transcripts to significantly improve uncertainty quantification and increase statistical power. 'bakR' includes a maximally efficient implementation of this model for conservative initial investigations of datasets. 'bakR' also provides more highly powered implementations using the probabilistic programming language 'Stan' to sample from the full posterior distribution. 'bakR' performs multiple-test adjusted statistical inference with the output of these model implementations to help biologists separate signal from background. Methods to automatically visualize key results and detect batch effects are also provided. Package: r-cran-balancedsampling Architecture: arm64 Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 367 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 162122 MD5sum: 1748391150f573acd85e25a7195b528b SHA1: e87c23099035b3a7326710259c3cbb972db404cc SHA256: 79a66bc789a70ce40b9e70fd65e873fbc4500ced9701d9015c0fce92e6eac90c SHA512: a1f5dcdee476ca5320fcd64466a2db0716d7d25442eac849600453e43589905b084037dc05057231379903f1df54727cd058057e061c8397d95917bc8cb3388a Homepage: https://cran.r-project.org/package=BalancedSampling Description: CRAN Package 'BalancedSampling' (Balanced and Spatially Balanced Sampling) Select balanced and spatially balanced probability samples in multi-dimensional spaces with any prescribed inclusion probabilities. It contains fast (C++ via Rcpp) implementations of the included sampling methods. The local pivotal method by Grafström, Lundström and Schelin (2012) and spatially correlated Poisson sampling by Grafström (2012) are included. Also the cube method (for balanced sampling) and the local cube method (for doubly balanced sampling) are included, see Grafström and Tillé (2013) . Package: r-cran-baldur Architecture: arm64 Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4536 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_arm64.deb Size: 2242870 MD5sum: d822e3f2da300d5b54587b93e1be0d45 SHA1: c890cf332ba38d45d3e1b7d8487a5ff7542ede25 SHA256: d2a8c1a48e694f1003cacb2c1751c0f4b7a6611b52f7ed5f56e95a2d92fbb5d6 SHA512: 26258d489142fdaacdcdcd678e2a1b323e076701a79987a05bf7a2969abad7f5c7aac4a8e62c7bb0b3c9038c5459dc4c256f898dc3d433195e27f7ef2309fc9d 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: arm64 Version: 1.3.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3044 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2422014 MD5sum: 96bed6eb342789634bcc0a26065ef0b5 SHA1: 28aeee2c7f86f7ece585ce16f32ccffac3368271 SHA256: 86bcf5003a66b45ef982c569e2897b7e1124f3233997127f5364d172b1d0e37b SHA512: 8aced14e2387bcca1c0d6577e416390d7640cb4e71837d5f09861d8a2b36e34f840f2dfbe7b5a2161f42063cdd7e3077e0112ddd38a7801421e49e2cef7d87b5 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: arm64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1737 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_arm64.deb Size: 511492 MD5sum: fa30903be708d8495f2394c59c1d0fe3 SHA1: 626cd95f7c7698aa606e8b7bc64d4318c06e91d8 SHA256: f3d66a02b5989a39820da6a8c24b95fc03b5263a56f0f0d2a66ed521ba7703c1 SHA512: dc1afceb62db747e7d60df8b469f99d3fe6808ef9ec32d6cc37b8a944e0a881202a04aba0f976a30db4ff219cd43fc939f0bc6d59550a4377d8fa015401ac161 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: arm64 Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1150 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 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_arm64.deb Size: 928408 MD5sum: 740ad1681303958f97cb76f3e6ca777b SHA1: 3dc7b917553e758a91066a249a0b78c22ec4c5e3 SHA256: d5bf365472f0969508af4beeaacf403d98731bc0028f20a9586ecb1bc50111e7 SHA512: 6b5264d2704c3503116687acc7578c2a3bc5ac502d72f6343613e3d39d02bc9ce975824b43da1867c3f687aed95d94db638b2d65a306d4e6dbc5e15999acae64 Homepage: https://cran.r-project.org/package=bama Description: CRAN Package 'bama' (High Dimensional Bayesian Mediation Analysis) Perform mediation analysis in the presence of high-dimensional mediators based on the potential outcome framework. Bayesian Mediation Analysis (BAMA), developed by Song et al (2019) and Song et al (2020) , relies on two Bayesian sparse linear mixed models to simultaneously analyze a relatively large number of mediators for a continuous exposure and outcome assuming a small number of mediators are truly active. This sparsity assumption also allows the extension of univariate mediator analysis by casting the identification of active mediators as a variable selection problem and applying Bayesian methods with continuous shrinkage priors on the effects. Package: r-cran-bambi Architecture: arm64 Version: 2.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 981 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_arm64.deb Size: 617316 MD5sum: a4654f961cdcf8ee6ebd2a0aa4697729 SHA1: 2ec39ab66848586b5385c9080aeb4e4759702201 SHA256: 8661e0d608f4dfb1c4c3b9de721cc486b08b17f6ad24f90c7d27c6091aac51b5 SHA512: be1370d4d54b4960b29a4d8d3150cc2768b5b570e9150f39aca49dfdeb75c0a540d95f3afc0bbb5f32944302c276ddb8c1c8fa109e6dce6e470210e85fa63465 Homepage: https://cran.r-project.org/package=BAMBI Description: CRAN Package 'BAMBI' (Bivariate Angular Mixture Models) Fit (using Bayesian methods) and simulate mixtures of univariate and bivariate angular distributions. Chakraborty and Wong (2021) . Package: r-cran-bamlss Architecture: arm64 Version: 1.2-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4558 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), 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_arm64.deb Size: 4027740 MD5sum: b043f9d0deb3b91cb4fdaf4d1ebf73a0 SHA1: ff9a0dbd69d0895b3b617921b97774f569228362 SHA256: f41e46ae7c7f3aefca3f2e6c6bbf7781b9f9593db6f64899083a5ad3447a0048 SHA512: 6ce9d56b916e3bcd0efc6c9b05157fe21cdd75250448f0b36512f3db12692a8da27e15ca6c9fb7338a3c4ddc1f1a0e14cc64dd043852373827f2d69830c2352b Homepage: https://cran.r-project.org/package=bamlss Description: CRAN Package 'bamlss' (Bayesian Additive Models for Location, Scale, and Shape (andBeyond)) Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019) and the R package in Umlauf, Klein, Simon, Zeileis (2021) . Package: r-cran-bamm Architecture: arm64 Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1728 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1076706 MD5sum: 9ded7814ef89a5f222728fdb7abd5eda SHA1: e1a678af10c028fca88ee0a0ef237528dac74a84 SHA256: 0487621ea5c9e5bc9f0f1a6a51558c6252727336e656dffab9f56ece0ce3b6e2 SHA512: ce52a25f894f65daccf81718997e5c6e643627d51ca8462366254b491f043b41454960252ebceed00f2f6b71fad8f934dc93cda4c466528cad2a6714bfa99cef Homepage: https://cran.r-project.org/package=bamm Description: CRAN Package 'bamm' (Species Distribution Models as a Function of Biotic, Abiotic andMovement Factors (BAM)) Species Distribution Modeling (SDM) is a practical methodology that aims to estimate the area of distribution of a species. However, most of the work has focused on estimating static expressions of the correlation between environmental variables. The outputs of correlative species distribution models can be interpreted as maps of the suitable environment for a species but not generally as maps of its actual distribution. Soberón and Peterson (2005) presented the BAM scheme, a heuristic framework that states that the occupied area of a species occurs on sites that have been accessible through dispersal (M) and have both favorable biotic (B) and abiotic conditions (A). The 'bamm' package implements classes and functions to operate on each element of the BAM and by using a cellular automata model where the occupied area of a species at time t is estimated by the multiplication of three binary matrices: one matrix represents movements (M), another abiotic -niche- tolerances (A), and a third, biotic interactions (B). The theoretical background of the package can be found in Soberón and Osorio-Olvera (2023) . Package: r-cran-bammtools Architecture: arm64 Version: 2.1.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1162 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1078174 MD5sum: 4448e98cafb5596f41a84bbe887cded2 SHA1: a954c9fc1584ec8583b2b4514453d44e870e2708 SHA256: 37b730bb43894d87234a95da2e57a3f4ba3eca4880394d4c5f9e20a6fc727669 SHA512: 2dcd0573afecd78d43ccf49460c6774895b16a3b826c2d858f0147d46d975ac10bb6880c105b379a19221b2a643a977aa801d4dd33906deea8bc374c800b6acb Homepage: https://cran.r-project.org/package=BAMMtools Description: CRAN Package 'BAMMtools' (Analysis and Visualization of Macroevolutionary Dynamics onPhylogenetic Trees) Provides functions for analyzing and visualizing complex macroevolutionary dynamics on phylogenetic trees. It is a companion package to the command line program BAMM (Bayesian Analysis of Macroevolutionary Mixtures) and is entirely oriented towards the analysis, interpretation, and visualization of evolutionary rates. Functionality includes visualization of rate shifts on phylogenies, estimating evolutionary rates through time, comparing posterior distributions of evolutionary rates across clades, comparing diversification models using Bayes factors, and more. Package: r-cran-bamp Architecture: arm64 Version: 2.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 931 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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_arm64.deb Size: 664078 MD5sum: cb42c84e5b7ad7116e8b04ab4ef402ba SHA1: 81aa7c4b06c61bda1a466dab6cf5226549d6435e SHA256: cf13e5a0c4ed3a17854ee1ff62882e6b9cdf6fadfc39baabde6caac9ad80e4f3 SHA512: a98ca153cda104d6b30ad65e33bf335c96cc1b1e31b241e1bd4ca43b9c9abfe30128c91b66916476ec0339dabd8c5bfa3f188dfe24023afffcb3e2385924ff6b Homepage: https://cran.r-project.org/package=bamp Description: CRAN Package 'bamp' (Bayesian Age-Period-Cohort Modeling and Prediction) Bayesian Age-Period-Cohort Modeling and Prediction using efficient Markov Chain Monte Carlo Methods. This is the R version of the previous BAMP software as described in Volker Schmid and Leonhard Held (2007) Bayesian Age-Period-Cohort Modeling and Prediction - BAMP, Journal of Statistical Software 21:8. This package includes checks of convergence using Gelman's R. Package: r-cran-banditpam Architecture: arm64 Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 702 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 327754 MD5sum: 558c339d4b1f2e577d38fdc6be1fd787 SHA1: 9deca0cb1810a0bf76b3f8a49fdf962fef17724e SHA256: f5763229a7bafff707b59aa1146bb6a9a23598daf2e777913310cb9395d8c67c SHA512: 0a03a743d36af310a3c85093ff2fe9261ceb1fce30682ab323a82d323cabb7e844699dfdd05800462743487bb8258cd207649dd1b62d398f6ab23ff0b3f754c8 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: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 958 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_arm64.deb Size: 592250 MD5sum: 9420c8fa7f1bf76553cfffcf1b7bb941 SHA1: d02d3d537210af14eb6417e86f11323feea350fa SHA256: d411d8e0eb83a642775d2357a1c283eee78be234bfa7f6640a3539cf749dcddb SHA512: 1ab37c8fff14ed93ed1ff2f1443c074e2509c870cab9ea7e510b26164e2c023a5ff91db9ffa5cd99ef376bb8c223db5369f94fbdfe0e212415f656879e1256d2 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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Package: r-cran-bark Architecture: arm64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 462 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bart, r-cran-e1071, r-cran-fdm2id, r-cran-rmarkdown, r-cran-knitr, r-cran-roxygen2, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-bark_1.0.5-1.ca2604.1_arm64.deb Size: 270680 MD5sum: b586565788980cd59fca93264f00a0a3 SHA1: f1629d61ec75195cc4b4affa1ab3307dbfed60be SHA256: 01757d8765e6313bf278fcdb32afe0c1e0d7ede7e22eb855c17d6cca94581e02 SHA512: 5408fb3928b597988e528c92fd90911876561d1444ac56a5e0e07ce83e9c5a25246d505c07bd87c76f4f7b03ee512a8656e644594aa5c34d6b61b242da9cac12 Homepage: https://cran.r-project.org/package=bark Description: CRAN Package 'bark' (Bayesian Additive Regression Kernels) Bayesian Additive Regression Kernels (BARK) provides an implementation for non-parametric function estimation using Levy Random Field priors for functions that may be represented as a sum of additive multivariate kernels. Kernels are located at every data point as in Support Vector Machines, however, coefficients may be heavily shrunk to zero under the Cauchy process prior, or even, set to zero. The number of active features is controlled by priors on precision parameters within the kernels, permitting feature selection. For more details see Ouyang, Z (2008) "Bayesian Additive Regression Kernels", Duke University. PhD dissertation, Chapter 3 and Wolpert, R. L, Clyde, M.A, and Tu, C. (2011) "Stochastic Expansions with Continuous Dictionaries Levy Adaptive Regression Kernels, Annals of Statistics Vol (39) pages 1916-1962 . Package: r-cran-barnard Architecture: arm64 Version: 1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-barnard_1.8-1.ca2604.1_arm64.deb Size: 23220 MD5sum: 59ebbe52ba2dc7dfd2e152909671fec4 SHA1: 169785c55c9f2608cb7e624774810729d18f16b8 SHA256: aed805850d929732faae745bc264a6e2ba4727b923ffc0e50fc564354038dd84 SHA512: 05cfd975b94873aca5629d183edf19cd44370369eabf17032a6409436a5e4abeca0591c30bb4eede1482db6e341c01aa6a923d3cfdfca25c5bcce16587d8b535 Homepage: https://cran.r-project.org/package=Barnard Description: CRAN Package 'Barnard' (Barnard's Unconditional Test) Barnard's unconditional test for 2x2 contingency tables. Package: r-cran-bart Architecture: arm64 Version: 2.9.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4767 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 4309794 MD5sum: 2e9965e97c31df3b20162bbb206ecaae SHA1: 9fe21fc2086a33d3e00a8dd0d741b3858fcc26f0 SHA256: 7457ca96c4cf4a6fbcb83920dd7079efafce89a453b12c0e10cd34b219182c8e SHA512: d3b9b6cd1023bb5b33c2e302e1a1649a77b889800b05f6b57075a94c9ff1d8c2c3e62a48f215c7c8d9d4917466d53b25e4aaac8c46645f9d2cf9aa9c27c8b78b Homepage: https://cran.r-project.org/package=BART Description: CRAN Package 'BART' (Bayesian Additive Regression Trees) Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. For more information see Sparapani, Spanbauer and McCulloch . Package: r-cran-bartcs Architecture: arm64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 545 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 275622 MD5sum: 4669743629f99b05d3cc231414a00919 SHA1: 3e57bdc1055ef4ffb29f600a4060af6534c3d5f2 SHA256: 95c5a8cfb21fa3142843c6557b5edf757c4f25c6929df5222defff30046c5ae8 SHA512: 78b570cfcc69c3a63b82ddfca7e6696ad7de8009419fec4ae5d5e21bc3fe52612afdfb42e29bea1f6c067a1750bfd0e68161afc63e637b81352238eab1330fdd Homepage: https://cran.r-project.org/package=bartcs Description: CRAN Package 'bartcs' (Bayesian Additive Regression Trees for Confounder Selection) Fit Bayesian Regression Additive Trees (BART) models to select true confounders from a large set of potential confounders and to estimate average treatment effect. For more information, see Kim et al. (2023) . Package: r-cran-bas Architecture: arm64 Version: 2.0.2-1.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_arm64.deb Size: 1163888 MD5sum: 452d1465bdd64f943690e403c86d8aa9 SHA1: 40f0e95df93004a537777fb1d54319459bb1747b SHA256: 9294027d59ffa41a6724114da5847561b2cd96bae70ea515c0391ff0763f0eea SHA512: 13911e1de9ea3814a2a6e8c04bbb5550ad9324ca0b057655aa36935490f95ed1adc888415ecc5d5df96f49274559638343719f97335f82e74604fa17ff5be5c4 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: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 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_arm64.deb Size: 130442 MD5sum: 833744d56f6fca55e48c40925ad5a38e SHA1: 755374731473a7be396accd36e8b49168c5ce996 SHA256: 33d98dbb44ea161bb9ec119f452a88c4641b6d481d3e1d3cf16d809e1115c10b SHA512: 04893849257c84170f18b26c01c3877d128bc30af67e71a1028b8ff946761b3297897f9158249fbd205764234a07cbbe4858109e926203332f0da1f297911cec 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: arm64 Version: 0.1-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-base64enc_0.1-6-1.ca2604.1_arm64.deb Size: 29322 MD5sum: d15a576fbe0bc9369277346022995a65 SHA1: 53fdd098270085b35ff626f759118316f7b56258 SHA256: cf9fa713d3abc20e1daded311fc4edf3d6d59b369598d602faf25d0b752a29db SHA512: 699054e709d9469df84ba1f50635fc69b0d9ba03ae0b5b83a9be26deb574bc4e0e7eff3c87c18f405a5422599be2f069b8f45d17ba110133a3e2a4c88301c1c0 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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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: arm64 Version: 0.25-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2282 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2035186 MD5sum: d7e4edee1b5cf7a69c0d44cb94e8f963 SHA1: 6cbfc0dd3abececeafd6a09ad2073c0c141bb3ee SHA256: 7353979bfaff51865555f921b48a12a3225387c7324b053235d2c6137234dde9 SHA512: b801e6f9dbc4f4567dd5425273396635112cef1fc0ceac1f9d5c85575d34f5cf49de5916ca67f1eca89595d1f02d4721306379d084296f8a3de9b443f31b0181 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: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 509 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 259360 MD5sum: c3166fa3f56f7dc48e87109c5cc42139 SHA1: 39e1f7432f5c7a89a2eade31547fc5b6b3004682 SHA256: ef93d2a1449873b74b341d70579bdfb6c0e3f04646abfeb5e7fbc72ebd2320bf SHA512: aeb402585afaf5da496c73faf5069c29d4ccca2d171daa91534eb82dfd05cf1d546cecf4d70b05c0f1bfb88e475fee026ed53cb8357d041285d45d0566f368cf 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) . 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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: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 42332 MD5sum: 4b11180876e2d608a2026e3e8f16dd43 SHA1: 4a209dcbe7e51fbd42ad6aa47763bad30378c3d7 SHA256: b12c25736f116effcbb064fe6946ac1ea43904f766d858fbad95b2b8d9447f02 SHA512: 28cb04b58b5fec7ab1cf2eb8958c3636352552fea6bb26ff64f645662efbe2b4fef70b105c6c084e96d985a28d59db9872e63bc30d7c4dc8f49edc20b0f54d27 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: arm64 Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 374 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 155808 MD5sum: 6be8d08cf04af9054d79a83c666c777c SHA1: b28f79d50cf4ceed6bb92d2a2589421794d3330d SHA256: 143f6a6a5a02ff5ee9711b448201c46b064bbcfebbf6707fd4f9e20f1e54a64c SHA512: 937431480c7cba32d4bce1702349e7229576347127e0b1bafdbe78e69334f40089e3cda7eaace9ef4b7a4553804f83c89901382770a7a9c3956f72a5f4247222 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: arm64 Version: 1.2.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7677 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 3115360 MD5sum: cd8ee7cf2a4081250e8c48ecde9cf7bc SHA1: 015b72719e41d3b3f51148c0242ebf340e4fc60c SHA256: ff6494ce370b9bc422ab530a1af0c8196e6df784ba2ba57f7b8cf46ea358ad00 SHA512: 590acb773d53b9fd5992ea36defe7ad77eebcb163d63646c4aab01b39cf77897e06d8792cd55b16d7108801464cf58a79e9d4beac5380da219b21abe48340b55 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 ). 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Package: r-cran-bayescount Architecture: arm64 Version: 0.9.99-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 618 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-runjags, r-cran-rjags, r-cran-coda Filename: pool/dists/resolute/main/r-cran-bayescount_0.9.99-9-1.ca2604.1_arm64.deb Size: 257138 MD5sum: e48bf205be3789d0d17293650c8ecbcc SHA1: 1c54d0cf4a8375aba07a1927d728617cb04681b4 SHA256: ffb907d962b0c5f7629bc16aefbbddff6652b7b2e36eb3e7690e3bead22377b2 SHA512: 3ee789f44be9b826a59502da52fd0956678aaa1b92e0d51eb8618aebbeda74cb2495b8b2aa15e4b4c83ddb8ae59acee6875290e373c3d343c96f7e21f9a0106f Homepage: https://cran.r-project.org/package=bayescount Description: CRAN Package 'bayescount' (Power Calculations and Bayesian Analysis of Count Distributionsand FECRT Data using MCMC) A set of functions to allow analysis of count data (such as faecal egg count data) using Bayesian MCMC methods. Returns information on the possible values for mean count, coefficient of variation and zero inflation (true prevalence) present in the data. A complete faecal egg count reduction test (FECRT) model is implemented, which returns inference on the true efficacy of the drug from the pre- and post-treatment data provided, using non-parametric bootstrapping as well as using Bayesian MCMC. Functions to perform power analyses for faecal egg counts (including FECRT) are also provided. 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Package: r-cran-bayesdecon Architecture: arm64 Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1290 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-msm, r-cran-corpcor, r-cran-ks, r-cran-mvtnorm, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-gridextra, r-cran-foreach, r-cran-doparallel, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-bayesdecon_0.1.6-1.ca2604.1_arm64.deb Size: 633924 MD5sum: fbb5868af78c8d5f5a5861893b253c22 SHA1: c3dd75901633eebfff02e4a39d01016be5f60dbc SHA256: 87d4a5fb12f85d7c0821e315eef23d4214c881f3217a01526c8da023e9eeb961 SHA512: 541d6ec995fdecb1eebe2eb7820628217c534b06505624677fbf8ac5dbc62ff527df6a19bd503c25ef5b8d7adf9e19d3d653342ad1c8bbf978ebe402699c6ca5 Homepage: https://cran.r-project.org/package=BayesDecon Description: CRAN Package 'BayesDecon' (Density Deconvolution Using Bayesian Semiparametric Methods) Estimates the density of a variable in a measurement error setup, potentially with an excess of zero values. 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Package: r-cran-bayesdp Architecture: arm64 Version: 1.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3091 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_arm64.deb Size: 1507962 MD5sum: 1ac5a211a2c03243e2805e696388ee6e SHA1: 9f33611c76d34e42ae2f420a7277d29b62afe80b SHA256: cf26c1f92161c7ff4d98cbb8305cc51eff4aa4e4222ef7e1d217a38a6571b46e SHA512: a83931d027e73408c85153565efc77749ad609b1b67004db74b691319a13e2f998d89cb4304647e45d2f4670aa8f808af8e151ea1445b7f9bdc3fc1b22d8c99f 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. Package: r-cran-bayeseo Architecture: arm64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4497 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-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-rcpp, r-cran-stars, r-cran-terra, r-cran-tibble, r-cran-tidyr, r-cran-tmap, r-cran-yaml, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bayeseo_0.2.2-1.ca2604.1_arm64.deb Size: 2295162 MD5sum: 6976103572d43002e974d7dd622179ae SHA1: a8c253db5f498dbfb165e405885323345831f005 SHA256: 1dc5e44410fe08b340825b949f860e61b31cfabdb9ffa4addf4fb46423a8adf8 SHA512: a9dd5ba4663a72b8502b568742a3b2135aa88b7f4fbed65d756ecede6f0dd188c34ac7444633cd6fbe33590145a1dcaad8ca1f49d58cd47f7603a657642854fd Homepage: https://cran.r-project.org/package=bayesEO Description: CRAN Package 'bayesEO' (Bayesian Smoothing of Remote Sensing Image Classification) A Bayesian smoothing method for post-processing of remote sensing image classification which refines the labelling in a classified image in order to enhance its classification accuracy. Combines pixel-based classification methods with a spatial post-processing method to remove outliers and misclassified pixels. Package: r-cran-bayesess Architecture: arm64 Version: 0.1.19-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mcmcpack, r-cran-laplacesdemon, r-cran-rcpp, r-cran-dfcrm, r-cran-matrixmodels, r-cran-mass, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bayesess_0.1.19-1.ca2604.1_arm64.deb Size: 78914 MD5sum: 9fb02d6337ecdb691217a479987eab3e SHA1: 478dcdbbe885bb0d95a5c64f084742452484610c SHA256: d5edee061d0117519b7e105ce4f54c8ed26ddf39cd7d45ac6f9ddc4303fd0b6b SHA512: e68492249347e15baf9a1a03938bd18af48ca2451ed82247c771816f45b4c605146d7d04bceb5f6143cdeb82a98d225c34f169b42892ffced66f81d8daa66661 Homepage: https://cran.r-project.org/package=BayesESS Description: CRAN Package 'BayesESS' (Determining Effective Sample Size) Determines effective sample size of a parametric prior distribution in Bayesian models. For a web-based Shiny application related to this package, see . Package: r-cran-bayesfactor Architecture: arm64 Version: 0.9.12-4.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12653 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-matrix, r-cran-pbapply, r-cran-mvtnorm, r-cran-stringr, r-cran-matrixmodels, r-cran-rcpp, r-cran-hypergeo, r-cran-rcppeigen Suggests: r-cran-domc, r-cran-foreach, r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-arm, r-cran-lme4, r-cran-xtable, r-cran-languager Filename: pool/dists/resolute/main/r-cran-bayesfactor_0.9.12-4.8-1.ca2604.1_arm64.deb Size: 6640072 MD5sum: 0081468da7a8f03e1fc26517ac5cddce SHA1: c39893f2a507acac0e95735241e3dbcf634ada13 SHA256: 23371badfd31bf4141d1e978a5efb81a1d24ad52f29ed06d7ee999af414de4af SHA512: 32451f877336d3fdd3c4b28eafcb8ea9991bb35a002fcd6a5effbf06b2a2b9b2222451eaecfb87de27b3bd109dd296bc487ba1a90cdf31832b9080d21f940e18 Homepage: https://cran.r-project.org/package=BayesFactor Description: CRAN Package 'BayesFactor' (Computation of Bayes Factors for Common Designs) A suite of functions for computing various Bayes factors for simple designs, including contingency tables, one- and two-sample designs, one-way designs, general ANOVA designs, and linear regression. Package: r-cran-bayesfm Architecture: arm64 Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 305 Depends: libc6 (>= 2.38), 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_arm64.deb Size: 199072 MD5sum: fd34ab1f85f992949a64323ea648cd5a SHA1: e1656422ab89093afc8789cbfb26aa35054187bc SHA256: 724a561448e368ca78150450821ab1303c4d61613dc260706acc27ae0e7d4df7 SHA512: 39dc3fc6e0b781110c075563f09585e4a24c67731f615b9d1f8b85745812ab3f8464fa6a4312b89bf079013aa29f0a979ff9a7fe5ce5c32098413636af364b22 Homepage: https://cran.r-project.org/package=BayesFM Description: CRAN Package 'BayesFM' (Bayesian Inference for Factor Modeling) Collection of procedures to perform Bayesian analysis on a variety of factor models. Currently, it includes: "Bayesian Exploratory Factor Analysis" (befa) from G. Conti, S. Frühwirth-Schnatter, J.J. Heckman, R. Piatek (2014) , an approach to dedicated factor analysis with stochastic search on the structure of the factor loading matrix. The number of latent factors, as well as the allocation of the manifest variables to the factors, are not fixed a priori but determined during MCMC sampling. Package: r-cran-bayesfmri Architecture: arm64 Version: 0.11.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1008 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_arm64.deb Size: 702650 MD5sum: 1747816e884cb46a78bc02d5093912cc SHA1: 9db197bdbeba96d663f4388d4602b3546ce6d20d SHA256: 6b96e28fc8c91dc751edbca16340bacb8c302f2f0a74e889b552478dd92f6752 SHA512: fd838d6338f8ab06be24190f87d4f4f3fd83db266896ccfb4a6239750ed74e38d25a53b21099c88c7e51fedbf60b81552ae51a28f242a17913b4f0e559eed865 Homepage: https://cran.r-project.org/package=BayesfMRI Description: CRAN Package 'BayesfMRI' (Spatial Bayesian Methods for Task Functional MRI Studies) Performs a spatial Bayesian general linear model (GLM) for task functional magnetic resonance imaging (fMRI) data on the cortical surface. Additional models include group analysis and inference to detect thresholded areas of activation. Includes direct support for the 'CIFTI' neuroimaging file format. For more information see A. F. Mejia, Y. R. Yue, D. Bolin, F. Lindgren, M. A. Lindquist (2020) and D. Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022) . Package: r-cran-bayesforecast Architecture: arm64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8258 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_arm64.deb Size: 3360134 MD5sum: f23d516e184bbbae37bdcdbf0792a47f SHA1: 60449f81240b2b15c270de59d58a879358539cbb SHA256: 70cb1b723e6b3aa679f5f21079281ad7cbe79335b969abc9fe50655f45892be0 SHA512: a93a837a3cbc16b741c98d7b2f410251ead5407f1aa2a713d8ab71698d665a621a4c862299bf0539fd481e0cd6addc2d54462b0efafa922a1df746a7011ed48a Homepage: https://cran.r-project.org/package=bayesforecast Description: CRAN Package 'bayesforecast' (Bayesian Time Series Modeling with Stan) Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) ; Carpenter et al. (2017) . Package: r-cran-bayesgarch Architecture: arm64 Version: 2.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 173 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_arm64.deb Size: 73858 MD5sum: 41e4747ff1e25e0ee487438aff14c15b SHA1: 8aa754a4402800d27530f790df2c13525c049bbb SHA256: 42faf033e21d1057960267fc50ca8688cb8afd3eda26cb316e7ecab55b388d2d SHA512: e080f084d5bb6a1287b1da6f1b27bfc54829a8536b46e0f9f959339cf57182f66b86cb4a874ee17df9409307e91fdb68b853ecd529278ca02598c6ee25da0912 Homepage: https://cran.r-project.org/package=bayesGARCH Description: CRAN Package 'bayesGARCH' (Bayesian Estimation of the GARCH(1,1) Model with Student-tInnovations) Provides the bayesGARCH() function which performs the Bayesian estimation of the GARCH(1,1) model with Student's t innovations as described in Ardia (2008) . Package: r-cran-bayesgmed Architecture: arm64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6592 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_arm64.deb Size: 1294810 MD5sum: 7c21f781392f3db5a99f12e8920a59c4 SHA1: fe55def8211b72a1abdad9bb9bc226da8d82cec3 SHA256: 867ca48d56df6aa83969a59bb2b4b9ae40c0509f197d8a979369bad4570cbd5e SHA512: e497bd0a2147e108b8529a082228c899f495771a20ce098c4b1a6549654441dccd2ad453a6034f343bc87030508313916688a3bfcbeb209d303998a3a7155668 Homepage: https://cran.r-project.org/package=BayesGmed Description: CRAN Package 'BayesGmed' (Bayesian Causal Mediation Analysis using 'Stan') Performs parametric mediation analysis using the Bayesian g-formula approach for binary and continuous outcomes. The methodology is based on Comment (2018) and a demonstration of its application can be found at Yimer et al. (2022) . Package: r-cran-bayesgp Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1942 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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_arm64.deb Size: 1126122 MD5sum: 30e08eee242b118e3d347a2239f2cedd SHA1: b10aef96ca1924bf16f45eff7375002c67b6a261 SHA256: 70760439bf831f9c7692c97659f69b44669c7b6b4c87431247dd8cae4150513a SHA512: c9b1ffd58504d598885740d6657ebed941b5a5c2e279e3ce2a74fc71dd7aaa92e2bc64ee1282ec88ee6f192d1f1df66eeb9da9cef6667f8ce8578ff90df2be9c Homepage: https://cran.r-project.org/package=BayesGP Description: CRAN Package 'BayesGP' (Efficient Implementation of Gaussian Process in BayesianHierarchical Models) Implements Bayesian hierarchical models with flexible Gaussian process priors, focusing on Extended Latent Gaussian Models and incorporating various Gaussian process priors for Bayesian smoothing. Computations leverage finite element approximations and adaptive quadrature for efficient inference. Methods are detailed in Zhang, Stringer, Brown, and Stafford (2023) ; Zhang, Stringer, Brown, and Stafford (2024) ; Zhang, Brown, and Stafford (2023) ; and Stringer, Brown, and Stafford (2021) . Package: r-cran-bayesgpfit Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-bayesgpfit_1.1.0-1.ca2604.1_arm64.deb Size: 96006 MD5sum: 728b58146c1396cc9519a104ee75c7c4 SHA1: d43b642f56a95facb1585d86a66ac7cfff944638 SHA256: 7bfbda6947b463701f4b939f8d1bfaa30d08735e887bcb8f3bdd538242e618a9 SHA512: 6765806b2717189a28d60e50b181ef6e87e1f392185289751e675af7f2c7e44ad81771beb81256dd9916f1a0d4def5057b4e4e57b395a908f24d5b45162078dd Homepage: https://cran.r-project.org/package=BayesGPfit Description: CRAN Package 'BayesGPfit' (Fast Bayesian Gaussian Process Regression Fitting) Bayesian inferences on nonparametric regression via Gaussian Processes with a modified exponential square kernel using a basis expansion approach. Package: r-cran-bayesgrowth Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3885 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_arm64.deb Size: 1700908 MD5sum: b7eb7cc432853eb70fbc53441e05fdb6 SHA1: 9f7730d7526648ce5690bb3892316c4f4d9f4f7a SHA256: 3de784470f761bf964b372bc4ba684e52df7c933dc1b09c78ab442bb7665244e SHA512: 4c2c443cc5845fd3a87c8e253c5b96f4849e2553dfa2d4aa139c2c28218994ddc49384c307d210b4b5f716c1094649fa4964e8f19de372626e7124c04fd1ec3f Homepage: https://cran.r-project.org/package=BayesGrowth Description: CRAN Package 'BayesGrowth' (Estimate Fish Growth Using MCMC Analysis) Estimate fish length-at-age models using MCMC analysis with 'rstan' models. This package allows a multimodel approach to growth fitting to be applied to length-at-age data and is supported by further analyses to determine model selection and result presentation. The core methods of this package are presented in Smart and Grammer (2021) "Modernising fish and shark growth curves with Bayesian length-at-age models". PLOS ONE 16(2): e0246734 . Package: r-cran-bayesianetas Architecture: arm64 Version: 2.0.0-1.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_arm64.deb Size: 110434 MD5sum: 1878ae6e49b73037e9dbe1afd6f8576b SHA1: 6a40babea618faed446c0fb09425ac1d2551afdb SHA256: 7a26e3ce6691af851d0ac62f6f65462a74fb306bb692aaceb698a42a568b298f SHA512: d6b85884252b448e6da33a4a867ccd1c850d7ad6e39ff1c81d4efeb590703a750caaccb47e501928b0dff58110cc682a814e2a625aafa69e5fd06c61f8bc9313 Homepage: https://cran.r-project.org/package=bayesianETAS Description: CRAN Package 'bayesianETAS' (Bayesian Estimation of the Temporal and Spatio-Temporal ETASModels for Earthquake Occurrences) The Epidemic Type Aftershock Sequence (ETAS) model is widely used for modelling and forecasting earthquake occurrences. This package implements Bayesian estimation routines for both the temporal and spatial ETAS model, allowing samples to be drawn from the full posterior distribution of the model parameters given an earthquake catalogue. The methods are described in Ross (2021) "Bayesian Estimation of the ETAS Model for Earthquake Occurrences" . Package: r-cran-bayesianlasso Architecture: arm64 Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1761 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1096532 MD5sum: 1b283b7d8b66b3516d6297af4b9b3a9b SHA1: d789a049ffef178aeb1687e3439c8cfe5cafafdf SHA256: 954d96ddd5be20a2fc1f2ea941b6d09be82c5f0acf268bc434652210511c55a2 SHA512: bb036552a9ea7059f84f02b8e517b38d6d047866ef4c88fe245a21f8677bf39c63c22356dc7d55a6ea7458351f0874e6a2e60ce4ca3fd684b6419a1ac6d1e621 Homepage: https://cran.r-project.org/package=BayesianLasso Description: CRAN Package 'BayesianLasso' (Bayesian Lasso Regression and Tools for the Lasso Distribution) Implements Bayesian Lasso regression using efficient Gibbs sampling algorithms, including modified versions of the Hans and Park Casella (PC) samplers. Includes functions for working with the Lasso distribution, such as its density, cumulative distribution, quantile, and random generation functions, along with moment calculations. Also includes a function to compute the Mills ratio. Designed for sparse linear models and suitable for high-dimensional regression problems. Package: r-cran-bayesianplatformdesigntimetrend Architecture: arm64 Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6700 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_arm64.deb Size: 4166176 MD5sum: 65b2b3c20c4f15b5c2b590057abd576b SHA1: a4e3d7bd1c2477a0b7304f3c9ea6a4bcfb646c10 SHA256: a45bc6003987baa4d90eb892679b3e3fb9910e109f8b7189e4d3b9ef9d3cccbb SHA512: ded1340e2df3f0646a0f162936d04337a39907d2d0b0fca010b2f8fb0d7914bf942f5695901165e4a708679a8b9eecc3264b344f5e02ff015a491284369cd062 Homepage: https://cran.r-project.org/package=BayesianPlatformDesignTimeTrend Description: CRAN Package 'BayesianPlatformDesignTimeTrend' (Simulate and Analyse Bayesian Platform Trial with Time Trend) Simulating the sequential multi-arm multi-stage or platform trial with Bayesian approach using the 'rstan' package, which provides the R interface for the Stan. This package supports fixed ratio and Bayesian adaptive randomization approaches for randomization. Additionally, it allows for the study of time trend problems in platform trials. There are demos available for a multi-arm multi-stage trial with two different null scenarios, as well as for Bayesian trial cutoff screening. The Bayesian adaptive randomisation approaches are described in: Trippa et al. (2012) and Wathen et al. (2017) . The randomisation algorithm is described in: Zhao W . The analysis methods of time trend effect in platform trial are described in: Saville et al. (2022) and Bofill Roig et al. (2022) . Package: r-cran-bayesiantools Architecture: arm64 Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1345 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_arm64.deb Size: 924696 MD5sum: fc636ee84c7154f6d59fd574233d5103 SHA1: 203bfc5c8197dfeb682ccc064e8db7eb771fa8df SHA256: b846cf5af140d8bc75fe57c9f7a412ce0316202a425b1be205b76cba7f6102bd SHA512: 4daa0c7f6d26d15697303a6b16b80afa90478366068f951bf457766723cc5e755437358c611faedd40e78af699930d446e1d676e2708d8e2de1690c29fa35e90 Homepage: https://cran.r-project.org/package=BayesianTools Description: CRAN Package 'BayesianTools' (General-Purpose MCMC and SMC Samplers and Tools for BayesianStatistics) General-purpose MCMC and SMC samplers, as well as plots and diagnostic functions for Bayesian statistics, with a particular focus on calibrating complex system models. Implemented samplers include various Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential Monte Carlo (SMC) particle filter. 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Implements state-of-the-art shrinkage priors following Gruber & Kastner (2025) . Efficient equation-per-equation estimation following Kastner & Huber (2020) and Carrerio et al. (2021) . Package: r-cran-bayesimages Architecture: arm64 Version: 0.7-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3500 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_arm64.deb Size: 3049176 MD5sum: e408fca5ff6d7114e3c9e1d513f98ae5 SHA1: c4a44102763099f3cde83bac57f1e47b15d572c1 SHA256: 21f1c8df2dcd845fb985193907027cc0c89ad17c173fef04af21bf59d86a09c1 SHA512: eceb65f9d78023215e622179a64f6189659e4a671f032614d7fceb02c7b704a735ce6405bd31ca4ee0905a0f08d9d4179f9a63e79257ffac66ad33d8f08c07a6 Homepage: https://cran.r-project.org/package=bayesImageS Description: CRAN Package 'bayesImageS' (Bayesian Methods for Image Segmentation using a Potts Model) Various algorithms for segmentation of 2D and 3D images, such as computed tomography and satellite remote sensing. This package implements Bayesian image analysis using the hidden Potts model with external field prior of Moores et al. (2015) . Latent labels are sampled using chequerboard updating or Swendsen-Wang. Algorithms for the smoothing parameter include pseudolikelihood, path sampling, the exchange algorithm, approximate Bayesian computation (ABC-MCMC and ABC-SMC), and the parametric functional approximate Bayesian (PFAB) algorithm. Refer to Moores, Pettitt & Mengersen (2020) for an overview and also to and for further details of specific algorithms. Package: r-cran-bayeslife Architecture: arm64 Version: 5.3-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2727 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2384548 MD5sum: d25c4e8f215714386dce20a44724405f SHA1: dd471438e554e91e0cff02cd24565539914b0aff SHA256: 1371526485d0504222a0a9fba193ee211b4f385668f21acb0b23803454609243 SHA512: 896780bce7ebcf7dd3c36706ce4f98f7d34857563c94be763967902a2ede1c096354fd32add3ffc99e68c9afaf3adf81ee22d5c47c5b6610850597291f116a5c 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: arm64 Version: 0.0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 16833 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_arm64.deb Size: 2838148 MD5sum: 72350446abfcff274f42782d336818a1 SHA1: 50611c2bc71299af67df84e827ee79f71cd13b72 SHA256: db95a095fbb79029385e21b50bcf2c4e47b0317f77a5e296cef1a626527cea95 SHA512: 68e371d9f0dba3bca625296df5953a5814074a53a1542f1fd92f780ed984a6e2c974c7609d962511852a60c85d9af8785045b6044d8bdc8d243991486bbec82a 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: arm64 Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 908 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_arm64.deb Size: 312502 MD5sum: 1c222180e3c3e78ecb72e32a98f54c91 SHA1: 43173379c41f397339a2d7fcb468d1ec0f72755b SHA256: 1356dc9e8b7b7a674fd5af56b7c521ca19625be2b5804a14329aa48155d47a27 SHA512: 7a26fd88541944cf2b32936753aa6f2fecfa519ed92b04ef265c5a2b1954040b3b10442ba2ec2f5a199c8db01d86143ceba8aa3da2d0b3bb1ef3ab79237a44bc 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: arm64 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_arm64.deb Size: 272078 MD5sum: a6ffa3e1a54f491cd4961cb185cb0e4f SHA1: 58a0e700bd207928f944ad6baa2d748d619b9500 SHA256: ef20a811c3c295b0df10ee1d5ac5f11ce12a21b7f79c3ad2e80b1b1dd7536747 SHA512: 3eec9ab6e3d1f881e54ef0cef9e20bf330520ac385c10eed61315cb713f59d8268f8077fd8a465a971d565267ab6e4996ea61c1bef87d79819213d7ef22b96ee 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: arm64 Version: 2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bayeslogit_2.3-1.ca2604.1_arm64.deb Size: 98072 MD5sum: 0fbae522a3e5aa6f2689b2b0d59de476 SHA1: 111f8a08473f827a652900283381982c777d17aa SHA256: 834cf095a05fcbc0b31475432ff4633df951eb45834b512ef138f9f110d23c58 SHA512: cc8c2b943d1563d52ef982798522be89b6142069a8c2f91ed16fb00fff7d7d2ab4969bae7148460c507cbd11ff9f5ce932fbfff40cd4f193b3a5b7ae5c59ed18 Homepage: https://cran.r-project.org/package=BayesLogit Description: CRAN Package 'BayesLogit' (PolyaGamma Sampling) Tools for sampling from the PolyaGamma distribution based on Polson, Scott, and Windle (2013) . Useful for logistic regression. Package: r-cran-bayesm Architecture: arm64 Version: 3.1-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5717 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_arm64.deb Size: 2588240 MD5sum: 62219bfa6053291919afce64b8d35441 SHA1: c574571fd180e8eddfab58eac4d57bb2f8939472 SHA256: af16c121cc577eb656199026dd3f48f0fb005a66acfe6a057d7fe69dde104ef1 SHA512: 4912319cdf22f07cfde6db4e3025a90c1e72f1a8f324f81672d0c245503458ed2549a408dba09bdf6e48c858cfd65eb054293781ea89ce56f538147b08d4a599 Homepage: https://cran.r-project.org/package=bayesm Description: CRAN Package 'bayesm' (Bayesian Inference for Marketing/Micro-Econometrics) Covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var), Bayes Seemingly Unrelated Regression (SUR), Binary and Ordinal Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP), Multivariate Probit, Negative Binomial (Poisson) Regression, Multivariate Mixtures of Normals (including clustering), Dirichlet Process Prior Density Estimation with normal base, Hierarchical Linear Models with normal prior and covariates, Hierarchical Linear Models with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates, Hierarchical Negative Binomial Regression Models, Bayesian analysis of choice-based conjoint data, Bayesian treatment of linear instrumental variables models, Analysis of Multivariate Ordinal survey data with scale usage heterogeneity (as in Rossi et al, JASA (01)), Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see Jiang, Manchanda, Rossi 2009) For further reference, consult our book, Bayesian Statistics and Marketing by Rossi, Allenby and McCulloch (Wiley second edition 2024) and Bayesian Non- and Semi-Parametric Methods and Applications (Princeton U Press 2014). Package: r-cran-bayesmallows Architecture: arm64 Version: 2.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4145 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 2752206 MD5sum: 2335da693e20d10301c8b6064afbdc26 SHA1: c2c201d2542c22d19976bc66f91f3633573118aa SHA256: 1ce0434a6688207960075d67acd53492cf3b5398beb174dc8ab64dd88a6c5804 SHA512: 4139c4c25c167f0491008cfd7c47cb52dd992a3f600a0fdba705439a69ab8e043d6a64d54066ad52b6ac142d513cbc2cfae10fcfe811419e9687412fd4afef8a Homepage: https://cran.r-project.org/package=BayesMallows Description: CRAN Package 'BayesMallows' (Bayesian Preference Learning with the Mallows Rank Model) An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 ; Crispino et al., Annals of Applied Statistics, 2019 ; Sorensen et al., R Journal, 2020 ; Stein, PhD Thesis, 2023 ). Both Metropolis-Hastings and sequential Monte Carlo algorithms for estimating the models are available. Cayley, footrule, Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the posterior distributions of parameters are provided. The package also provides functions for estimating the partition function (normalizing constant) of the Mallows rank model, both with the importance sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm (Mukherjee, Annals of Statistics, 2016 ). Package: r-cran-bayesmallowssmc2 Architecture: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1163 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 459978 MD5sum: 61640d1719d5ca5fcb1cd024aa63e844 SHA1: 24cecbbeb9e051bb984e8e27937eb3501ba815d9 SHA256: b4e801250bc94bc15f811da93f21429c7a41f7e1f359a50cff4789985b6caa51 SHA512: c5cdffaf7877347904346246fa43c1feda4f3400b56305b43d78123b93c4d7243ae7d78fac1cb3281b93483a09a6e0793cdc0ff07223cce583aa29bd35edbfe5 Homepage: https://cran.r-project.org/package=BayesMallowsSMC2 Description: CRAN Package 'BayesMallowsSMC2' (Nested Sequential Monte Carlo for the Bayesian Mallows Model) Provides nested sequential Monte Carlo algorithms for performing sequential inference in the Bayesian Mallows model, which is a widely used probability model for rank and preference data. The package implements the SMC2 (Sequential Monte Carlo Squared) algorithm for handling sequentially arriving rankings and pairwise preferences, including support for complete rankings, partial rankings, and pairwise comparisons. The methods are based on Sorensen (2025) . 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Package: r-cran-bayesmove Architecture: arm64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1475 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_arm64.deb Size: 1340502 MD5sum: ef887fe792694d81d592d14f77c52bd2 SHA1: 26985d61306eeaffa7cc7c41980b0fbac150f241 SHA256: 26df2d01f345bb48f41d447ecfce0e718af67b45f8c17f480936171ace0ea1b4 SHA512: e9107287ec602b7945ae268fc504063c73e1c2186b7ea9decf895c713fc6607f8ee5a4169511803710b94c2585a9830dfd47cdc29226f6922387106b25a10567 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) . Package: r-cran-bayespim Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 550 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-coda, r-cran-rcpp, r-cran-mvtnorm, r-cran-mass, r-cran-ggamma, r-cran-doparallel, r-cran-foreach, r-cran-actuar Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-bayespim_1.0.1-1.ca2604.1_arm64.deb Size: 304304 MD5sum: 46c7ab4463ea4f0a46ddae5628fa100f SHA1: bff836d4ca88c71cdb9abc49dab869f674ea1bbb SHA256: dfa019496d6422774a81c8e814b22549cf7facdaf01916cff400e0507038cd7a SHA512: f602d2af8e9873e8e0e32b9fb20d31cf41adf7db4a5ad64af206e571f020e7b4b56a1ebbb39a75aa3959055f9b36cce79f190117d20c4a38ca0fbd027ff82573 Homepage: https://cran.r-project.org/package=BayesPIM Description: CRAN Package 'BayesPIM' (Bayesian Prevalence-Incidence Mixture Model) Models time-to-event data from interval-censored screening studies. It accounts for latent prevalence at baseline and incorporates misclassification due to imperfect test sensitivity. For usage details, see the package vignette "BayesPIM_intro". Further details can be found in Klausch, Lissenberg-Witte and Coupé (2026) . 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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. 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The package can be also used for subnational population projections. Package: r-cran-bayespower Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3425 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 1136898 MD5sum: b995d23f7d9570efc27333a3170a213f SHA1: 33890fdae961eba1c5f3aad0403ca4694000f533 SHA256: ebaabca581247b80cc918a5af79a3a7ddf34ae58a35b612c30265bc30e2dd3c5 SHA512: 376ef5cd2ad88efc6c45dcde56e9857aa53b8264f44606ba8b8b570cdc4b20720e31c17145a3abb7051e0565a67558d6ae9dede704b242a476e7f240258b011c Homepage: https://cran.r-project.org/package=BayesPower Description: CRAN Package 'BayesPower' (Sample Size and Power Calculation for Bayesian Testing withBayes Factor) The goal of 'BayesPower' is to provide tools for Bayesian sample size determination and power analysis across a range of common hypothesis testing scenarios using Bayes factors. The main function, BayesPower_BayesFactor(), launches an interactive 'shiny' application for performing these analyses. The application also provides command-line code for reproducibility. Details of the methods are described in the tutorial by Wong, Pawel, and Tendeiro (2025) . Package: r-cran-bayesppd Architecture: arm64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 933 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_arm64.deb Size: 395224 MD5sum: 4c8a75e013f067319ebb9d3de6eea7f6 SHA1: ecd074ebf86af4150213ba4f9e28e05f132ef969 SHA256: 28a771123295e051e769d885193c3e3dedd466bea4097b553f7367e8c44c354b SHA512: bbbb09b515e962489a3e2719cd00f41fd0e830eaafb379852a05cdbada9ff53794f12215aff13f70682b6f69afbc5171db545b16e6483cac62e534b4367ef35b Homepage: https://cran.r-project.org/package=BayesPPD Description: CRAN Package 'BayesPPD' (Bayesian Power Prior Design) Bayesian power/type I error calculation and model fitting using the power prior and the normalized power prior for generalized linear models. Detailed examples of applying the package are available at . Models for time-to-event outcomes are implemented in the R package 'BayesPPDSurv'. The Bayesian clinical trial design methodology is described in Chen et al. (2011) , and Psioda and Ibrahim (2019) . The normalized power prior is described in Duan et al. (2006) and Ibrahim et al. (2015) . Package: r-cran-bayesppdsurv Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 188628 MD5sum: e75f49cb197aa4bb452cd366337c0536 SHA1: dbe12647506528de255df610f454d5a45b466062 SHA256: 5ad5c9014a5e251f05211393023fb5682372a2e03546b3047850914387b02e6c SHA512: 77003fc7e07dc1d0476a92b4ca42261cd02227c39c7870b895dd7487e6713116091e036331e6ba28b08c78733999f20a8802ed46ebbebaf388c5f6929d3df0bf Homepage: https://cran.r-project.org/package=BayesPPDSurv Description: CRAN Package 'BayesPPDSurv' (Bayesian Power Prior Design for Survival Data) Bayesian power/type I error calculation and model fitting using the power prior and the normalized power prior for proportional hazards models with piecewise constant hazard. The methodology and examples of applying the package are detailed in . The Bayesian clinical trial design methodology is described in Chen et al. (2011) , and Psioda and Ibrahim (2019) . The proportional hazards model with piecewise constant hazard is detailed in Ibrahim et al. (2001) . Package: r-cran-bayesproject Architecture: arm64 Version: 1.0-1.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_arm64.deb Size: 112286 MD5sum: be9e56f2f5bfffab12ae9649d409325e SHA1: 34268d189ade25d1acc58a361d42cdc1bc2cac96 SHA256: 672b783ab62c1a1a494c808080454f900b59e5a7823d30104e10bb74d15e9131 SHA512: 834660ad2fb59f98fc6a20399925c7b5ad6c8d9d8304cbb929b5884a537ab7f0a79d9ccdc056b3fa6433675801c160ccf342bfe0b9e9ff276f0a3e7334ac7d87 Homepage: https://cran.r-project.org/package=BayesProject Description: CRAN Package 'BayesProject' (Fast Projection Direction for Multivariate Changepoint Detection) Implementations in 'cpp' of the BayesProject algorithm (see G. Hahn, P. Fearnhead, I.A. Eckley (2020) ) which implements a fast approach to compute a projection direction for multivariate changepoint detection, as well as the sum-cusum and max-cusum methods, and a wild binary segmentation wrapper for all algorithms. Package: r-cran-bayesqr Architecture: arm64 Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bayesqr_2.4-1.ca2604.1_arm64.deb Size: 98724 MD5sum: cc7b0991afae778ce347a80f755493c0 SHA1: 25f04fb65ad0492f149e9c9863653c04c23696e9 SHA256: 16ebae24b5364a03d80ab5463961a839b5d21b70573310fb1a2df86ca8e9dda3 SHA512: b323c415970ffcbf988dc836cd5b1e93a5956d512e8369cb67e25e4656e9aaea89bd7a8cbdd010028d3baf1a57c94e1f789c3ad7e6f1e0b2b670634c3a3a6996 Homepage: https://cran.r-project.org/package=bayesQR Description: CRAN Package 'bayesQR' (Bayesian Quantile Regression) Bayesian quantile regression using the asymmetric Laplace distribution, both continuous as well as binary dependent variables are supported. The package consists of implementations of the methods of Yu & Moyeed (2001) , Benoit & Van den Poel (2012) and Al-Hamzawi, Yu & Benoit (2012) . To speed up the calculations, the Markov Chain Monte Carlo core of all algorithms is programmed in Fortran and called from R. Package: r-cran-bayesqrsurvey Architecture: arm64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 342390 MD5sum: 1de4d7ca0e204015193bc530e396f1d2 SHA1: 0635f4e9acb676af4ddac9b0de2589a06200f610 SHA256: 4edb1948ad17c8491091c01b4d6dc0d0e2f6a3b7e6697c1d30ef7cd7035367b8 SHA512: 9d5be04020e6c2ee6710cf72c1cb91436db94b449f5b66fa51ca88cd5c9d273f8635786c326e75f676c1f9611c2a0cb50696699eba6b4fc1d1fa08efd4303f5c Homepage: https://cran.r-project.org/package=bayesQRsurvey Description: CRAN Package 'bayesQRsurvey' (Bayesian Quantile Regression Models for Complex Survey DataAnalysis) Provides Bayesian quantile regression models for complex survey data under informative sampling using survey-weighted estimators. Both single- and multiple-output models are supported. To accelerate computation, all algorithms are implemented in 'C++' using 'Rcpp', 'RcppArmadillo', and 'RcppEigen', and are called from 'R'. See Nascimento and Gonçalves (2024) and Nascimento and Gonçalves (2025, in press) . Package: r-cran-bayesregdtr Architecture: arm64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 409 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 275916 MD5sum: e56551f765e0271bb28b06702e57f088 SHA1: c365fa4a53372de62934b7031212cedb76230cce SHA256: 1f97ba855c0342acfba76db92207cb6cdb42daa13c5482b2406e537a71d59939 SHA512: 78f350fead5b33b02d89446fdd701c74bff91f46c0a650733274ca9e65f8cc84325c3726cd79a6109dc25e97bf230bd8ebcec554df1a2cc7352266634253dd4f Homepage: https://cran.r-project.org/package=BayesRegDTR Description: CRAN Package 'BayesRegDTR' (Bayesian Regression for Dynamic Treatment Regimes) Methods to estimate optimal dynamic treatment regimes using Bayesian likelihood-based regression approach as described in Yu, W., & Bondell, H. D. (2023) Uses backward induction and dynamic programming theory for computing expected values. Offers options for future parallel computing. Package: r-cran-bayesrel Architecture: arm64 Version: 0.7.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 324462 MD5sum: 568e8777eeaddf1d4be97f6236b7e0d6 SHA1: 2d813a6cc5ee1b56f097cf3edca2329902a335dd SHA256: 60184a7ebdc5c389edc9bf7ecf60712ed6e2dcc16f717275e1437a8598c0c820 SHA512: de786b0c3dfc10a7ed6c59395ddcd7fab39d087009b38a65d3d3a50a45811aaac48c6bb6b05c49354303ceb757eb2207555b3234214f5992124a200a2d5fe6b8 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: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4533 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_arm64.deb Size: 1019126 MD5sum: 43109587f608cd91682c79b3b498dc95 SHA1: de268b815f51e884e4a8b7e4ae867650139e74e7 SHA256: 1245a00e1dec179a7fe0a4c18e24953857002bf447487deaa325a3ff30c86cc6 SHA512: c1e287d191d08876944865c59781be61c72afba3b08ec899b8f415415aa444ef1adbd2418c135c2b1389b41ee538295751aef978b093eedba8e3a80bd4d93081 Homepage: https://cran.r-project.org/package=BayesSenMC Description: CRAN Package 'BayesSenMC' (Different Models of Posterior Distributions of Adjusted OddsRatio) Generates different posterior distributions of adjusted odds ratio under different priors of sensitivity and specificity, and plots the models for comparison. It also provides estimations for the specifications of the models using diagnostics of exposure status with a non-linear mixed effects model. It implements the methods that are first proposed in and . Package: r-cran-bayesssm Architecture: arm64 Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-dplyr, r-cran-future, r-cran-future.apply, r-cran-rcpp, r-cran-checkmate Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-ggplot2, r-cran-tidyr, r-cran-extradistr, r-cran-rlang, r-cran-expm Filename: pool/dists/resolute/main/r-cran-bayesssm_0.7.1-1.ca2604.1_arm64.deb Size: 306406 MD5sum: 985f9c6f1b6cf547c4b040024bd3f6ef SHA1: 9ea45efbbf9daef7bc3edef717a535725cb4d321 SHA256: 95dcca98d3637522921f54cb3e88c6c8ba98e2cfe7f51a79059a6307d5b68fa3 SHA512: a87ae6b0a3a2a665f3ee928d218e9dd3dc2ba2767ab9bc5a140e2df5194d8630ef3215c16b8622439d211dae27dfaa8ba34fa767863101bfd39dbe9f33ca0306 Homepage: https://cran.r-project.org/package=bayesSSM Description: CRAN Package 'bayesSSM' (Bayesian Methods for State Space Models) Implements methods for Bayesian analysis of State Space Models. Includes implementations of the Particle Marginal Metropolis-Hastings algorithm described in Andrieu et al. (2010) and automatic tuning inspired by Pitt et al. (2012) and J. Dahlin and T. B. Schön (2019) . 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The sparse seemingly unrelated regression is described in Bottolo et al. (2021) , the software paper is in Zhao et al. (2021) , and the model with random effects is described in Zhao et al. (2024) . Package: r-cran-bayessurv Architecture: arm64 Version: 3.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1913 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_arm64.deb Size: 1290094 MD5sum: 288c3ffee7828a0f1f89e728fd1913d2 SHA1: c533257fe540070eaea97e31e02f55666a5b71a6 SHA256: 1348f45e37f0c04b7fa05ba487265b17c5550e4112a38ea8bab6a9a5b46b9ac2 SHA512: 17e39f8cc9dd6278a5c70930219ac6ac215b4c7a66d71facaad1b122a91d9371ddfd2e0dbeac10432b061f183bb5b4cd3a1998d1dba6b202b98825a46fa2c539 Homepage: https://cran.r-project.org/package=bayesSurv Description: CRAN Package 'bayesSurv' (Bayesian Survival Regression with Flexible Error and RandomEffects Distributions) Contains Bayesian implementations of the Mixed-Effects Accelerated Failure Time (MEAFT) models for censored data. Those can be not only right-censored but also interval-censored, doubly-interval-censored or misclassified interval-censored. The methods implemented in the package have been published in Komárek and Lesaffre (2006, Stat. Modelling) , Komárek, Lesaffre and Legrand (2007, Stat. in Medicine) , Komárek and Lesaffre (2007, Stat. Sinica) , Komárek and Lesaffre (2008, JASA) , García-Zattera, Jara and Komárek (2016, Biometrics) . Package: r-cran-bayessurvive Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1801 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-ggally, r-cran-mvtnorm, r-cran-survival, r-cran-riskregression, r-cran-rcpparmadillo, r-cran-testthat Suggests: r-cran-knitr, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-bayessurvive_0.1.0-1.ca2604.1_arm64.deb Size: 1250692 MD5sum: 4560d21e3f71d5febbb4812ca87ec460 SHA1: b9172a83be35e0fb1660dbfcbf6242af736fca1f SHA256: 7cf93be7c782111c4460f877f5a36a46def9839aa1a8175308a4963b561181f3 SHA512: a52ba20b2fca7fc68dbff4c573afcdaa5a7fc01da5fff712a5f12cfde18add277c23a3d47cd9b95a92269c142da9675c25c43e7f53de4295b17df1f98779c896 Homepage: https://cran.r-project.org/package=BayesSurvive Description: CRAN Package 'BayesSurvive' (Bayesian Survival Models for High-Dimensional Data) An implementation of Bayesian survival models with graph-structured selection priors for sparse identification of omics features predictive of survival (Madjar et al., 2021 ) and its extension to use a fixed graph via a Markov Random Field (MRF) prior for capturing known structure of omics features, e.g. disease-specific pathways from the Kyoto Encyclopedia of Genes and Genomes database (Hermansen et al., 2025 ). 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See Moriña D, Puig P, Navarro A. (2021) . 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Package: r-cran-bbmix Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1823 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_arm64.deb Size: 767716 MD5sum: d003784763f3998726e4b8033cb45c80 SHA1: b79316108f3d3f001744d667f93084947a65ef72 SHA256: 2d1ad827c278fd3e05bae72299ec814154f968fec3a3ac940e12f6f201412b9d SHA512: 7317faa94ef488fad81494d63c2bd07077610382be7dd3628e63dc72267818c9e01733077d9de43077d2859d1c62c1532a62bca65ea210d831f5553802603892 Homepage: https://cran.r-project.org/package=bbmix Description: CRAN Package 'bbmix' (Bayesian Model for Genotyping using RNA-Seq) The method models RNA-seq reads using a mixture of 3 beta-binomial distributions to generate posterior probabilities for genotyping bi-allelic single nucleotide polymorphisms. Elena Vigorito, Anne Barton, Costantino Pitzalis, Myles J. Lewis and Chris Wallace (2023) "BBmix: a Bayesian beta-binomial mixture model for accurate genotyping from RNA-sequencing." Package: r-cran-bbotk Architecture: arm64 Version: 1.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1853 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-paradox, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-lgr, r-cran-mlr3misc, r-cran-r6 Suggests: r-cran-adagio, r-cran-emoa, r-cran-gensa, r-cran-irace, r-cran-knitr, r-cran-mirai, r-cran-nloptr, r-cran-processx, r-cran-progressr, r-cran-redux, r-cran-rhpcblasctl, r-cran-rush, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bbotk_1.10.0-1.ca2604.1_arm64.deb Size: 1154816 MD5sum: ca69fbfa1e14a286caa9ff0262c1ee9f SHA1: ca0a534d6137c675c2e547656abf80ec5e3eeec1 SHA256: a997ca85a8ed1e6ea288f438f4d551e60851a9e1ce2e7a7a9e377082bf4256c3 SHA512: ec39c1842631892eea537481414eb7f6dcca9625dce84d8584ef5501115a292bb3a6053021ee73ee30e951d6821132463f2d76b1e2528b96834dada77376b77e Homepage: https://cran.r-project.org/package=bbotk Description: CRAN Package 'bbotk' (Black-Box Optimization Toolkit) Features highly configurable search spaces via the 'paradox' package and optimizes every user-defined objective function. The package includes several optimization algorithms e.g. Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband (in 'mlr3hyperband'). bbotk is the base package of 'mlr3tuning', 'mlr3fselect' and 'miesmuschel'. Package: r-cran-bcbcsf Architecture: arm64 Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 801 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind Filename: pool/dists/resolute/main/r-cran-bcbcsf_1.0-2-1.ca2604.1_arm64.deb Size: 721208 MD5sum: c39038ebf6b3b2ef3b9fb12e2a865833 SHA1: 6a8160ea5bcbd8451f49312ec442f6f6cce45bae SHA256: cf1ee9649cb655766de04acb01a2f2e94c822134f4af774c7af032e051c6a523 SHA512: fc0bded9a0fd339785a3ad3abff79b77987ac039fb4de5b877cd6fdc7ebf808fe93755879dc7334df7bb3eaf55ce1e2e474832552b1ee9395819298cdcdcf275 Homepage: https://cran.r-project.org/package=BCBCSF Description: CRAN Package 'BCBCSF' (Bias-Corrected Bayesian Classification with Selected Features) Fully Bayesian Classification with a subset of high-dimensional features, such as expression levels of genes. The data are modeled with a hierarchical Bayesian models using heavy-tailed t distributions as priors. When a large number of features are available, one may like to select only a subset of features to use, typically those features strongly correlated with the response in training cases. Such a feature selection procedure is however invalid since the relationship between the response and the features has be exaggerated by feature selection. This package provides a way to avoid this bias and yield better-calibrated predictions for future cases when one uses F-statistic to select features. Package: r-cran-bcclong Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5030 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_arm64.deb Size: 4370540 MD5sum: 8ef3e6f54ea1cf4ab400c6be90da07da SHA1: 2279f79d456b3f4f911cad32c2d35831ce9dc27e SHA256: 85d411cf9bd41f9f1c54120f588d6d2551a6fea77132d1072d7ebe839d1a1a61 SHA512: 5e049d36539bdb1d0b13a199a89efa7c59d7b73d1e12620acfdc1684f4a9b49115ff5a96bd80cb12a662fff666f3e13c6129140bf1fa017473b48cb8ed47342c Homepage: https://cran.r-project.org/package=BCClong Description: CRAN Package 'BCClong' (Bayesian Consensus Clustering for Multiple Longitudinal Features) It is very common nowadays for a study to collect multiple features and appropriately integrating multiple longitudinal features simultaneously for defining individual clusters becomes increasingly crucial to understanding population heterogeneity and predicting future outcomes. 'BCClong' implements a Bayesian consensus clustering (BCC) model for multiple longitudinal features via a generalized linear mixed model. Compared to existing packages, several key features make the 'BCClong' package appealing: (a) it allows simultaneous clustering of mixed-type (e.g., continuous, discrete and categorical) longitudinal features, (b) it allows each longitudinal feature to be collected from different sources with measurements taken at distinct sets of time points (known as irregularly sampled longitudinal data), (c) it relaxes the assumption that all features have the same clustering structure by estimating the feature-specific (local) clusterings and consensus (global) clustering. Package: r-cran-bcee Architecture: arm64 Version: 1.3.2-1.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), 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_arm64.deb Size: 152122 MD5sum: e6a8dc27f31edb38cb9bbe3d24c25b76 SHA1: f392fa9af79f54ec42856eb4aed3a50684f42d18 SHA256: c4d3d1d8121e66cf7088b4bbf3c36cbb990f59f451a1ea5e20d4c933a9376413 SHA512: 76f6ff7dfd090508718420c4ba1f77e0e813447049d9531c9ac0801a62731a234a36bd0e9fff8a47dd874ae59fbebf50e6b6a957f6cb61377bd71e5073dd7665 Homepage: https://cran.r-project.org/package=BCEE Description: CRAN Package 'BCEE' (The Bayesian Causal Effect Estimation Algorithm) A Bayesian model averaging approach to causal effect estimation based on the BCEE algorithm. Currently supports binary or continuous exposures and outcomes. For more details, see Talbot et al. (2015) Talbot and Beaudoin (2022) . Package: r-cran-bcf Architecture: arm64 Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1486 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 856866 MD5sum: 41304fbd20e4cf5423c3e63200ca1e80 SHA1: f3464262be1375d83e3b65e6da2bcd3172e1a60a SHA256: a4d014b499e55fc9dd6f7eb2f1bb1863268efbbd0030abca40a964883be6025e SHA512: b95ec25a6a3af37c4f088a22796c91a04cdb9107c4c8f15d816f9cb69e573c04423fcee7bf56ee47786709212b416374902a9c32d9fdcbf921067bbb036095cb Homepage: https://cran.r-project.org/package=bcf Description: CRAN Package 'bcf' (Causal Inference using Bayesian Causal Forests) Causal inference for a binary treatment and continuous outcome using Bayesian Causal Forests. See Hahn, Murray and Carvalho (2020) for additional information. This implementation relies on code originally accompanying Pratola et. al. (2013) . Package: r-cran-bcfm Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1069 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 575168 MD5sum: d1fd50ad44b2305ab9535547c8982270 SHA1: 993259949b77e99b0d8b52510bcd7c9c93880ca7 SHA256: 7a8ddaba09ab20b399d5f5e68b14630fd31b344cefdfbc433933d1d54d07ad65 SHA512: 71cb79c1108ac7ff2afb1075d1eedb78ec69e5d065d859540569de88043eb28eedaab9f2306aae5d90bb349760ce3ae32fc3258f60c2fb92fce34c5b815a03a0 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: arm64 Version: 4.7.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1707 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-coda, r-cran-mclust, r-cran-ggplot2, r-cran-ggridges, r-cran-magrittr, r-cran-purrr, r-cran-ggforce, r-cran-dplyr, r-cran-scales, r-cran-stringr, r-cran-checkmate Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-bchron_4.7.8-1.ca2604.1_arm64.deb Size: 1182690 MD5sum: 3c321f5cd1dcb6c845afbb2307fa723f SHA1: c2c8b8fa74ab9a5edea9263dcc60a7435eb1ecc7 SHA256: 0d27badcd996ce9e556a7040092bf1f0db91875449d17ad58314b71e4d024ed5 SHA512: ead9989a023712f0b488dd17e5947d5b505fc13733d07834b454716ed6653f2c910695be253169b619ced13975651c11268974d4df54b4c15b5fa6078805a5d9 Homepage: https://cran.r-project.org/package=Bchron Description: CRAN Package 'Bchron' (Age-Depth Radiocarbon Modelling) Enables quick calibration of radiocarbon dates under various calibration curves (including user generated ones); age-depth modelling as per the algorithm of Haslett and Parnell (2008) ; Relative sea level rate estimation incorporating time uncertainty in polynomial regression models (Parnell and Gehrels 2015) ; non-parametric phase modelling via Gaussian mixtures as a means to determine the activity of a site (and as an alternative to the 'Oxcal' function SUM(); currently unpublished), and reverse calibration of dates from calibrated into 14C years (also unpublished). Package: r-cran-bclogit Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3475 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_arm64.deb Size: 954570 MD5sum: 4ece4aee17fe0c692fae65cf163d9c45 SHA1: 8ee57be71ab02a99236b16df1a033e843a1d20e3 SHA256: 2193936bfc0e487011f9ec12c7376d7b9683b7d85c7fa001550e7302b3536cee SHA512: dc1f05001191313bc236551cfdd241ad12636b648a2f0c7df1777243508c6a6b1b8aaaaf276286727aaaa0bfd38882c820588adcb7c3a4c2684da4cbafca7b02 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: arm64 Version: 4.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 589 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 298718 MD5sum: b88be8f766b59dfe305567515518c282 SHA1: 413279dc3245c7e4fe15ac7e2277300cb21ab53e SHA256: 6fc24fa6837fb7f0cb9d7e4e55b7ec2c980bd4a9058ecc220283e6ccabdd4a2a SHA512: 46b65694f6b182b28cb3b8e6c4d9abb1cd79db497b29f86b00410aa3cb74f29aabfa719a334197f397a3049450d241fec18f0354fd8da5f98a5f1ff3b065a07e Homepage: https://cran.r-project.org/package=bcp Description: CRAN Package 'bcp' (Bayesian Analysis of Change Point Problems) Provides an implementation of the product partition model described in Barry and Hartigan (2019) for the normal errors change point problem using Markov Chain Monte Carlo (MCMC). It also extends the methodology to regression models on a connected graph as reported in Wang and Emerson (2015) , allowing estimation of change point models with multivariate responses. Parallel MCMC, previously available in 'bcp' v.3.0.0, is currently not implemented. Package: r-cran-bcpa Architecture: arm64 Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 808 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_arm64.deb Size: 579656 MD5sum: f93aa36db3b0ec217b0bdc7a47bcbbfe SHA1: 67f2572b5692faa6f753bffcfddeba8c65d9a9e8 SHA256: 504021d1f9ba1a72a83c2b3adeb80aac8f44f659bac29f26088ccebc881aa33a SHA512: 7c247aa1ce57c6473483a7afdc471b22a837863209e46853c7c5757a07cc8d3ab08016e639854fcf376d8e9e0f85564ce6b8912fe1b120ea411d694c2e6b6c13 Homepage: https://cran.r-project.org/package=bcpa Description: CRAN Package 'bcpa' (Behavioral Change Point Analysis of Animal Movement) The Behavioral Change Point Analysis (BCPA) is a method of identifying hidden shifts in the underlying parameters of a time series, developed specifically to be applied to animal movement data which is irregularly sampled. The method is based on: E. Gurarie, R. Andrews and K. Laidre A novel method for identifying behavioural changes in animal movement data (2009) Ecology Letters 12:5 395-408. A development version is on . NOTE: the BCPA method may be useful for any univariate, irregularly sampled Gaussian time-series, but animal movement analysts are encouraged to apply correlated velocity change point analysis as implemented in the smoove package, as of this writing on GitHub at . An example of a univariate analysis is provided in the UnivariateBCPA vignette. Package: r-cran-bcrocsurface Architecture: arm64 Version: 1.0-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2018 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnet, r-cran-rgl, r-cran-boot, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-bcrocsurface_1.0-6-1.ca2604.1_arm64.deb Size: 671980 MD5sum: e9c3463e4f1ac439d0ba74f3c8e9154d SHA1: 84071692424f15ba171fdfcfd4bcf1597ba418c5 SHA256: c3fb775e0b28f43122bb60490f8d1ddd583f085ca342293537848255f8572cdf SHA512: 5317550dd6fdd2339e1ea33bf19fe152e9091e04c9c1cdd480b93265853cd75257da2fed4111d51925e7fac31766c58d03ae480b2fa0279ce68689432d6de35d Homepage: https://cran.r-project.org/package=bcROCsurface Description: CRAN Package 'bcROCsurface' (Bias-Corrected Methods for Estimating the ROC Surface ofContinuous Diagnostic Tests) The bias-corrected estimation methods for the receiver operating characteristics ROC surface and the volume under ROC surfaces (VUS) under missing at random (MAR) assumption. Package: r-cran-bcrypt Architecture: arm64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-openssl Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-bcrypt_1.2.1-1.ca2604.1_arm64.deb Size: 27176 MD5sum: 08ba5f60a2a2d15bc828608aed64146e SHA1: ebb5e4f87cccb9116151984d80c535da445972d3 SHA256: 74c1b8b694e6fd4f4be1de57754b9de9a3d50748309724a1093b87eace6b74d6 SHA512: 297f0c1c831abc87f3b60673873c78800ca709bb592b857b441c3c2b7c45a2822425e25dfd4faef311d55d4a60c7fcb7d03508d9eae1fb26bb748d4d026d7777 Homepage: https://cran.r-project.org/package=bcrypt Description: CRAN Package 'bcrypt' ('Blowfish' Key Derivation and Password Hashing) Bindings to the 'blowfish' password hashing algorithm derived from the 'OpenBSD' implementation. Package: r-cran-bcv Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-bcv_1.0.2-1.ca2604.1_arm64.deb Size: 68552 MD5sum: 3592c4efd46b3cc8eb9ecceb34be09b9 SHA1: bdcf5e6a6133f0195795283d86a2dcb329fda94e SHA256: 904465cdbc836d39dd02abf47b170987129077cfaa41d0488d888e7f84c4a6ff SHA512: ce71290221343b72e3e1c0c3ba89a1b263f1ddfae6b252ce4ba373495ed7cce513874257caeff1fc89124ef6a1b3c5d2bf9713be62db1e3701123bf554bdd8d2 Homepage: https://cran.r-project.org/package=bcv Description: CRAN Package 'bcv' (Cross-Validation for the SVD (Bi-Cross-Validation)) Methods for choosing the rank of an SVD (singular value decomposition) approximation via cross validation. The package provides both Gabriel-style "block" holdouts and Wold-style "speckled" holdouts. It also includes an implementation of the SVDImpute algorithm. For more information about Bi-cross-validation, see Owen & Perry's 2009 AoAS article (at ) and Perry's 2009 PhD thesis (at ). 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Leday and Richardson (2019), Biometrics, . 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This Biological Entity Dictionary (BED) has been developed to address three main challenges. The first one is related to the completeness of identifier mappings. Indeed, direct mapping information provided by the different systems are not always complete and can be enriched by mappings provided by other resources. More interestingly, direct mappings not identified by any of these resources can be indirectly inferred by using mappings to a third reference. For example, many human Ensembl gene ID are not directly mapped to any Entrez gene ID but such mappings can be inferred using respective mappings to HGNC ID. The second challenge is related to the mapping of deprecated identifiers. Indeed, entity identifiers can change from one resource release to another. The identifier history is provided by some resources, such as Ensembl or the NCBI, but it is generally not used by mapping tools. 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The model is presented in the publication from Baas, J., Goussen, B., Miles, M., Preuss, T.G., Roessing, I. (2022) and Baas, J., Goussen, B., Taenzler, V., Roeben, V., Miles, M., Preuss, T.G., van den Berg, S., Roessink, I. (2024) , and is based on the GUTS framework Jager, T., Albert, C., Preuss, T.G. and Ashauer, R. (2011) . The authors are grateful to Bayer A.G. for its financial support. 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Package: r-cran-beezdemand Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10017 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlsr, r-cran-nlstools, r-cran-nls2, r-cran-ggplot2, r-cran-optimx, r-cran-broom, r-cran-lme4, r-cran-emmeans, r-cran-minpack.lm, r-cran-nls.multstart, r-cran-performance, r-cran-scales, r-cran-tibble, r-cran-lifecycle, r-cran-dplyr, r-cran-tidyr, r-cran-nlme, r-cran-rlang, r-cran-tmb, r-cran-rcppeigen Suggests: r-cran-broom.mixed, r-cran-ggally, r-cran-knitr, r-cran-tidyverse, r-cran-rmarkdown, r-cran-purrr, r-cran-conflicted, r-cran-devtools, r-cran-here, r-cran-readr, r-cran-patchwork, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-beezdemand_0.2.0-1.ca2604.1_arm64.deb Size: 5723718 MD5sum: ef4cfc5a5ac0cdbe91ee6e783d6db4bd SHA1: 970344af2222627b1e5ddbc2c93a457f6d2ca603 SHA256: e0cbb32226e83e30a98a4acf0f38884e3de079ac34067c5e928dbe34cae05adc SHA512: e87b2bd865fe7a0e5fcd31594d8f1fba754c1b46f7314eed1de8a8b29f1886180a2e2b193505a80f2e8b212fec2f4281005a2c156b31eaa45204b5d963ae7ebf Homepage: https://cran.r-project.org/package=beezdemand Description: CRAN Package 'beezdemand' (Behavioral Economic Easy Demand) Facilitates many of the analyses performed in studies of behavioral economic demand. The package supports commonly-used options for modeling operant demand including (1) data screening proposed by Stein, Koffarnus, Snider, Quisenberry, & Bickel (2015; ), (2) fitting models of demand such as linear (Hursh, Raslear, Bauman, & Black, 1989, ), exponential (Hursh & Silberberg, 2008, ) and modified exponential (Koffarnus, Franck, Stein, & Bickel, 2015, ), and (3) calculating numerous measures relevant to applied behavioral economists (Intensity, Pmax, Omax). Also supports plotting and comparing data. Package: r-cran-bekks Architecture: arm64 Version: 1.4.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1774 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-reshape2, r-cran-ggplot2, r-cran-mathjaxr, r-cran-gridextra, r-cran-ggfortify, r-cran-xts, r-cran-future, r-cran-future.apply, r-cran-ks, r-cran-lubridate, r-cran-pbapply, r-cran-numderiv, r-cran-moments, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bekks_1.4.7-1.ca2604.1_arm64.deb Size: 1177868 MD5sum: 594e8a1a0e6ede463178f91561f7040f SHA1: e5a7505904f2f713cce7eb9cc785c0747bc14781 SHA256: e29e6798d1632b38433f1ffaea3d9423fa52d2eef60116e7ca4443d3788005e1 SHA512: a81a3c611455205e699a6ffdd0342010f989e8f504e40c7becdb5e19160bc59217289ab659db391a583a1ebfe19097a166b2774eae6f2a809a36bd58be4ae4ee Homepage: https://cran.r-project.org/package=BEKKs Description: CRAN Package 'BEKKs' (Multivariate Conditional Volatility Modelling and Forecasting) Methods and tools for estimating, simulating and forecasting of so-called BEKK-models (named after Baba, Engle, Kraft and Kroner) based on the fast Berndt–Hall–Hall–Hausman (BHHH) algorithm described in Hafner and Herwartz (2008) . For an overview, we refer the reader to Fülle et al. (2024) . 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Package: r-cran-benchmarking Architecture: arm64 Version: 0.33-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 737 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lpsolveapi, r-cran-ucminf, r-cran-quadprog, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-benchmarking_0.33-1.ca2604.1_arm64.deb Size: 549258 MD5sum: b6cbcdd03fe05ef4b9b918f3ce79612e SHA1: f0eb63ef6ef2cbc42233aa6f6fb2a71dee749447 SHA256: 477f77c0a7ed36b6718b9ea7f1b4ef9ddd5ff6600394653314171fa57196f9ca SHA512: 5b02696bf9f3a09251da9f41e2df4911209f62db8904d345b2df681e777b5b0b55e12c56e954c2e090518c333a82aae8577b25880fa4d6cedfcc70f64c831f2c Homepage: https://cran.r-project.org/package=Benchmarking Description: CRAN Package 'Benchmarking' (Benchmark and Frontier Analysis Using DEA and SFA) Methods for frontier analysis, Data Envelopment Analysis (DEA), under different technology assumptions (fdh, vrs, drs, crs, irs, add/frh, and fdh+), and using different efficiency measures (input based, output based, hyperbolic graph, additive, super, and directional efficiency). 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Described in Bouranis, L., Demiris, N., Kalogeropoulos, K., and Ntzoufras, I. (2022) . 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See Kai Zhang (2019) BET on Independence, Journal of the American Statistical Association, 114:528, 1620-1637, , Kai Zhang, Wan Zhang, Zhigen Zhao, Wen Zhou. (2023). BEAUTY Powered BEAST, and Wan Zhang, Zhigen Zhao, Michael Baiocchi, Yao Li, Kai Zhang. (2023) SorBET: A Fast and Powerful Algorithm to Test Dependence of Variables, Techinical report. 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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: arm64 Version: 3.2-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2921 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-flexmix, r-cran-formula, r-cran-lmtest, r-cran-modeltools, r-cran-sandwich Suggests: r-cran-bamlss, r-cran-car, r-cran-distributions3, r-cran-knitr, r-cran-lattice, r-cran-numderiv, r-cran-partykit, r-cran-quarto, r-cran-statmod, r-cran-strucchange Filename: pool/dists/resolute/main/r-cran-betareg_3.2-4-1.ca2604.1_arm64.deb Size: 1673210 MD5sum: 7451388072daf380ead54b011f4a7002 SHA1: c2582db259da3fda8ddb6b082db834a3be642458 SHA256: 9638f0823e253dc901601e602e47836d92e44897b6b7bab0fffabc916dc11414 SHA512: 9b5f74a8f13e84e8c3ecd4d046c71684911681226cb84e7cd6f1430d1f4344dfe1218d92577d85c1111c5e76989b1e7c16fed700fcc4bb494da17e7e629e25e1 Homepage: https://cran.r-project.org/package=betareg Description: CRAN Package 'betareg' (Beta Regression) Beta regression for modeling beta-distributed dependent variables on the open unit interval (0, 1), e.g., rates and proportions, see Cribari-Neto and Zeileis (2010) . Moreover, extended-support beta regression models can accommodate dependent variables with boundary observations at 0 and/or 1, see Kosmidis and Zeileis (2025) . For the classical beta regression model, alternative specifications are provided: Bias-corrected and bias-reduced estimation, finite mixture models, and recursive partitioning for beta regression, see Grün, Kosmidis, and Zeileis (2012) . Package: r-cran-betaregscale Architecture: arm64 Version: 2.6.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2654 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 1748562 MD5sum: adf07ddce0644653a53218d682c0bd77 SHA1: f8ac6036092da8690daae07fcd089744bbb170a6 SHA256: cccdcf2e863ad77255cac451bbe6c5c79a29526679b4fd2792b5bbb42f625035 SHA512: 217a6f239b0c80a415c1e8bc7fef6d6f71df996fb2dea96bb22301863718e16435a11e9976ea8e1547acd623eef8ee974248cb9fcf059d6f845985eb82ab1718 Homepage: https://cran.r-project.org/package=betaregscale Description: CRAN Package 'betaregscale' (Beta Regression for Interval-Censored Scale-Derived Outcomes) Maximum-likelihood estimation of beta regression models for responses derived from bounded rating scales. 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Package: r-cran-betategarch Architecture: arm64 Version: 3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 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_arm64.deb Size: 125914 MD5sum: 4f8d30d6fba5e1bb7ad607f978808e9f SHA1: ad356bd72dfbeddec8d50e55d383934628c50844 SHA256: ebd0d22950027f1f1a265c6380425841941be39615d9a44a1573bdda585ee5c0 SHA512: 8d52ece701c2b1df8ca33c32e81963166e506e2e85302043105231a8c82500f11a6fc257b1b9cc9788c8f1c7be1959849f030bef99faebe38d2a350ea4f72bca Homepage: https://cran.r-project.org/package=betategarch Description: CRAN Package 'betategarch' (Simulation, Estimation and Forecasting of Beta-Skew-t-EGARCHModels) Simulation, estimation and forecasting of first-order Beta-Skew-t-EGARCH models with leverage (one-component, two-component, skewed versions). Package: r-cran-bevimed Architecture: arm64 Version: 7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 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_arm64.deb Size: 378520 MD5sum: 8d062a919133d3c902c09d80c06e2028 SHA1: 6c4a6fcae65ca7ca1c0e7ae492a2f4b6a7ee99a6 SHA256: 38063ad5097d83bef6eab2c881b218a50154929abfe9937e1308c91163b62cb3 SHA512: 4976197c6ee2d4317bcec245e7df83d89d0da72b4b150daa7e6b31d0bfa0bc2f4368d441f96df54e7068d818d4b7bb035df8e17be09da780eed1f927288b0886 Homepage: https://cran.r-project.org/package=BeviMed Description: CRAN Package 'BeviMed' (Bayesian Evaluation of Variant Involvement in Mendelian Disease) A fast integrative genetic association test for rare diseases based on a model for disease status given allele counts at rare variant sites. 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Package: r-cran-bfast Architecture: arm64 Version: 1.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 783 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 256794 MD5sum: cac9ba26de6a620df41f009805fa8c02 SHA1: b2259872c35b15498c279071867ed465a3c0de5f SHA256: 9a9f2d75824a3b59a9b255bade307e6a0deaf0b6f307d9cf4bd442c5ef4f1a41 SHA512: ab7378d3c4ed5e70cc641565ac018a1860f6c70832aba243907c555a9b9f44bf1f3210ef426454982aa2c3706eae47e58aea682a17721c37ebe1050a84adb58d Homepage: https://cran.r-project.org/package=bfast Description: CRAN Package 'bfast' (Breaks for Additive Season and Trend) Decomposition of time series into trend, seasonal, and remainder components with methods for detecting and characterizing abrupt changes within the trend and seasonal components. 'BFAST' can be used to analyze different types of satellite image time series and can be applied to other disciplines dealing with seasonal or non-seasonal time series, such as hydrology, climatology, and econometrics. The algorithm can be extended to label detected changes with information on the parameters of the fitted piecewise linear models. 'BFAST' monitoring functionality is described in Verbesselt et al. (2010) . 'BFAST monitor' provides functionality to detect disturbance in near real-time based on 'BFAST'- type models, and is described in Verbesselt et al. (2012) . 'BFAST Lite' approach is a flexible approach that handles missing data without interpolation, and will be described in an upcoming paper. Furthermore, different models can now be used to fit the time series data and detect structural changes (breaks). Package: r-cran-bfp Architecture: arm64 Version: 0.0-50-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 766 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_arm64.deb Size: 354500 MD5sum: f44d3e4464eb449d88cd10259b744f86 SHA1: 6ee469ba2efd31f1fb7080e8c62d5be4cdec575a SHA256: 9ba53e7bf6235d42f9b55abb6fb731ea16b37091d13e7254e284a6728bcefe68 SHA512: f7931e602cbd4e9c23d90b61448f074c00741a66ab77dbc1d8a85e4a6ef36d46367e8a08193c276787aed9568bc37a8d0c6fde1fab30aeb2fdf881a752431b7e Homepage: https://cran.r-project.org/package=bfp Description: CRAN Package 'bfp' (Bayesian Fractional Polynomials) Implements the Bayesian paradigm for fractional polynomial models under the assumption of normally distributed error terms, see Sabanes Bove, D. and Held, L. (2011) . 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The package is intended for applied quantitative researchers in the social and behavioral sciences, medical research, and related fields. The Bayes factor tests can be executed for statistical models such as univariate and multivariate normal linear models, correlation analysis, generalized linear models, special cases of linear mixed models, survival models, relational event models. Parameters that can be tested are location parameters (e.g., group means, regression coefficients), variances (e.g., group variances), and measures of association (e.g,. polychoric/polyserial/biserial/tetrachoric/product moments correlations), among others. Relevant references on the methodology The statistical underpinnings are described in O'Hagan (1995) , Mulder and Xin (2022) , Mulder and Gelissen (2019) , Mulder and Fox (2019) , Boeing-Messing, van Assen, Hofman, Hoijtink, and Mulder (2017) , Hoijtink, Mulder, van Lissa, and Gu (2018) , Gu, Mulder, and Hoijtink (2018) , Hoijtink, Gu, and Mulder (2018) , and Hoijtink, Gu, Mulder, and Rosseel (2018) . When using the packages, please refer to the package Mulder et al. (2021) and the relevant methodological papers. 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Package: r-cran-bglr Architecture: arm64 Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4792 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_arm64.deb Size: 4608428 MD5sum: c059395078690fd35d52e404abfa7647 SHA1: a542e72e3e596dcf493ab0b36e6a4f27cfe5d132 SHA256: 71074bc17e18c45a43d57d6217344e9dd7c10831203624abf1300e844085342f SHA512: 2647cd22928b64f21cd67ad3bb19aaf456063f5a2c3525a87fea4e04cd84b80ba154ce741636b70b65db7f658209b186f5f407c1125d059fb0ec6f40aa8d2d3c 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: arm64 Version: 0.1.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1997 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 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_arm64.deb Size: 1068200 MD5sum: ee1ff2b3b1c0866954b25215a4692133 SHA1: d0c8aee789c116124dbd89ce052603bef1631bb5 SHA256: 5ac508e1bd818a3dbc7efb28b04c6d243b7635b1d8e3b111cecfafdbfa7364d6 SHA512: e2e43ff7d84d8e52374d9baf82df9ece65cd27b53a8e5a8dcb53cf1637b37a5e7ee789dd203c7915fac6d58246e32a1b7f9eecafc50bd8ddcf2247b02cfdff3a Homepage: https://cran.r-project.org/package=bgms Description: CRAN Package 'bgms' (Bayesian Analysis of Networks of Binary and/or Ordinal Variables) Bayesian variable selection methods for analyzing the structure of a Markov random field model for a network of binary and/or ordinal variables. Package: r-cran-bgumbel Architecture: arm64 Version: 0.0.3-1.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_arm64.deb Size: 36888 MD5sum: db99bbb03b7ebcb42d419e094be20112 SHA1: f17143e392773dda4bdc05a644af9b2a2457e2d3 SHA256: 91de789bb5bdad1bfe096b62f5916bf2d33b4206d4eec7f38c2eaf8360f43a16 SHA512: 3677a2adb0e1bc6ea5d964b306fcc4cf168dec35edff534fb32774ddd1f0094eab8e3c632b7c5fd5a263b82f5331e9d20d0cefdf8c151f13c763ccfb4de580a8 Homepage: https://cran.r-project.org/package=bgumbel Description: CRAN Package 'bgumbel' (Bimodal Gumbel Distribution) Bimodal Gumbel distribution. General functions for performing extreme value analysis. Package: r-cran-bgvar Architecture: arm64 Version: 2.5.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4792 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 3239528 MD5sum: 4d3f1c35e7544d8268f8d904e90a18d7 SHA1: 45f7f55339d98058c8169f1e6f8bbe6c02ee6a92 SHA256: 8de30a338c122375d2cbfe5280b0b68023e9423bb1a28805c882e3c2ec1cc382 SHA512: ce594ea4cb0128f25acf5c35f280dc99392913562cf0aa836cbacaf02f18a55935012c78a022989b76d20cf8b4f9d766ebeb9b64386ae219f2ca8f4cc7781a6d Homepage: https://cran.r-project.org/package=BGVAR Description: CRAN Package 'BGVAR' (Bayesian Global Vector Autoregressions) Estimation of Bayesian Global Vector Autoregressions (BGVAR) with different prior setups and the possibility to introduce stochastic volatility. Built-in priors include the Minnesota, the stochastic search variable selection and Normal-Gamma (NG) prior. For a reference see also Crespo Cuaresma, J., Feldkircher, M. and F. Huber (2016) "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach", Journal of Applied Econometrics, Vol. 31(7), pp. 1371-1391 . Post-processing functions allow for doing predictions, structurally identify the model with short-run or sign-restrictions and compute impulse response functions, historical decompositions and forecast error variance decompositions. Plotting functions are also available. The package has a companion paper: Boeck, M., Feldkircher, M. and F. Huber (2022) "BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R", Journal of Statistical Software, Vol. 104(9), pp. 1-28 . Package: r-cran-bgw Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bgw_0.1.4-1.ca2604.1_arm64.deb Size: 136756 MD5sum: a4fe6662a95ce476e74c88e4e3d91f15 SHA1: 4a70aef4296ce848cc0a469d87ce852740ef3753 SHA256: 4cb57b83a94c868c51e9cda188571357fc8321350f313557890e191da7cd8f91 SHA512: cb227cc0a516f4cdf0317f384c1bb6bf5397e20db9935fc167a93fda3161891b0729ddb18bf0e4d0e1c109a56bda3284873416a20543f999f028f5ea57b87746 Homepage: https://cran.r-project.org/package=bgw Description: CRAN Package 'bgw' (Bunch-Gay-Welsch Statistical Estimation) Performs statistical estimation and inference-related computations by accessing and executing modified versions of 'Fortran' subroutines originally published in the Association for Computing Machinery (ACM) journal Transactions on Mathematical Software (TOMS) by Bunch, Gay and Welsch (1993) . The acronym 'BGW' (from the authors' last names) will be used when making reference to technical content (e.g., algorithm, methodology) that originally appeared in ACM TOMS. A key feature of BGW is that it exploits the special structure of statistical estimation problems within a trust-region-based optimization approach to produce an estimation algorithm that is much more effective than the usual practice of using optimization methods and codes originally developed for general optimization. The 'bgw' package bundles 'R' wrapper (and related) functions with modified 'Fortran' source code so that it can be compiled and linked in the 'R' environment for fast execution. This version implements a function ('bgw_mle.R') that performs maximum likelihood estimation (MLE) for a user-provided model object that computes probabilities (a.k.a. probability densities). The original motivation for producing this package was to provide fast, efficient, and reliable MLE for discrete choice models that can be called from the 'Apollo' choice modelling 'R' package ( see ). Starting with the release of Apollo 3.0, BGW is the default estimation package. However, estimation can also be performed using BGW in a stand-alone fashion without using 'Apollo' (as shown in simple examples included in the package). Note also that BGW capabilities are not limited to MLE, and future extension to other estimators (e.g., nonlinear least squares, generalized method of moments, etc.) is possible. The 'Fortran' code included in 'bgw' was modified by one of the original BGW authors (Bunch) under his rights as confirmed by direct consultation with the ACM Intellectual Property and Rights Manager. See . The main requirement is clear citation of the original publication (see above). Package: r-cran-bhetgp Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 713 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_arm64.deb Size: 492838 MD5sum: 8a5be4325fe31a6a0427538676d45021 SHA1: 0b47e05b6277387cf2b78f12a57c2dfc7dd990d9 SHA256: 4219005e8ae1fb18289a3289dad764ceefad995207a183c684c6d738c9c71369 SHA512: ce591b14c8d8a72fed551056808a204f9f71c251467ebc69f6c9598286ac9ea91b881944e68aeed10a0c13c36267d323f0519827f98c00a352e0109051460f88 Homepage: https://cran.r-project.org/package=bhetGP Description: CRAN Package 'bhetGP' (Bayesian Heteroskedastic Gaussian Processes) Performs Bayesian posterior inference for heteroskedastic Gaussian processes. Models are trained through MCMC including elliptical slice sampling (ESS) of latent noise processes and Metropolis-Hastings sampling of kernel hyperparameters. Replicates are handled efficientyly through a Woodbury formulation of the joint likelihood for the mean and noise process (Binois, M., Gramacy, R., Ludkovski, M. (2018) ) For large data, Vecchia-approximation for faster computation is leveraged (Sauer, A., Cooper, A., and Gramacy, R., (2023), ). Incorporates 'OpenMP' and SNOW parallelization and utilizes 'C'/'C++' under the hood. Package: r-cran-bhmsmafmri Architecture: arm64 Version: 2.3-1.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-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_arm64.deb Size: 596262 MD5sum: 30350370d0a577e1bdd65653129a42e6 SHA1: 96046751cfc644b304373fbf972a84685d171fe7 SHA256: 67690fa29f5c7fc48081158d650612f251fda21d7b08d6680e2b9562296bf39b SHA512: de27463401e82d7e17b40cc3a39e8d6e50cefe369366fe44be30674df43cc57a9b030470d471e1d32bb4b42723875b0602622fdbf2aa5522c481ca3f8b7c4694 Homepage: https://cran.r-project.org/package=BHMSMAfMRI Description: CRAN Package 'BHMSMAfMRI' (Bayesian Hierarchical Multi-Subject Multiscale Analysis ofFunctional MRI (fMRI) Data) Package BHMSMAfMRI performs Bayesian hierarchical multi-subject multiscale analysis of fMRI data as described in Sanyal & Ferreira (2012) , or other multiscale data, using wavelet-based prior that borrows strength across subjects and provides posterior smoothed images of the effect sizes and samples from the posterior distribution. Package: r-cran-bhpm Architecture: arm64 Version: 1.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1096 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_arm64.deb Size: 815264 MD5sum: ac6ee57c8ba64c9a9fcfa30b3300d8dc SHA1: 1c3b44f25dc66e00fd37f17d76303b89f7c9d79c SHA256: 332d65e9c9ba23c21bc5ce590e66e8fa558fc3bcd0218145076a23d4165031e8 SHA512: b7c90e99a35f55228fccea9cecd199f5cc8412fa004655470b407749891286aab32e59c5dd42f03f10aa8100e7503828f185d1be32a66abfb6b4f31349991879 Homepage: https://cran.r-project.org/package=bhpm Description: CRAN Package 'bhpm' (Bayesian Hierarchical Poisson Models for Multiple GroupedOutcomes with Clustering) Bayesian hierarchical methods for the detection of differences in rates of related outcomes for multiple treatments for clustered observations (Carragher et al. (2020) ). This software was developed for the Precision Drug Theraputics: Risk Prediction in Pharmacoepidemiology project as part of a Rutherford Fund Fellowship at Health Data Research (UK), Medical Research Council (UK) award reference MR/S003967/1 (). Principal Investigator: Raymond Carragher. Package: r-cran-bhsbvar Architecture: arm64 Version: 3.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 705 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 322138 MD5sum: 52eb8246d2ee70ae820fb45b0c1afec3 SHA1: f442fd90eae6e957a185df7167b38ae56b6b423e SHA256: 847b048081c12b3d5465a4f760640d52680827543bb72e08269f6263ea9c682d SHA512: a883f01f760f908fc631eb9ad089042907cb599c0d468cb630dec708a62db87aa3407e3c8d002f5b5b75a95a97ecd8432ff7804e0ab1ad012debc7f6babe899e Homepage: https://cran.r-project.org/package=BHSBVAR Description: CRAN Package 'BHSBVAR' (Structural Bayesian Vector Autoregression Models) Provides a function for estimating the parameters of Structural Bayesian Vector Autoregression models with the method developed by Baumeister and Hamilton (2015) , Baumeister and Hamilton (2017) , and Baumeister and Hamilton (2018) . Functions for plotting impulse responses, historical decompositions, and posterior distributions of model parameters are also provided. Package: r-cran-biasedurn Architecture: arm64 Version: 2.0.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: libc6 (>= 2.29), libstdc++6 (>= 4.1.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-biasedurn_2.0.12-1.ca2604.1_arm64.deb Size: 277722 MD5sum: db18746fad2979510629ee4deca01f0d SHA1: 17a0d790464a80678c9a2cf85875e7791827febf SHA256: 946c51c95e85bd7c61f022e54899947113e16a88d6a79786236094f6d4047df5 SHA512: e8ac65bba5fc5065cacfba0eb107c6e52bca56b64504522ebe2aa6d9bec0a7fae918088b30475f6ff3b62ebe038dc2cfdd92022facd7d13d703a35f857c68eb2 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: arm64 Version: 2.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1731 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1607578 MD5sum: 7af98bd330b17451f26989da86eec6e4 SHA1: 9b587d8600e45a6485990c046dde523e13c5a68f SHA256: 5c9d134ca83350710b519846b7b3f9c93e8664b11e17b793dee27b964c6ae16a SHA512: e1719f66392c3882c5b1c6047b3a7e6510b9ed55e58e2a451052955529c4ff87609fce9304208d87522b95ac7e2327a362f25b6109c32a60f73ad6cb5eedaaeb Homepage: https://cran.r-project.org/package=BiDAG Description: CRAN Package 'BiDAG' (Bayesian Inference for Directed Acyclic Graphs) Implementation of a collection of MCMC methods for Bayesian structure learning of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient inference on larger DAGs, the space of DAGs is pruned according to the data. To filter the search space, the algorithm employs a hybrid approach, combining constraint-based learning with search and score. A reduced search space is initially defined on the basis of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with search and score. Search and score is then performed following two approaches: Order MCMC, or Partition MCMC. The BGe score is implemented for continuous data and the BDe score is implemented for binary data or categorical data. The algorithms may provide the maximum a posteriori (MAP) graph or a sample (a collection of DAGs) from the posterior distribution given the data. All algorithms are also applicable for structure learning and sampling for dynamic Bayesian networks. References: J. Kuipers, P. Suter, G. Moffa (2022) , N. Friedman and D. Koller (2003) , J. Kuipers and G. Moffa (2017) , M. Kalisch et al. (2012) , D. Geiger and D. Heckerman (2002) , P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) . Package: r-cran-bidistances Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 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-paralleldist, r-cran-datavisualizations, r-cran-diptest, r-cran-e1071, r-cran-vegan, r-cran-pracma, r-cran-ggplot2, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-remotes, r-cran-sphet, r-cran-transport, r-cran-ineq Filename: pool/dists/resolute/main/r-cran-bidistances_0.1.3-1.ca2604.1_arm64.deb Size: 148458 MD5sum: 622a144d3dca59a177d4a56238b32f85 SHA1: f2ee668914c023269baaa28ef8012eda4d4d6577 SHA256: 86cf1735f071411439b6793f273bd439ba28c164576e74b57f3ef4ef8b061b32 SHA512: dda5dbe275ac74da35a9974ae33170230e2854a5e3410325b8b2f1c5adaa0d71fe642d22204fa9ddd65dbbf938df176f8a12683e9bda956fd297590d6a17352a Homepage: https://cran.r-project.org/package=BIDistances Description: CRAN Package 'BIDistances' (Bioinformatic Distances) A selection of distances measures for bioinformatics data. Other important distance measures for bioinformatics data are selected from the R package 'parallelDist'. A special distance measure for the Gene Ontology is available. Package: r-cran-bife Architecture: arm64 Version: 0.7.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 230960 MD5sum: a9e20b8bf067275087bb7901392152de SHA1: 0fb476bb539364d380be8b78e41e0d4f9b93c8fd SHA256: ce28734002e501a2a91e561cd1762efd8578c9fb1cb58808408c2a5069861c9c SHA512: d004759725cdc340539429934945a2c9c8f73ad26591997e4d72ed44b5caaddc2e8d733ccbd0bed81fb15a2905eab5e18e8d6ca8b23cc3cace0f408d196f1eb2 Homepage: https://cran.r-project.org/package=bife Description: CRAN Package 'bife' (Binary Choice Models with Fixed Effects) Estimates fixed effects binary choice models (logit and probit) with potentially many individual fixed effects and computes average partial effects. Incidental parameter bias can be reduced with an asymptotic bias correction proposed by Fernandez-Val (2009) . Package: r-cran-bifiesurvey Architecture: arm64 Version: 3.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2647 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 2157096 MD5sum: e935b77aaf7023a898a428005cae38ac SHA1: 5e0e98c135e2474602ee3feaa3162297eb9077a2 SHA256: 0808c97586ef9edf47f7174bb899bac6f77a116c9866bd006162c39b053e5e88 SHA512: 82dfdc7e2451b1eda6aea4b58e051d8b1270604d1ce9636d1ee1d11952649a733bb8564e63ee33475a7ede5ff17b185a07039065ead3f3805a45b5f62d639f96 Homepage: https://cran.r-project.org/package=BIFIEsurvey Description: CRAN Package 'BIFIEsurvey' (Tools for Survey Statistics in Educational Assessment) Contains tools for survey statistics (especially in educational assessment) for datasets with replication designs (jackknife, bootstrap, replicate weights; see Kolenikov, 2010; Pfefferman & Rao, 2009a, 2009b, , ); Shao, 1996, ). Descriptive statistics, linear and logistic regression, path models for manifest variables with measurement error correction and two-level hierarchical regressions for weighted samples are included. Statistical inference can be conducted for multiply imputed datasets and nested multiply imputed datasets and is in particularly suited for the analysis of plausible values (for details see George, Oberwimmer & Itzlinger-Bruneforth, 2016; Bruneforth, Oberwimmer & Robitzsch, 2016; Robitzsch, Pham & Yanagida, 2016). The package development was supported by BIFIE (Federal Institute for Educational Research, Innovation and Development of the Austrian School System; Salzburg, Austria). Package: r-cran-bigalgebra Architecture: arm64 Version: 3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 346 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigmemory, r-cran-bh, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bigalgebra_3.1.0-1.ca2604.1_arm64.deb Size: 134970 MD5sum: a1bc4cfd82a1362bf23a3f82de7adfff SHA1: 1a38672fdfa9d3f4f06533c824cbd1d41a6fbb32 SHA256: b92ccaf4aca6a88898451a6427d2f7a5e51400181f70eeef4b7ac52e86bfeee4 SHA512: 8fca5263c734c5a329232aa7bdcc989e79e35ecd6f4584884c3bb38d16bf3343fdd4889e7d7ebef35f7585418290d7207b0ac04b95c64184fa27648f4533fe31 Homepage: https://cran.r-project.org/package=bigalgebra Description: CRAN Package 'bigalgebra' ('BLAS' and 'LAPACK' Routines for Native R Matrices and'big.matrix' Objects) Provides arithmetic functions for R matrix and 'big.matrix' objects as well as functions for QR factorization, Cholesky factorization, General eigenvalue, and Singular value decomposition (SVD). A method matrix multiplication and an arithmetic method -for matrix addition, matrix difference- allows for mixed type operation -a matrix class object and a big.matrix class object- and pure type operation for two big.matrix class objects. Package: r-cran-biganalytics Architecture: arm64 Version: 1.1.22-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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_arm64.deb Size: 136258 MD5sum: 9ee6fff143014bc8dafd6d451b3e5855 SHA1: 6121057ed502106d2ec92f15230e81dd48aea1a5 SHA256: 24c83893c3c728185aced1f46a5cf9451846411cd2b9803ec4c65ad1f614de38 SHA512: 78cca329204f58586e885e8e22c4812be87de4c2d0e9abd7e7d5394c27f83ec447ec968c35322bff8e75e155438c71924ea55ed9ee9225feae930e874e203d19 Homepage: https://cran.r-project.org/package=biganalytics Description: CRAN Package 'biganalytics' (Utilities for 'big.matrix' Objects from Package 'bigmemory') Extend the 'bigmemory' package with various analytics. Functions 'bigkmeans' and 'binit' may also be used with native R objects. For 'tapply'-like functions, the bigtabulate package may also be helpful. For linear algebra support, see 'bigalgebra'. For mutex (locking) support for advanced shared-memory usage, see 'synchronicity'. Package: r-cran-bigannoy Architecture: arm64 Version: 0.3.0-1.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_arm64.deb Size: 255484 MD5sum: 1f8f2613de7c5c09b90854b90855642d SHA1: 7693e901ef459449759c079d279c1321ecb18323 SHA256: 29fc439b5cbcd18872631f58e01f9c73fe347aef5ef6e8bf53d2ef610d498f39 SHA512: 28e1ec8a961e8636a530512db51dee211bc6fa3420b266c5a77cf33c152655242ab042a6b39de14930b5a843a52b2c08ceb0093e09fae2ad01211ed6c66a143c Homepage: https://cran.r-project.org/package=bigANNOY Description: CRAN Package 'bigANNOY' (Approximate k-Nearest Neighbour Search for 'bigmemory' Matriceswith Annoy) Approximate Euclidean k-nearest neighbour search routines that operate on 'bigmemory::big.matrix' data through Annoy indexes created with 'RcppAnnoy'. The package builds persistent on-disk indexes plus sidecar metadata from streamed 'big.matrix' rows, supports euclidean, angular, Manhattan, and dot-product Annoy metrics, and can either return in-memory results or stream neighbour indices and distances into destination 'bigmemory' matrices. Explicit index life cycle helpers, stronger metadata validation, descriptor-aware file-backed workflows, and benchmark helpers are also included. Package: r-cran-bigdatadist Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 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_arm64.deb Size: 218610 MD5sum: fada34443b6231d439d56c6f0543315d SHA1: 169405d06a5c0002aaa896ef587f3688247248ac SHA256: 019c4885efc90823f65ec91a4e0db36234f9f892a91af5ae7a75fa037243471a SHA512: 3aa3892c37fe3215532af468bb1c64eb12de85dd764523f70f854a958455de3f172a23ac77be36dc9e882734f2a0e4ef14bae9a829800f124471ecc117fcd9da Homepage: https://cran.r-project.org/package=bigdatadist Description: CRAN Package 'bigdatadist' (Distances for Machine Learning and Statistics in the Context ofBig Data) Functions to compute distances between probability measures or any other data object than can be posed in this way, entropy measures for samples of curves, distances and depth measures for functional data, and the Generalized Mahalanobis Kernel distance for high dimensional data. For further details about the metrics please refer to Martos et al (2014) ; Martos et al (2018) ; Hernandez et al (2018, submitted); Martos et al (2018, submitted). Package: r-cran-bigdatastatmeth Architecture: arm64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12177 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libcurl4t64 (>= 7.16.2), libgcc-s1 (>= 11), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libssl3t64 (>= 3.0.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-rcpp, r-cran-rcurl, r-cran-r6, r-cran-rcppeigen, r-bioc-rhdf5lib Suggests: r-cran-matrix, r-bioc-biocstyle, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-mass Filename: pool/dists/resolute/main/r-cran-bigdatastatmeth_2.0.1-1.ca2604.1_arm64.deb Size: 3870874 MD5sum: b4b85a35862587d07ce6bae130a562b7 SHA1: 16e1b974ed17440dcc77e41e779c1d1696a38a29 SHA256: 0f4394fec5d1b6f5acca63450dc8795c180d88efa94828e66fcff62e01235aaf SHA512: abfaf14e60589f5181d364cb6aaacea40a79542b3bbcf3f3f14a7dc30cc39b7b71bf1e59018d1cdb55460d13a99bf349396f1ffd20c8aeec330e6afcacad9d8c Homepage: https://cran.r-project.org/package=BigDataStatMeth Description: CRAN Package 'BigDataStatMeth' (Title: Scalable Statistical Computing with HDF5-Backed Matrices) A framework for 'scalable' statistical computing on large on-disk matrices stored in 'HDF5' files. It provides efficient block-wise implementations of core linear-algebra operations (matrix multiplication, SVD, PCA, QR decomposition, and canonical correlation analysis) written in C++ and R. These building blocks are designed not only for direct use, but also as foundational components for developing new statistical methods that must operate on datasets too large to fit in memory. The package supports data provided either as 'HDF5' files or standard R objects, and is intended for high-dimensional applications such as 'omics' and precision-medicine research. Package: r-cran-bigergm Architecture: arm64 Version: 1.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2697 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1917480 MD5sum: 99dfd3a9d8ce3660692eab6b83a537ff SHA1: ad6dbd0120307697f3acfacd09482dd1e5b99903 SHA256: 39eb4eddd8fb0bb278ddd2af9dd4a283944ae14f1ab31f4f1a6b9a277ade18c2 SHA512: dae76abe4138acb231a7b5803e4c2000488f733986cd247577846c1e80d9885dad5bfc4901057e7614b7f6cb0c7722b1fbf302e5f3eceba68b0af408ddc7967f 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: arm64 Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1500 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_arm64.deb Size: 1404468 MD5sum: 798c2609ad930b5a80b2c4e2ce4e2c4f SHA1: 1da409f15302f2a74a0825033f2b35b8e998fd28 SHA256: 1076423bf9bf5084337f2e4cdd3f021ae9d1e91b80ae5abe761dd943ffca9e09 SHA512: 98f7ca3ea0ee937af65b2a4aa1a341e3fa2c8c33c0ec8d9e37fe3d110c04879abb1beb124efc7f43cc0af4c07767fd9a4ca0dfbbf8b4a95fde444a870a0c1dfe 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: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 767 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_arm64.deb Size: 271370 MD5sum: 4509d24923faf0156784817e36356f79 SHA1: 25cf21810774846b5fcc8a5e7dfac98def5b9df9 SHA256: 093b3236146221fc52e652fee871c8f8b4dceea0d94906433d1d4363f8c80d6e SHA512: 13bf9ba8cf12d06c0bb96e9b9c689b1b00ae64fe40b0770c9d55f3d0378aadb9492a68e9c44e3cc7224a5a5c1886307dc8c593c79d73e57dd763d4214237c096 Homepage: https://cran.r-project.org/package=bigKNN Description: CRAN Package 'bigKNN' (Exact Search and Graph Construction for 'bigmemory' Matrices) Exact nearest-neighbour and radius-search routines that operate directly on 'bigmemory::big.matrix' objects. 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Package: r-cran-biglasso Architecture: arm64 Version: 1.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1460 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-bigmemory, r-cran-matrix, r-cran-ncvreg, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-glmnet, r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-biglasso_1.6.1-1.ca2604.1_arm64.deb Size: 956254 MD5sum: 2e84abdeba4d690cb9f007a61f2410b6 SHA1: bb47cbf72613e04136ae2c14c86311b1758fee63 SHA256: e6dba4a72979faa4bd0a4f513d58d8b8fe51b1d7d8a28f5d1538929fc15686eb SHA512: f327d53481bda3691cf2ee9eefa1a661d0e9a7afa3e3b802e9460f66d7952a4424875b66ffe7cbf5f6c4b4bad9fff0fe102234806493ba194e6b4e80f2087a35 Homepage: https://cran.r-project.org/package=biglasso Description: CRAN Package 'biglasso' (Extending Lasso Model Fitting to Big Data) Extend lasso and elastic-net model fitting for large data sets that cannot be loaded into memory. Designed to be more memory- and computation-efficient than existing lasso-fitting packages like 'glmnet' and 'ncvreg', thus allowing the user to analyze big data with limited RAM . 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Package: r-cran-biglmm Architecture: arm64 Version: 0.9-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 167 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi Suggests: r-cran-rsqlite, r-cran-rodbc Filename: pool/dists/resolute/main/r-cran-biglmm_0.9-3-1.ca2604.1_arm64.deb Size: 68094 MD5sum: d14ef145bc65b841093b83cfbc60e491 SHA1: e9ceb931dbab905945826aa6d51cb97661d6980f SHA256: 576a6359cf8a4da423b5e58cc8941a14c1d440d5cf5e7e3aafb1ad9dd7ee87ea SHA512: 13039bf93b991c3ae615be354bbdc231222c173291ffbc18c558909f2c16c6d0a7c3473b6f62ddea0724d2fd55cad461595a476850bb20a9e0c0a3bfd8b15ffc Homepage: https://cran.r-project.org/package=biglmm Description: CRAN Package 'biglmm' (Bounded Memory Linear and Generalized Linear Models) Regression for data too large to fit in memory. This package functions exactly like the 'biglm' package, but works with later versions of R. 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Package: r-cran-bigpcacpp Architecture: arm64 Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2349 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 4.2), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1418950 MD5sum: 63eb0a8345ffd206c6f98e4cb02ec146 SHA1: 461156fc0c332aab01aeb78b2fb2f87bcb70a8c1 SHA256: 94fccab14f69ee401c8e7f246c3a91ada7c7dcabc9ed6408adcf163a6ec8ecbc SHA512: 9d4b780feee7c287c46208fd3b9b86530a62c53b5049059228c75ac0f1980dd12971d1a4bea967495cb554daa5738fa66a793ccaa9f6dbfb424118790acdc110 Homepage: https://cran.r-project.org/package=bigPCAcpp Description: CRAN Package 'bigPCAcpp' (Principal Component Analysis for 'bigmemory' Matrices) High performance principal component analysis routines that operate directly on bigmemory::big.matrix() objects. The package avoids materialising large matrices in memory by streaming data through 'BLAS' and 'LAPACK' kernels and provides helpers to derive scores, loadings, correlations, and contribution diagnostics, including utilities that stream results into 'bigmemory'-backed matrices for file-based workflows. Additional interfaces expose 'scalable' singular value decomposition, robust PCA, and robust SVD algorithms so that users can explore large matrices while tempering the influence of outliers. 'Scalable' principal component analysis is also implemented, Elgamal, Yabandeh, Aboulnaga, Mustafa, and Hefeeda (2015) . Package: r-cran-bigplscox Architecture: arm64 Version: 0.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2255 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_arm64.deb Size: 1436576 MD5sum: 456c6511544038399299320d78c149e3 SHA1: 1ff42b814b3e79c002220d94520c3280a27c3a7d SHA256: cb454aeb20a4c45d245bee5a908049a2ee7550898f6385d66419aae36e080ebf SHA512: f2b84540e11aef0a84e022663fcce53d0cf5ec1db583127218206c618bc770d479c8148066bfab35428cbf43f8bc4fb35e653b7bbad6161db5731ee40582e574 Homepage: https://cran.r-project.org/package=bigPLScox Description: CRAN Package 'bigPLScox' (Partial Least Squares for Cox Models with Big Matrices) Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models for big data. Provides a Partial Least Squares (PLS) algorithm adapted to Cox proportional hazards models that works with 'bigmemory' matrices without loading the entire dataset in memory. Also implements a gradient-descent based solver for Cox proportional hazards models that works directly on 'bigmemory' matrices. Bertrand and Maumy (2023) , and highlighted fitting and cross-validating PLS-based Cox models to censored big data. Package: r-cran-bigplsr Architecture: arm64 Version: 0.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4329 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_arm64.deb Size: 2403722 MD5sum: d533101999ff0afd5cb7312ddbeda6ff SHA1: 03bb6ead4882d31fb3a0785368f5ca79c9976a82 SHA256: 89aaba330b53dac485b8f568f9536053e3664debf1a85891f6d6311d5b55961b SHA512: c6eca8c2cf286578a96cfbf582cdac43e7cec20cec142d86d7a254ee6388caa6976bef0113d406625dad34c1da709a0ce59faca03209b676cb5f560f83b64105 Homepage: https://cran.r-project.org/package=bigPLSR Description: CRAN Package 'bigPLSR' (Partial Least Squares Regression Models with Big Matrices) Fast partial least squares (PLS) for dense and out-of-core data. Provides SIMPLS (straightforward implementation of a statistically inspired modification of the PLS method) and NIPALS (non-linear iterative partial least-squares) solvers, plus kernel-style PLS variants ('kernelpls' and 'widekernelpls') with parity to 'pls'. Optimized for 'bigmemory'-backed matrices with streamed cross-products and chunked BLAS (Basic Linear Algebra Subprograms) (XtX/XtY and XXt/YX), optional file-backed score sinks, and deterministic testing helpers. Includes an auto-selection strategy that chooses between XtX SIMPLS, XXt (wide) SIMPLS, and NIPALS based on (n, p) and a configurable memory budget. About the package, Bertrand and Maumy (2023) , and highlighted fitting and cross-validating PLS regression models to big data. For more details about some of the techniques featured in the package, Dayal and MacGregor (1997) , Rosipal & Trejo (2001) , Tenenhaus, Viennet, and Saporta (2007) , Rosipal (2004) , Rosipal (2019) , Song, Wang, and Bai (2024) . Includes kernel logistic PLS with 'C++'-accelerated alternating iteratively reweighted least squares (IRLS) updates, streamed reproducing kernel Hilbert space (RKHS) solvers with reusable centering statistics, and bootstrap diagnostics with graphical summaries for coefficients, scores, and cross-validation workflows, alongside dedicated plotting utilities for individuals, variables, ellipses, and biplots. The streaming backend uses far less memory and keeps memory bounded across data sizes. For PLS1, streaming is often fast enough while preserving a small memory footprint; for PLS2 it remains competitive with a bounded footprint. On small problems that fit comfortably in RAM (random-access memory), dense in-memory solvers are slightly faster; the crossover occurs as n or p grow and the Gram/cross-product cost dominates. Package: r-cran-bigqf Architecture: arm64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 621 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 471870 MD5sum: 39520fce19f4b249f4cb4485717dafb3 SHA1: c63aee72a8298136a367902f4e63ac21691aa6a7 SHA256: 365e8087a09c9fb0525fbe2f4e9319a576f20f719d2868d16c10810955ee07b8 SHA512: 7ef335e520b1135a15028a1988f8403647aec305a4119261138a4c614d0451960f1e5eca78ae52d8bfc971d6fb09033134f7f4d2f9e4ed010d0b7c254e595cf6 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: arm64 Version: 1.1-13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 730 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_arm64.deb Size: 342210 MD5sum: 27c7b61f0d8854265d620bca588d08e7 SHA1: 486b440786a265cd2b5ab0347b5fe4b3541b5254 SHA256: 8a1e0ed470a64361c3630e9fddc6628a9fe3c779ad10aaf49167b4d781d3a708 SHA512: f235a26ae98d322aa0e0588a7d12e889bde0148ea5de0ac90681fb05cf581faba119a0bce6198eeab68c0e40a16b9f50bdd1cba9216462f3f1ecf8cc5b6c9c0c 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: arm64 Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 175808 MD5sum: 21599b56a302ef9b46041bd17068f6f4 SHA1: be79cf22673fb4cc44a4277f293a98c17042bd8e SHA256: f76d64318f1f067fe648cd5a12d1a5a8a6a0c2580d6ca775de36dc838416cf09 SHA512: a786decfbc161fd3f1d75a5affc001c0ac336963ff5aec0a4d5f1827bbe09d2b75f7938e65baff93de3832ae3a27db3411a4b22bee6bcca5fcdad62f02314b3a 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. 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Package: r-cran-bigreg Architecture: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 245378 MD5sum: 9ee325cf3e926d7b94f0793b945eb97b SHA1: 9f7db136b6eb107eb8d4234bb6fdd46593698ed3 SHA256: 47689636a419dd79a9ef31531a8c30128ee8d9c068150cdb6dd0faae640491af SHA512: bab231a1991a53942d4ef306073ce7d19ab38b90c8516e30872eee15187b1fe1de440804a7a785175b81f8d73bf5355c14ba5288c7b4cf64f5efa71b8c932dae 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: arm64 Version: 1.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 806 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_arm64.deb Size: 516990 MD5sum: 01c8a876f10da4cbeef75621daf5b186 SHA1: 7d657356b6293fbbc328f34d772528f3ea7ce99b SHA256: 5699c94ae9da40354609f722c0fdd4a9039d674ac63aa8b68df9aa7b7fea6e0f SHA512: 537b42616c3f1628f08c6effc14aa3f3e1d0906118352cf1a4cbfcf4d53b2273a38872da8507531903c5fa82377c1b3e0223f20041ab1d34e50e5c0327068418 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: arm64 Version: 1.12.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2049 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_arm64.deb Size: 1225890 MD5sum: c4763a90a9f0ce665447b774b1f08043 SHA1: 1c8ce3310989018993ce3f3b1b1ad6e5dbd36c79 SHA256: 59b3923fdea06cac6e87c55536b0da04949c063062e19f9ab0784e96f0b1618f SHA512: e181a72d337f1c9ed6391424220bb1e5c6916b52e468152e5f305cfdbf8d4aabdf37fd22d3411b2eea436715b171f34c067fd213bdd73e682ef46e278937ec56 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: arm64 Version: 0.7.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 536 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_arm64.deb Size: 289152 MD5sum: 5208a6ed784976282c3e1a8320922bf0 SHA1: 9dd32338caf8d2a59c5dfac604529ec4130ec41d SHA256: a13a1572b547b9272f138c934c369d6171d1496f99df04f4b014c57b88eb3caa SHA512: 9dbbab355a78933be9776b6d151da1c5f6e7f3bc1a6cd60ae75aa261ecd5a0dd1bc865888c876c1f5fd9967507de5279d4b3334c6b74d5adf48767ce6e5d96db 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++. 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Package: r-cran-bigsplines Architecture: arm64 Version: 1.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 670 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-quadprog Filename: pool/dists/resolute/main/r-cran-bigsplines_1.1-1-1.ca2604.1_arm64.deb Size: 581600 MD5sum: a8e5c1618e98df64bfa039e86f839f77 SHA1: 2d70591024afad62ebbc13d7112857fd34db004c SHA256: 3e471a66007798f96bb6640a39f31dee87a46147fe598390b2bd8751237e4d25 SHA512: 6197a9b7815fa45cdca3e31e1da03875637df77603b6106bdaed16a2a75949b769173589509fb07e73c547f3e934b9929fd293efd5e9ba0c159cac1db844bfb4 Homepage: https://cran.r-project.org/package=bigsplines Description: CRAN Package 'bigsplines' (Smoothing Splines for Large Samples) Fits smoothing spline regression models using scalable algorithms designed for large samples. Seven marginal spline types are supported: linear, cubic, different cubic, cubic periodic, cubic thin-plate, ordinal, and nominal. 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Most concepts and ideas within this R package are referenced from Sutton and Barto (2018) . The package allows for the intuitive definition of RL models using simple if-else statements and three basic models built into this R package are referenced from Niv et al. (2012) . Our approach to constructing and evaluating these computational models is informed by the guidelines proposed in Wilson & Collins (2019) . Example datasets included with the package are sourced from the work of Mason et al. (2024) . 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Package: r-cran-binpackr Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-testthat, r-cran-hedgehog, r-cran-bbmisc Filename: pool/dists/resolute/main/r-cran-binpackr_0.2.0-1.ca2604.1_arm64.deb Size: 313678 MD5sum: 37d0102e008f57ebdce37079a1bf0cab SHA1: b07cc34cad8f8b9ee63e945a20c61329069c8205 SHA256: 5bcf7809b19a3eeb5fd9499f43978f202cf59bf2b67c36a4d6da45ef916a5936 SHA512: 5c3cc3ee47403fce9b8a3e776746b8ab01e38146c67c1b1f51aef94fcf06356cd4d6936a03bb30adc1b2b7cf71ee773f8c902212221cc56bf9bc095d7c7f24ce Homepage: https://cran.r-project.org/package=binpackr Description: CRAN Package 'binpackr' (Fast 1d Bin Packing) Implements the First Fit Decreasing algorithm to achieve one dimensional heuristic bin packing. Runtime is of order O(n log(n)) where n is the number of items to pack. See "The Art of Computer Programming Vol. 1" by Donald E. Knuth (1997, ISBN: 0201896834) for more details. Package: r-cran-binsegbstrap Architecture: arm64 Version: 1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 451 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), 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-binsegbstrap_1.0-1-1.ca2604.1_arm64.deb Size: 297222 MD5sum: cdad79374bb4f7d3282527c227aefc48 SHA1: c0f06c1b609b661382610238b8166c4b071a0b7d SHA256: 5e2a4614583bd9f6a75e508a137e9650a31b25f6c8ab3aa5ac42c5e2ef6b75b0 SHA512: 48c2290745a292e936f8646605bdcbbda3a43f0bdd47cc0905f6fd6ffa23eed17a0d488351ae06de07cd609757859cb6b3d850327be44c90ed01e8c5f33d5bff Homepage: https://cran.r-project.org/package=BinSegBstrap Description: CRAN Package 'BinSegBstrap' (Piecewise Smooth Regression by Bootstrapped Binary Segmentation) Provides methods for piecewise smooth regression. A piecewise smooth signal is estimated by applying a bootstrapped test recursively (binary segmentation approach). Each bootstrapped test decides whether the underlying signal is smooth on the currently considered subsegment or contains at least one further change-point. Package: r-cran-binsegrcpp Architecture: arm64 Version: 2025.5.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 395 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_arm64.deb Size: 179800 MD5sum: 79c12d56fd54e0e3e5246d1c19435e56 SHA1: 1482bfd9e082ceccca5f9705bbd509854324c2cf SHA256: aadf91f59ebabe3128dfb8b9ecc08562f91f53d387f6fac0b7b6fb6f6ab8c430 SHA512: 8360a782568df4c001e2558b87cc0a8e80c7cee1f416e5eee280461984fa50495847d323ec3e3c4f604919dec8f6676ae0aa65e78c0e864b10579b8e051c6abc Homepage: https://cran.r-project.org/package=binsegRcpp Description: CRAN Package 'binsegRcpp' (Efficient Implementation of Binary Segmentation) Standard template library containers are used to implement an efficient binary segmentation algorithm, which is log-linear on average and quadratic in the worst case. Package: r-cran-binspp Architecture: arm64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1750 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 1428584 MD5sum: 5f64df6be4ff4d43a54e6a12cc0c1714 SHA1: 34cb4cc6facce59c1f92498eaa179cf193a531dc SHA256: 9a1d1b2a6cc004cc16a054f4b1380efd441dc70b5675d2f03cd307073755a198 SHA512: 2e7d09026e3411a1409cc72760685a3f75a5f8bc2e08a07cea15eb0bb212485d2abade9b7d7909591acbbc2ff2dfd483a4b3dc3687f905baafeb24100d787369 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. It allows for inhomogeneity in (i) distribution of parent points, (ii) mean number of points in a cluster, (iii) cluster spread. The package also allows for the Bayesian MCMC algorithm for the homogeneous generalized Thomas process. The cluster size is allowed to have a variance that is greater or less than the expected value (cluster sizes are over or under dispersed). Details are described in Dvořák, Remeš, Beránek & Mrkvička (2022) . Package: r-cran-bintools Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5331 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_arm64.deb Size: 1063444 MD5sum: 2e94e91ed4d9fe08ebd9d2e54e6884de SHA1: 1e0d79a79215aaafc3b3f87bace10c89d34b9148 SHA256: 925201d624462f6d14c0c76c163fa5a22048c9caf45a8de7e94bd34ab1989e05 SHA512: 7bce00ef26c717d6ff6eb20138646e6d5edfef21c2409c3f366b344d4bcfcfb2168dba2d20bcd5506a6c226d80724eace0d25a3a754f67fe2c0a0b8e1efa8b70 Homepage: https://cran.r-project.org/package=BINtools Description: CRAN Package 'BINtools' (Bayesian BIN (Bias, Information, Noise) Model of Forecasting) A recently proposed Bayesian BIN model disentangles the underlying processes that enable forecasters and forecasting methods to improve, decomposing forecasting accuracy into three components: bias, partial information, and noise. By describing the differences between two groups of forecasters, the model allows the user to carry out useful inference, such as calculating the posterior probabilities of the treatment reducing bias, diminishing noise, or increasing information. It also provides insight into how much tamping down bias and noise in judgment or enhancing the efficient extraction of valid information from the environment improves forecasting accuracy. This package provides easy access to the BIN model. For further information refer to the paper Ville A. Satopää, Marat Salikhov, Philip E. Tetlock, and Barbara Mellers (2021) "Bias, Information, Noise: The BIN Model of Forecasting" . Package: r-cran-bio3d Architecture: arm64 Version: 2.4-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3924 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_arm64.deb Size: 2978566 MD5sum: 709af166f9062ac8bbaf387fb8d2f37b SHA1: eb9ba12283d1ec78553174744b1d8a9ef79d6194 SHA256: 2a3474a303e15d85e7a2070639b40db767e5a4127eb3bab05ff37bfa0027cbbc SHA512: f83db90b0237c26c64d6e0f949aa04f67ac0ced19fddc2229a1ca96ee994421537eff88aa9e4756ed96474c8de266598c322884ed63ba9f65ba7be36611789c2 Homepage: https://cran.r-project.org/package=bio3d Description: CRAN Package 'bio3d' (Biological Structure Analysis) Utilities to process, organize and explore protein structure, sequence and dynamics data. Features include the ability to read and write structure, sequence and dynamic trajectory data, perform sequence and structure database searches, data summaries, atom selection, alignment, superposition, rigid core identification, clustering, torsion analysis, distance matrix analysis, structure and sequence conservation analysis, normal mode analysis, principal component analysis of heterogeneous structure data, and correlation network analysis from normal mode and molecular dynamics data. In addition, various utility functions are provided to enable the statistical and graphical power of the R environment to work with biological sequence and structural data. Please refer to the URLs below for more information. Package: r-cran-bioacoustics Architecture: arm64 Version: 0.2.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1670 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 3.0), libstdc++6 (>= 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_arm64.deb Size: 1078314 MD5sum: bda609afeb2f4b4da721e207fffb9099 SHA1: 55f5960a7a9198dab7a1cbfd9bdac982f52a1abb SHA256: 48c427b2374b3a5547d2df2ebc53f4d8b24fdf6f252d03578d30019735710929 SHA512: e1d81934e5004142dcb9c6fda339ad4e33542485b8912a7da7a8c165379f24daffb80b0892a99e5090304c76e9100a9d1792c6b1fa8ecbb5ecfa35044dad0133 Homepage: https://cran.r-project.org/package=bioacoustics Description: CRAN Package 'bioacoustics' (Analyse Audio Recordings and Automatically Extract AnimalVocalizations) Contains all the necessary tools to process audio recordings of various formats (e.g., WAV, WAC, MP3, ZC), filter noisy files, display audio signals, detect and extract automatically acoustic features for further analysis such as classification. Package: r-cran-biocro Architecture: arm64 Version: 3.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3584 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_arm64.deb Size: 2465368 MD5sum: 2c89ef35bd305c121faa1a404d8fcf5b SHA1: feebf7095927ed49ddba4fa9a33d98a1347b6acc SHA256: b5ba5548f78fcbb51cab435c58acb5a7819aa132540c4379266cc04e12bd259f SHA512: ba2808babf9563d06b55757fc74950e950fdbf72574157ecd33287d95c95f741786e6787844e9ade051baea853da5a6fea708ea0f9f8d5fc7af01de6117b122d Homepage: https://cran.r-project.org/package=BioCro Description: CRAN Package 'BioCro' (Modular Crop Growth Simulations) A cross-platform representation of models as sets of equations that facilitates modularity in model building and allows users to harness modern techniques for numerical integration and data visualization. Documentation is provided by several vignettes included in this package; also see Lochocki et al. (2022) . Package: r-cran-bioi Architecture: arm64 Version: 0.2.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: libc6 (>= 2.17), 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-dplyr, r-cran-igraph Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bioi_0.2.10-1.ca2604.1_arm64.deb Size: 80956 MD5sum: f3b37c6286fa84f3db3bc72233006a39 SHA1: d474c8f26d243594230175b26d63a08a0c31d4dd SHA256: f38ac4853672c7f15784168c7c88ef73a071858c0047d7d507bd32214e30ba22 SHA512: fca352c3c9221c2fc88532b99244b8d3e3ebc4b6281c31de1e4e0a781fd6d7b3fc039adb7e5915e714304776bbc82339cf2ed92d1c23a23ba4746cddf163d1e3 Homepage: https://cran.r-project.org/package=Bioi Description: CRAN Package 'Bioi' (Biological Image Analysis) Single linkage clustering and connected component analyses are often performed on biological images. 'Bioi' provides a set of functions for performing these tasks. This functionality is implemented in several key functions that can extend to from 1 to many dimensions. The single linkage clustering method implemented here can be used on n-dimensional data sets, while connected component analyses are limited to 3 or fewer dimensions. Package: r-cran-bioimagetools Architecture: arm64 Version: 1.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3033 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-tiff, r-bioc-ebimage, r-cran-httr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-abind, r-cran-fs, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-bioimagetools_1.1.9-1.ca2604.1_arm64.deb Size: 750790 MD5sum: 69b4b55530c96e334051bf19df940675 SHA1: 8ba851709e858cecbe3e383510f21beef10dc791 SHA256: 545e96d274ce62b92f1e138b0f5e4472506a660569dece664aa2ddd22f6f3ca2 SHA512: 87bb7f0a585846ff73ef4960d4530bbf0c640dec800c30c7d289345ee2dc842cd962bf9d5b0fc6a8d7106351a248da0fee91e9cbc6c0f7da48f00af225e255ca Homepage: https://cran.r-project.org/package=bioimagetools Description: CRAN Package 'bioimagetools' (Tools for Microscopy Imaging) Tools for 3D imaging, mostly for biology/microscopy. Read and write TIFF stacks. Functions for segmentation, filtering and analyzing 3D point patterns. Package: r-cran-biomartr Architecture: arm64 Version: 1.0.7-1.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_arm64.deb Size: 538804 MD5sum: e7451f6650fdd1bdf79d0ec60a35091c SHA1: f7d5f8389878b42a13d5e55916eacbce72a05c14 SHA256: 50a1b174d55120fdf086dfac510e28fa2322c6099bcec8eeab2f8d895d1d6cdb SHA512: 7dae60fbe2140526c68351c1c5be4d0c3409313772bea6e9b8ae80a939eef9dc9215f175480e2bc920167b06b0ec6dd663ca2ff0f1d3a3a27b87c0867c7bff7a Homepage: https://cran.r-project.org/package=biomartr Description: CRAN Package 'biomartr' (Genomic Data Retrieval) Perform large scale genomic data retrieval and functional annotation retrieval. This package aims to provide users with a standardized way to automate genome, proteome, 'RNA', coding sequence ('CDS'), 'GFF', and metagenome retrieval from 'NCBI RefSeq', 'NCBI Genbank', 'ENSEMBL', and 'UniProt' databases. Furthermore, an interface to the 'BioMart' database (Smedley et al. (2009) ) allows users to retrieve functional annotation for genomic loci. In addition, users can download entire databases such as 'NCBI RefSeq' (Pruitt et al. (2007) ), 'NCBI nr', 'NCBI nt', 'NCBI Genbank' (Benson et al. (2013) ), etc. with only one command. Package: r-cran-bioregion Architecture: arm64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7694 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 6227194 MD5sum: 64facac3141dc1fce28bc7bbd5f56ef8 SHA1: 15ca55f728ff0112e08f68dda253d094ba1707d9 SHA256: d1ab9f93bbe974c2c386b139306c4a672d3aad04be9d1b89d0f48a06144eed18 SHA512: 041bd218b7f1ab3c4c3548775df18478270cbee11ed249b173cc88eef1a2d1f05c03c6dbc3c8833888a63755707589c15f450eb51a30ab9c0f0ee514f9e3c726 Homepage: https://cran.r-project.org/package=bioregion Description: CRAN Package 'bioregion' (Comparison of Bioregionalization Methods) The main purpose of this package is to propose a transparent methodological framework to compare bioregionalization methods based on hierarchical and non-hierarchical clustering algorithms (Kreft & Jetz (2010) ) and network algorithms (Lenormand et al. (2019) and Leroy et al. (2019) ). Package: r-cran-biosensors.usc Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5716 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 852378 MD5sum: e0c17492180a5fe2c5deb97d15afb66a SHA1: ed57b145f3d40469e9c61d55afbe3c2ec042a1ea SHA256: 1e8580b8532e243c3dac556f741e1903bd096f31e943e419e440c99516b5e559 SHA512: 5399500aef44e5d75af0a98acc42487218164d466c362f00228b141fc7c33cff7c7261aa3b79ef162e029363d98ec9e8fe5b5d3c19d4770b6f1ed46f86b63940 Homepage: https://cran.r-project.org/package=biosensors.usc Description: CRAN Package 'biosensors.usc' (Distributional Data Analysis Techniques for Biosensor Data) Unified and user-friendly framework for using new distributional representations of biosensors data in different statistical modeling tasks: regression models, hypothesis testing, cluster analysis, visualization, and descriptive analysis. Distributional representations are a functional extension of compositional time-range metrics and we have used them successfully so far in modeling glucose profiles and accelerometer data. However, these functional representations can be used to represent any biosensor data such as ECG or medical imaging such as fMRI. Matabuena M, Petersen A, Vidal JC, Gude F. "Glucodensities: A new representation of glucose profiles using distributional data analysis" (2021) . Package: r-cran-bipartite Architecture: arm64 Version: 2.24-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3988 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sna, r-cran-vegan, r-cran-corpcor, r-cran-fields, r-cran-igraph, r-cran-mass, r-cran-permute Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-bipartite_2.24-1.ca2604.1_arm64.deb Size: 2922264 MD5sum: cccb77071bc1808cb51c38ba3a51c0ce SHA1: a04e7e8fcb9f5be1f862fd7f64319cfd40df8d2c SHA256: 87478655dbd8d89617e7cf73be16e606b8bc34ebb2d965e199b853606c0d45a4 SHA512: 4a235ad0eeb5f60408c2e4c1246fd16802d7d5880a89d1e726fe859032b65f9c83b9ea88b59c845043a799eb7e26b0887621eeecbe67d72ba87b9f18148cdc6b Homepage: https://cran.r-project.org/package=bipartite Description: CRAN Package 'bipartite' (Visualising Bipartite Networks and Calculating Some (Ecological)Indices) Functions to visualise webs and calculate a series of indices commonly used to describe pattern in (ecological) webs. It focuses on webs consisting of only two levels (bipartite), e.g. pollination webs or predator-prey-webs. Visualisation is important to get an idea of what we are actually looking at, while the indices summarise different aspects of the web's topology. Package: r-cran-bipartitemodularitymaximization Architecture: arm64 Version: 1.23.120.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: libc6 (>= 2.17), 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-bipartitemodularitymaximization_1.23.120.1-1.ca2604.1_arm64.deb Size: 49876 MD5sum: 1b7022e21e51d438558d3f82739e86a2 SHA1: 2dbbcbac81281be06346c243db0af1b8d8aa5c89 SHA256: 6c6cf6803483430f973b623ca3b9ff2560238faa3f7a97e522c5db18f343eafd SHA512: 1926c1975576c4bd4d6d17fa1a1aa31d13ad3298c6646a98bd97a14f8d9dd3ec17bb49f72f6e99400e46c54bd7d73d355b134331ba5ecacc37cf405ec8ed39bc Homepage: https://cran.r-project.org/package=BipartiteModularityMaximization Description: CRAN Package 'BipartiteModularityMaximization' (Partition Bipartite Network into Non-Overlapping Biclusters byOptimizing Bipartite Modularity) Function bipmod() that partitions a bipartite network into non-overlapping biclusters by maximizing bipartite modularity defined in Barber (2007) using the bipartite version of the algorithm described in Treviño (2015) . Package: r-cran-biplotez Architecture: arm64 Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4464 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2206026 MD5sum: 0ec84fec8a7271a676dd4b3bc6ac8226 SHA1: 150880b8bf6f861ff19333c6abf2e276f3f6e076 SHA256: ff416c71355a515b3a900bdd6ce4ca3bd12290afe17ea8e8769626a7afa33130 SHA512: 4dba1450ddb2eefac0464fa0aa9892abf2a76def74d62779db474fca1f2eeaff886903112c4dbdd3a95c6e93540e16620b65a8bc6aafbb49f99752ec36df54fd 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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Package: r-cran-bit64 Architecture: arm64 Version: 4.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 743 Depends: libc6 (>= 2.38), r-base-core (>= 4.6.0), r-api-4.0, r-cran-bit Suggests: r-cran-patrick, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-bit64_4.8.2-1.ca2604.1_arm64.deb Size: 556042 MD5sum: a9506fa0370057ec70c030c2a10aaa4c SHA1: 2ea31347162ee4bd027ff9549d141317b7c70b40 SHA256: f3db2c6b54072202f8984eafd33d1e64e4a738b3f3ac91799b7ba90a61e3926e SHA512: 5ca86fe69ed514b6b7979e2ad804e8b5b0e7f24b91d37a15cce292e86ce7bf84f85dddcfa80d5b8b25351eb3cbdce440381d5cfae1d82d881da441a3335a2724 Homepage: https://cran.r-project.org/package=bit64 Description: CRAN Package 'bit64' (A S3 Class for Vectors of 64bit Integers) Package 'bit64' provides serializable S3 atomic 64bit (signed) integers. These are useful for handling database keys and exact counting in +-2^63. 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The package covers: (1) Bivariate Laplace transform of residual lives and stochastic comparisons based on the bivariate Laplace transform order of residual lives (BLt-rl), including weak bivariate hazard rate, mean residual life, and relative mean residual life orders, nonparametric estimation, and NBUHR/NWUHR aging class characterisation; Jayalekshmi, Rajesh, and Nair (2022) "Bivariate Laplace Transform of Residual Lives and Their Properties" ; (2) Bivariate Laplace transform order of reversed residual lives (BLt-Rrl), reversed hazard gradient, reversed mean residual life, and the associated stochastic orders (weak bivariate reversed hazard rate, weak bivariate reversed mean residual life); Jayalekshmi, Rajesh, and Nair (2022) "Bivariate Laplace Transform Order and Ordering of Reversed Residual Lives" ; (3) Univariate Laplace transform of residual life, hazard rate, mean residual life, and the corresponding stochastic orders (Lt-rl order, hazard rate order, MRL order), together with a nonparametric estimator. Shannon entropy and Golomb's (1966) information generating function are also provided. Parametric families supported include the Gumbel bivariate exponential, Farlie-Gumbel-Morgenstern (FGM), bivariate power, and Schur-constant distributions. Plotting utilities and a simulation framework for evaluating estimator performance are also provided. 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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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Package: r-cran-blatent Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 779 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-mnormt, r-cran-r6, r-cran-truncnorm, r-cran-matrix, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-blatent_0.1.3-1.ca2604.1_arm64.deb Size: 530818 MD5sum: 44e0748090e9f67fa3661f98b476230a SHA1: ce8732803ef149572b81794759caf53c315ccb65 SHA256: 5f9264a310f5b00bc79dd8be780ebe3ac5bc4c1008d7778e5842a11a0623f653 SHA512: 7d5b65568a13ddf79bd79e3b8a3a64a2022f01ba8adc8b16e130c3bda8f5632c7a0d7dab0f7b89e3aa872b4c5f587a369dd490b5f8e21e2bff8e55b9d95e21b4 Homepage: https://cran.r-project.org/package=blatent Description: CRAN Package 'blatent' (Bayesian Latent Variable Models) Estimation of latent variable models using Bayesian methods. Currently estimates the loglinear cognitive diagnosis model of Henson, Templin, and Willse (2009) . Package: r-cran-blavaan Architecture: arm64 Version: 0.5-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7881 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-lavaan, r-cran-coda, r-cran-mnormt, r-cran-nonnest2, r-cran-loo, r-cran-rstan, r-cran-rstantools, r-cran-bayesplot, r-cran-matrix, r-cran-future.apply, r-cran-tmvnsim, r-cran-igraph, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-runjags, r-cran-modeest, r-cran-rjags, r-cran-semtools, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-blavaan_0.5-10-1.ca2604.1_arm64.deb Size: 3984708 MD5sum: ed61f22619f859876c1273bbeb351160 SHA1: c8db4b3be812cc46391e5f3cce1b1885da308644 SHA256: 367f6ee4ddb1e61435966e72699adea7b6805052dcfced2223b04dfb955911cd SHA512: 1afc3e6792342c6f825903924f59171d652ceeb7502cb1b92c84608c7bb9f327694a53b42a8112dbd233c9e0d103c9f2e6a1dd847b4286294ba65c0f6ceebeb6 Homepage: https://cran.r-project.org/package=blavaan Description: CRAN Package 'blavaan' (Bayesian Latent Variable Analysis) Fit a variety of Bayesian latent variable models, including confirmatory factor analysis, structural equation models, and latent growth curve models. References: Merkle & Rosseel (2018) ; Merkle et al. (2021) . Package: r-cran-blend Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 690 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 253482 MD5sum: dcde653c7e93acffa97a83b598cfd6e0 SHA1: f0c51a9fc9687d8377925f984df590b1a9bd42ed SHA256: 98021f81e8c28b815fcd468ef80e3fc5b3ab73783bf5ef067d591904ccf7d20e SHA512: a61c20dc948bb843949857803e00b7eecf7350f27b1f7fc6ca3f41b4f72b6915c2368505e63ebf3962c584488def8ab73ae92bb4bcc67857714be1ec5dc81d9f Homepage: https://cran.r-project.org/package=Blend Description: CRAN Package 'Blend' (Robust Bayesian Longitudinal Regularized Semiparametric MixedModels) Our recently developed fully robust Bayesian semiparametric mixed-effect model for high-dimensional longitudinal studies with heterogeneous observations can be implemented through this package. This model can distinguish between time-varying interactions and constant-effect-only cases to avoid model misspecifications. Facilitated by spike-and-slab priors, this model leads to superior performance in estimation, identification and statistical inference. In particular, robust Bayesian inferences in terms of valid Bayesian credible intervals on both parametric and nonparametric effects can be validated on finite samples. The Markov chain Monte Carlo algorithms of the proposed and alternative models are efficiently implemented in 'C++'. Package: r-cran-blindrecalc Architecture: arm64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 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_arm64.deb Size: 184644 MD5sum: ded060ab026a5cb674ff974b0feb7d27 SHA1: 91cae50ba63bacdb3642a66057acf07148e274b7 SHA256: 9c94d2b793f9a0bca54d3bed72c6caf7382b478a1cec30b853c0138299fc5790 SHA512: 6ba609dc4e443663cc9728c84ac18d16db92a07e70db546a3e7966c5cdd2ed657026bf2cbbd01fa0c0ac1df13fcc6896c94936ceb27cf8dbdfe69c4662d5f390 Homepage: https://cran.r-project.org/package=blindrecalc Description: CRAN Package 'blindrecalc' (Blinded Sample Size Recalculation) Computation of key characteristics and plots for blinded sample size recalculation. Continuous as well as binary endpoints are supported in superiority and non-inferiority trials. See Baumann, Pilz, Kieser (2022) for a detailed description. The implemented methods include the approaches by Lu, K. (2016) , Kieser, M. and Friede, T. (2000) , Friede, T. and Kieser, M. (2004) , Friede, T., Mitchell, C., Mueller-Veltern, G. (2007) , and Friede, T. and Kieser, M. (2011) . Package: r-cran-bliss Architecture: arm64 Version: 1.1.1-1.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), 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_arm64.deb Size: 3518598 MD5sum: 18ffb617a998a9c1adf7964a0d6a4699 SHA1: e47ad851bf50bfa878165b606a7f12a323503bb9 SHA256: 3c5dea917a065b0b1814dbf41375399ebd109ecb1026629b617b21b5650d83fc SHA512: 2fa3971c90c4b4eab39bac6b37d1ca3815104cac934a44cab92f6f539430ec1a427042a6081607f9e2ef58d0c519757dcd66a7aa6b3b89053f139a329f9bdb53 Homepage: https://cran.r-project.org/package=bliss Description: CRAN Package 'bliss' (Bayesian Functional Linear Regression with Sparse Step Functions) A method for the Bayesian functional linear regression model (scalar-on-function), including two estimators of the coefficient function and an estimator of its support. A representation of the posterior distribution is also available. Grollemund P-M., Abraham C., Baragatti M., Pudlo P. (2019) . Package: r-cran-blmengineinr Architecture: arm64 Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1304 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 609652 MD5sum: 90d04ad0ee7f07cbb5d411d6de1f9e48 SHA1: ef1693ce32ea435de2136a374a0155cc48af523b SHA256: 3f75c9c5ed650fc16e1f73f9d0023ca17a112dc6eb05243ddb21a949e8b26b5c SHA512: 15e517eec9818e932fe37d988415872b71814d77f99e47214cee297363f393c91cff52eccc20381a6081ef79ed7d01ce89896b34c01dcc4c522bd6a70d96aa49 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: arm64 Version: 3.2-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3076 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2488214 MD5sum: 82f4f728bce5cf47414493fafb5b7a5a SHA1: 3564fb3183b3cb5995492c53a27faa57d1b4226f SHA256: 2be26cc07a7e537290602ec1fe557e967e6cc33dc16469703dfa8baa98d3770a SHA512: 066161aa5b5edf2622dd79e830ec6cdb00f533350945a606d26159ae62d90c20e4a3dc0228d9c53a4bbe752301c7285d5c2198454a74f278fba02cc02e8b7c60 Homepage: https://cran.r-project.org/package=blockCV Description: CRAN Package 'blockCV' (Spatial and Environmental Blocking for K-Fold and LOOCross-Validation) Creating spatially or environmentally separated folds for cross-validation to provide a robust error estimation in spatially structured environments; Investigating and visualising the effective range of spatial autocorrelation in continuous raster covariates and point samples to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019) . Package: r-cran-blockforest Architecture: arm64 Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 846 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_arm64.deb Size: 457274 MD5sum: 4921fdd8b467e4966a542bc606d83ad0 SHA1: 37d91a883240b1976f9a9b0695f86b51bdf02ca2 SHA256: cf4b2718d451f13f63d4e043e771a638d6f37e4e401220c6685ed7445bd0964e SHA512: ec0a0d92ede00a3fffca32d70c77c641d9294095689cf3c8c9fd881918fdd5ce28cc8b36d65e99b7a4af263da0d4c37f1d9d7bb4564d9e1e560312694ef4e485 Homepage: https://cran.r-project.org/package=blockForest Description: CRAN Package 'blockForest' (Block Forests: Random Forests for Blocks of Clinical and OmicsCovariate Data) A random forest variant 'block forest' ('BlockForest') tailored to the prediction of binary, survival and continuous outcomes using block-structured covariate data, for example, clinical covariates plus measurements of a certain omics data type or multi-omics data, that is, data for which measurements of different types of omics data and/or clinical data for each patient exist. Examples of different omics data types include gene expression measurements, mutation data and copy number variation measurements. Block forest are presented in Hornung & Wright (2019). The package includes four other random forest variants for multi-omics data: 'RandomBlock', 'BlockVarSel', 'VarProb', and 'SplitWeights'. These were also considered in Hornung & Wright (2019), but performed worse than block forest in their comparison study based on 20 real multi-omics data sets. Therefore, we recommend to use block forest ('BlockForest') in applications. The other random forest variants can, however, be consulted for academic purposes, for example, in the context of further methodological developments. Reference: Hornung, R. & Wright, M. N. (2019) Block Forests: random forests for blocks of clinical and omics covariate data. BMC Bioinformatics 20:358. . Package: r-cran-blockmodeling Architecture: arm64 Version: 1.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 612 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-sna, r-cran-dorng, r-cran-doparallel, r-cran-foreach Filename: pool/dists/resolute/main/r-cran-blockmodeling_1.1.8-1.ca2604.1_arm64.deb Size: 428968 MD5sum: a896f7fef92a02b849dba7090dcdb5cb SHA1: c9af22e0761fabe4efb21d78c76c7006159fa979 SHA256: f223d8af9d451f279fa0c048a51cda0f5afeb78b85957c8b215d36d175c9a3db SHA512: a58202a594b0ee5b6f7e6306d87a28f98397fb12fee1626309c9d0e7b0a6ccd88f5f55df58e7819fd747c7f6f2765850bd481bd06d333a96a67e53ea73d79b8c Homepage: https://cran.r-project.org/package=blockmodeling Description: CRAN Package 'blockmodeling' (Generalized and Classical Blockmodeling of Valued Networks) This is primarily meant as an implementation of generalized blockmodeling for valued networks. In addition, measures of similarity or dissimilarity based on structural equivalence and regular equivalence (REGE algorithms) can be computed and partitioned matrices can be plotted: Žiberna (2007), Žiberna (2008), Žiberna (2014). 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Various probability distribution are provided (Bernoulli, Poisson...), with or without covariates. Package: r-cran-blocktools Architecture: arm64 Version: 0.6.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 189006 MD5sum: 7eaf6f3b0612d34166ff8079d7bab89a SHA1: 7e6cf5791a2e553b2058b401410b41b9d4328d01 SHA256: e3712aa3b5b39a8bd936a42af2629c116d7774cb9db58eb27a9adcdbc90fc4c9 SHA512: 5718f9e89cad555465ab118e5821b4f9292fecdf432a9202c8ea14b0dece95db2bf55a6685ae254d387f86454997e568e3bb7b2c43427b0ed100b508f9f069e9 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: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 221 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 97072 MD5sum: 9fa4bbd2d8e1b70514c1281b6da35cd5 SHA1: 5c0409c113fc944c12b28900b1987a0931c9a048 SHA256: eafb2d0d8054b40d327c1fec42f4f2328a36c899e3ee88263a9f53997e614f0e SHA512: 7610263560530e09fbba2cf2a45b12a8ae59c9d2239371a42496d3313b7f3b61ea591af3cf2fe3db29e0f0db207761e4d2b762ee1efaa9823c4ca13487f399bf 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. This can be exploited for divisive hierarchical clustering using singular vectors, named HC-SVD. The method is described in Bauer (202Xa) . 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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: arm64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 627 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-blr_1.6-1.ca2604.1_arm64.deb Size: 537056 MD5sum: 78833550ab87a8901217484122add20d SHA1: 7ba5339a2f4e09767ee7c93e59cb4d298880de64 SHA256: 0344548713702f355eaddb3475285a5c23eb0351ac7b226e4cce5c4a158d28b5 SHA512: b9a4158bee89513e7e6fac68ce28ebfafd015e45a64831d277feb405e456281343c19fa007a54ab427bdc92b50a4ac618c2c3d0bdcdb72466095902f39cb87d8 Homepage: https://cran.r-project.org/package=BLR Description: CRAN Package 'BLR' (Bayesian Linear Regression) Bayesian Linear Regression. Package: r-cran-blsm Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 374 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-rgl Filename: pool/dists/resolute/main/r-cran-blsm_0.1.0-1.ca2604.1_arm64.deb Size: 200036 MD5sum: ec9cb09c8c0f04f61567d2b6067d23cb SHA1: eb7f2dc4e861b7bd88e43107b71bd170f43ccdd9 SHA256: 1b9e7dd100847bc6429c64db6be0aa637f02b40c6f7211f8e51b3d7da1d30cac SHA512: 3b618df36ba065d153f791f9d236f17f4a5260b88d3d0302325a9b7a6df44de5bb7344b51142c20844cf58e3755fb673ee83cafab04b933af37c95f7658e34a5 Homepage: https://cran.r-project.org/package=BLSM Description: CRAN Package 'BLSM' (Bayesian Latent Space Model) Provides a Bayesian latent space model for complex networks, either weighted or unweighted. Given an observed input graph, the estimates for the latent coordinates of the nodes are obtained through a Bayesian MCMC algorithm. The overall likelihood of the graph depends on a fundamental probability equation, which is defined so that ties are more likely to exist between nodes whose latent space coordinates are close. The package is mainly based on the model by Hoff, Raftery and Handcock (2002) and contains some extra features (e.g., removal of the Procrustean step, weights implemented as coefficients of the latent distances, 3D plots). The original code related to the above model was retrieved from . Users can inspect the MCMC simulation, create and customize insightful graphical representations or apply clustering techniques. 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Enables programmatic and graphical exploration of the solution space of BLV models when parameters are varied. See Wilson, A. (2008) . Package: r-cran-bma Architecture: arm64 Version: 3.18.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 593 Depends: libc6 (>= 2.27), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-leaps, r-cran-robustbase, r-cran-inline, r-cran-rrcov Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-bma_3.18.21-1.ca2604.1_arm64.deb Size: 400246 MD5sum: 8f5f432cf6685624490d552c07026252 SHA1: b29ccdca8c4cd61ae6a69dc818aa1320eb155a53 SHA256: a16e005a050b3fe363b659298cdf96022a4f5d13479d968ac8b28b0052db81b5 SHA512: b4ba9f183984e6014dbeebbb06da260c077c0984f956a2e314cf5261d3543e40fd9c81af324f7a7bc4efb53367d45b37d89a5dbe4b1422b21fc79b1cb20d4d4b Homepage: https://cran.r-project.org/package=BMA Description: CRAN Package 'BMA' (Bayesian Model Averaging) Package for Bayesian model averaging and variable selection for linear models, generalized linear models and survival models (cox regression). 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Generate (weighted) forecasts for means, variances (volatility) and correlations. Currently DCC(P,Q), CCC(P,Q), pdBEKK(P,Q), and BEKK(P,Q) parameterizations are implemented, alongside a constant covariance baseline (that can be used for testing whether GARCH is warranted), based either on a multivariate gaussian normal or student-t distribution. DCC and CCC models are based on Engle (2002) and Bollerslev (1990). The BEKK parameterization follows Engle and Kroner (1995) while the pdBEKK as well as the estimation approach for this package is described in Rast et al. (2020) . The fitted models contain 'rstan' objects and can be examined with 'rstan' functions. 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Package: r-cran-bmstdr Architecture: arm64 Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6891 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_arm64.deb Size: 3470698 MD5sum: 4df3993770f599b783cfec366cfed8a8 SHA1: 6f363696ea8cb2bf2d4af1ed404a1aeb788dd482 SHA256: e3589993c3de68745efbd90ade1e3dbb48f10c08f12a07e2f294053c3e27ff8f SHA512: 3599f274e0a83eb7d90ece38677b2583273975b77cc7b9778752f4c3fd11f86dc4e07d4a76084cbf60c02a4ef08a750fe12637e00b3d72b00471322e8c336faf 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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Package: r-cran-bnlearn Architecture: arm64 Version: 5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2950 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-bioc-graph, r-bioc-rgraphviz, r-cran-igraph, r-cran-lattice, r-cran-grbase, r-cran-grain, r-cran-rmpfr, r-cran-gmp Filename: pool/dists/resolute/main/r-cran-bnlearn_5.1-1.ca2604.1_arm64.deb Size: 2622660 MD5sum: 4a2dfacc46b37d9cd0aee918297f2ae5 SHA1: b2b62caf992ffc6fa43e9dc72b4cc2339fd78d2c SHA256: 3524032ef4011b112d55bc32886e44859fdc5d4e40768b5ff641111a9e0f5fc3 SHA512: c544ca757151c0e8f509c4d86a33a6aba0d0ddad0da8bef7f84b7f0dd2b18464768233024aa6a664b31b07de7c3bde5869a14b287435452e8bbed803f86a0474 Homepage: https://cran.r-project.org/package=bnlearn Description: CRAN Package 'bnlearn' (Bayesian Network Structure Learning, Parameter Learning andInference) Bayesian network structure learning, parameter learning and inference. This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC, Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete, Gaussian and conditional Gaussian networks, along with many score functions and conditional independence tests. The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented. Some utility functions (model comparison and manipulation, random data generation, arc orientation testing, simple and advanced plots) are included, as well as support for parameter estimation (maximum likelihood and Bayesian) and inference, conditional probability queries, cross-validation, bootstrap and model averaging. Development snapshots with the latest bugfixes are available from . Package: r-cran-bnnsurvival Architecture: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 381 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-prodlim, r-cran-pec, r-cran-rcpp Suggests: r-cran-survival, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bnnsurvival_0.1.5-1.ca2604.1_arm64.deb Size: 201840 MD5sum: 665c5d222d6fb30b5996fa4323f75ba3 SHA1: 155660462fc723732bb5f5467c1e595e6132a032 SHA256: d989956db95053a8fb7b630084336b372355d7e3e81c5b0fcf40123753a42572 SHA512: dae3f61baf4ffc9284f4eb3a9f0a6f5074a922cb7d10257038a2ddce94b7938fbe4cb64b4c0952e97d244033b8e23bee2fe4211548acec367c5fd8f26ca5ac55 Homepage: https://cran.r-project.org/package=bnnSurvival Description: CRAN Package 'bnnSurvival' (Bagged k-Nearest Neighbors Survival Prediction) Implements a bootstrap aggregated (bagged) version of the k-nearest neighbors survival probability prediction method (Lowsky et al. 2013). In addition to the bootstrapping of training samples, the features can be subsampled in each baselearner to break the correlation between them. The Rcpp package is used to speed up the computation. 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See Corradin et al. (2021) for more details. Package: r-cran-bnsl Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bnlearn, r-cran-igraph, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-bnsl_0.1.4-1.ca2604.1_arm64.deb Size: 153612 MD5sum: a4e75da48b5ffe337364a1b1c6e4faaf SHA1: 82b7888e06f283cd9118e19ebe0c81551772a6f4 SHA256: d7419f9ff4457d975c6c72a4da15f83dece0aa3e9584123e2750fefda9237f83 SHA512: ffa0cc05233411b61ecfa1c6f2e4f57479bbe430519a285384d92b4d2b45718ebc7b0cda3e92daf61670053788b86969a3c4baa63977c2d85dd76c236af58aec Homepage: https://cran.r-project.org/package=BNSL Description: CRAN Package 'BNSL' (Bayesian Network Structure Learning) From a given data frame, this package learns its Bayesian network structure based on a selected score. 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Package: r-cran-bnstruct Architecture: arm64 Version: 1.0.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2057 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bitops, r-cran-igraph Suggests: r-bioc-graph, r-bioc-rgraphviz, r-cran-qgraph, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bnstruct_1.0.15-1.ca2604.1_arm64.deb Size: 1239514 MD5sum: 600763b4ec8d0a4e38aa01282d868d23 SHA1: 87d408e15e3c405487b92c390b3d4078fb791259 SHA256: 4c32f58b994003424fde9712ff5bb6447b47f17afc9931718b5c3a5d339d37c7 SHA512: 4329b519760b26da7b459a856c3ab062526e791bf43127ae3edcb57951f3ef9271eadc15c33b20e63f8ab1869ca28df874bd2bf17cbbd902921fbec7f97ac74d Homepage: https://cran.r-project.org/package=bnstruct Description: CRAN Package 'bnstruct' (Bayesian Network Structure Learning from Data with MissingValues) Bayesian Network Structure Learning from Data with Missing Values. 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The package implements Reynolds-style separation, alignment, and cohesion rules with optional obstacles, attractors, predators, species parameters, and reproducible frame export. Simulation state is renderer-neutral; optional adapters can hand frame data to visualization packages such as 'ggWebGL'. The model follows Reynolds (1987) . Package: r-cran-boltzmm Architecture: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 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-bh Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bnstruct Filename: pool/dists/resolute/main/r-cran-boltzmm_0.1.5-1.ca2604.1_arm64.deb Size: 122256 MD5sum: 41dfd2b5230b6a624869ace3c7b28dc6 SHA1: a484d0b080200e7ed5edae7cd387f65e6157eda6 SHA256: e974e2cb5939cc6d5499e9a956b254d4d2f35c806866084769e6721f971baa68 SHA512: fb02f4ffb2786312f535cd81b6ea6f10049ce92a7d7db3fb3395690e09e744c0c8d63ec8d0e9aa43a79cbec2a5cc9f48af03615c2fb04a7ad7e8317ac913f288 Homepage: https://cran.r-project.org/package=BoltzMM Description: CRAN Package 'BoltzMM' (Boltzmann Machines with MM Algorithms) Provides probability computation, data generation, and model estimation for fully-visible Boltzmann machines. 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Package: r-cran-bondvaluation Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 790 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-timedate Filename: pool/dists/resolute/main/r-cran-bondvaluation_0.1.1-1.ca2604.1_arm64.deb Size: 495482 MD5sum: 4d1440d8524144f4db1f39253783dd7e SHA1: fd63340f6760ad465b9a932622fcf6af275242af SHA256: c2cad3f93e8312adea70bb1c806f30194c4edf8cd5e377017e487e6a157fd6a0 SHA512: 3dce4bb603ffe73a60124e9c3ae150542ca86517ab9a18bd2f3783dd1ec216a9928bccd10b69fa50027510cc2e33985c46ace8d85986c40c206217e3956fb720 Homepage: https://cran.r-project.org/package=BondValuation Description: CRAN Package 'BondValuation' (Fixed Coupon Bond Valuation Allowing for Odd Coupon Periods andVarious Day Count Conventions) Analysis of large datasets of fixed coupon bonds, allowing for irregular first and last coupon periods and various day count conventions. 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: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8595 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_arm64.deb Size: 8050482 MD5sum: 5ce4bcd0ec3f8e31d90576cd4637f56b SHA1: c469a386e908f2efbb6531a0c1c1cf8733f7fdf1 SHA256: 3cd77dde1d99105439342693e02494dea7183d6d78cf22124f2d0e841ebe8302 SHA512: ccd5edc91c452c2c22f6d760cf44534455d84377abf8e55f60af2b8e725cff22c874313ba5f628efa95b43364ae091b91318a0fb2381e15e2b8956413ad58893 Homepage: https://cran.r-project.org/package=bonsaiforest Description: CRAN Package 'bonsaiforest' (Shrinkage Based Forest Plots) Subgroup analyses are routinely performed in clinical trial analyses. From a methodological perspective, two key issues of subgroup analyses are multiplicity (even if only predefined subgroups are investigated) and the low sample sizes of subgroups which lead to highly variable estimates, see e.g. Yusuf et al (1991) . This package implements subgroup estimates based on Bayesian shrinkage priors, see Carvalho et al (2019) . In addition, estimates based on penalized likelihood inference are available, based on Simon et al (2011) . The corresponding shrinkage based forest plots address the aforementioned issues and can complement standard forest plots in practical clinical trial analyses. Package: r-cran-boodd Architecture: arm64 Version: 0.1-1.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_arm64.deb Size: 481544 MD5sum: 3d4fa818f94e28c63c26ce6e124fee3d SHA1: 93bf23380b189015e34607d6217d5fc7ea01c3e2 SHA256: 64117c50b8794a9cf11164534d1f86afa165338f3d2da9b7866129f9d569a0fb SHA512: abb98bfa1b0767230a9814c9c103189bd02b7d11c6fa5f2dee7be5cfd2e50b3a1a611062498d06c96b9f29a1af5670d1c9e4885a60ca2e150ce3e84243ce4c4d 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: arm64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 529 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 249852 MD5sum: 173ced1dd4f0de4565fade4a7006ea06 SHA1: f6ddb36027dd6d963314cbdb8c7a17a7f9202f5f SHA256: 420461c66694f8cec3261e6032b95a74923c9a7521395b68d74472137565dbb1 SHA512: d0a9e9c66fbb20f7a5ece83ee15b98d6a8cf69874781623b5fe8dab0a2d47edd2caff80b35581f4a2170403b54780a3ab70d14e5727afdd67b165d7d9ae9ca83 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: arm64 Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 746 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_arm64.deb Size: 379652 MD5sum: 06c3389f45f066b43af5ecb0704257e9 SHA1: 76af7d5332efec2c490b7157bf748668a9032858 SHA256: 7acacf56ef4918f4cafaf99b8e8bfc2e60a4ff4f2f52d8d4bad7d6a3cea1ee48 SHA512: 096b74e34dbfbb4cb1ddcdb3d09111c72981374bfea16e50603eaaa0f6f90ca6e289c7f1bc9ae907075200b8906c3bb1211bd44dd1cbd5f5a859cd949aaac69b 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: arm64 Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 489 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_arm64.deb Size: 182920 MD5sum: 117a0ff8d7f791286aaae874993180f1 SHA1: 2b21230e9b3beac0443bfa54fc663b8b73e7b02d SHA256: 931a9630bbd2f08058b5dcce936a3388e2e8a68e01efbcc0c83ba7f697e86c42 SHA512: 302e00ef807e12493f2f1c2c298700028767a40267e6d4bf3e76c7e218d077d05e8efb9d5610c96dd9a8e2f55d9f38e46b6d5fe88727ae33f24cdc03cf2468cc Homepage: https://cran.r-project.org/package=bpr Description: CRAN Package 'bpr' (Fitting Bayesian Poisson Regression) Posterior sampling and inference for Bayesian Poisson regression models. The model specification makes use of Gaussian (or conditionally Gaussian) prior distributions on the regression coefficients. Details on the algorithm are found in D'Angelo and Canale (2023) . Package: r-cran-bprinstrattte Architecture: arm64 Version: 0.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2149 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_arm64.deb Size: 686522 MD5sum: 3560cc72a1bcc1e7597b3fc7266e3ff3 SHA1: 3c6e2b2bb252bd7d7804ca81dd58f6d3a21b7cbf SHA256: db97f38d2209d83896240c820d15b7912ddc99a98f70083f2a5bf9164acf661d SHA512: 08ff6ab349a6faa81b404d44c221aed18b5a4510d40b60bc1b1452c172d2ce40b9f2cfa58c29b4269be0feff70465005dfc1fc45c6cb32d727a276b9c9b6ac9a Homepage: https://cran.r-project.org/package=BPrinStratTTE Description: CRAN Package 'BPrinStratTTE' (Causal Effects in Principal Strata Defined by AntidrugAntibodies) Bayesian models to estimate causal effects of biological treatments on time-to-event endpoints in clinical trials with principal strata defined by the occurrence of antidrug antibodies. The methodology is based on Frangakis and Rubin (2002) and Imbens and Rubin (1997) , and here adapted to a specific time-to-event setting. Package: r-cran-bpvars Architecture: arm64 Version: 1.0-1.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-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_arm64.deb Size: 1378332 MD5sum: 1ea867b05673bef792f99b21bd98593a SHA1: c945911bbd751cbeeca557bd0e486c0c60fef177 SHA256: c75de0ceb56395078cd19dbadf515877f4577337f647613080a1556b8b221b7b SHA512: e7ed16855d0e942376bb32b5c1b6f8da8a689df9ac4185ba34fe1bb17bba38e9f044486f3f03eba3a2314f42527a34331f220636c8f92f3a5dc98121a6651830 Homepage: https://cran.r-project.org/package=bpvars Description: CRAN Package 'bpvars' (Forecasting with Bayesian Panel Vector Autoregressions) Provides Bayesian estimation and forecasting of dynamic panel data using Bayesian Panel Vector Autoregressions with hierarchical prior distributions. The models include country-specific VARs that share a global prior distribution that extend the model by Jarociński (2010) . Under this prior expected value, each country's system follows a global VAR with country-invariant parameters. Further flexibility is provided by the hierarchical prior structure that retains the Minnesota prior interpretation for the global VAR and features estimated prior covariance matrices, shrinkage, and persistence levels. Bayesian forecasting is developed for models including exogenous variables, allowing conditional forecasts given the future trajectories of some variables and restricted forecasts assuring that rates are forecasted to stay positive and less than 100. The package implements the model specification, estimation, and forecasting routines, facilitating coherent workflows and reproducibility. It also includes automated pseudo-out-of-sample forecasting and computation of forecasting performance measures. Beautiful plots, informative summary functions, and extensive documentation complement all this. An extraordinary computational speed is achieved thanks to employing frontier econometric and numerical techniques and algorithms written in 'C++'. The 'bpvars' package is aligned regarding objects, workflows, and code structure with the 'R' packages 'bsvars' by Woźniak (2024) and 'bsvarSIGNs' by Wang & Woźniak (2025) , and they constitute an integrated toolset. Copyright: 2025 International Labour Organization. Package: r-cran-bqtl Architecture: arm64 Version: 1.0-39-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 592 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-bqtl_1.0-39-1.ca2604.1_arm64.deb Size: 504122 MD5sum: dda01b383cbb3d3a799628a3aa2cfd87 SHA1: fe9f646bc045f8170ef2f40b4d972cfc304a33a9 SHA256: f43e9038e56f71f82913ee91851bc2bb795d8039128a9aa20d2d15e269ce5d75 SHA512: e70766ccaab5f8a185e81600c11f7ad5f1e6a7316bcfac4b230a2e9a95d22d74e3cb79d4e37774772b5fa6b40aba3f0b462ec5bcd787b15daf4f3d587f5b9da0 Homepage: https://cran.r-project.org/package=bqtl Description: CRAN Package 'bqtl' (Bayesian QTL Mapping Toolkit) QTL mapping toolkit for inbred crosses and recombinant inbred lines. Includes maximum likelihood and Bayesian tools. Package: r-cran-braggr Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 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_arm64.deb Size: 46524 MD5sum: 31c59e75c6fa51dfde76eada672eed91 SHA1: 35aa5bcbf8b831742f53f48c1cbde0020791cbde SHA256: 8401a2f32b4d6b3de96317c3bc02ac39b1d5702a9adf3d5db59aa07c43b50fed SHA512: 842f359b2d761b0d84ce3bb0ee868228da86185d44828e9c1873982dea7220f4164d8349d2c6e01212d59701764ada7c7bbc5555550566290c4d84ff90cd82c2 Homepage: https://cran.r-project.org/package=braggR Description: CRAN Package 'braggR' (Calculate the Revealed Aggregator of Probability Predictions) Forecasters predicting the chances of a future event may disagree due to differing evidence or noise. To harness the collective evidence of the crowd, Ville Satopää (2021) "Regularized Aggregation of One-off Probability Predictions" proposes a Bayesian aggregator that is regularized by analyzing the forecasters' disagreement and ascribing over-dispersion to noise. This aggregator requires no user intervention and can be computed efficiently even for a large numbers of predictions. The author evaluates the aggregator on subjective probability predictions collected during a four-year forecasting tournament sponsored by the US intelligence community. The aggregator improves the accuracy of simple averaging by around 20% and other state-of-the-art aggregators by 10-25%. The advantage stems almost exclusively from improved calibration. This aggregator -- know as "the revealed aggregator" -- inputs a) forecasters' probability predictions (p) of a future binary event and b) the forecasters' common prior (p0) of the future event. In this R-package, the function sample_aggregator(p,p0,...) allows the user to calculate the revealed aggregator. Its use is illustrated with a simple example. Package: r-cran-branchglm Architecture: arm64 Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1421 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-rcpparmadillo, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-branchglm_3.0.1-1.ca2604.1_arm64.deb Size: 667756 MD5sum: e16ba9c99cd4a8bcd4b6a64dfb77ac77 SHA1: 49f6fa71212436d2a01dc92e3a490832920e3463 SHA256: 27d3fd65440a5d344468bafc383dcdaf2a9625bd0f2b7af07ca7350e8e1cef65 SHA512: 9124f1b2d738325e2a4641a5f6142ad398161c8b4bfed60e1f22919ab94c0dbc10f0137813ad7dca2cea02611c37433b01b9d2343405d02d428ddc37528d6412 Homepage: https://cran.r-project.org/package=BranchGLM Description: CRAN Package 'BranchGLM' (Efficient Best Subset Selection for GLMs via Branch and BoundAlgorithms) Performs efficient and scalable glm best subset selection using a novel implementation of a branch and bound algorithm. To speed up the model fitting process, a range of optimization methods are implemented in 'RcppArmadillo'. Parallel computation is available using 'OpenMP'. Package: r-cran-branching Architecture: arm64 Version: 0.9.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 Depends: libc6 (>= 2.17), libstdc++6 (>= 4.3), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-branching_0.9.7-1.ca2604.1_arm64.deb Size: 98688 MD5sum: f251b23aaa84a476bf3571cb0ea19670 SHA1: a9ff308785aa03045355fb813b6f95a1f44ecbe6 SHA256: 190effd06e26fd21900e39ad621b93d7f87487c0ff8e9d5f69f89cdfcdff752c SHA512: 6967421ae474e3900fdb61fcee03e8c5dea632b9cf880cfe0352b2092d4cea293f5c3006c695ad19d54871174a3d4688646d6e41b2c8b435aff9a57baf970a93 Homepage: https://cran.r-project.org/package=Branching Description: CRAN Package 'Branching' (Simulation and Estimation for Branching Processes) Simulation and parameter estimation of multitype Bienayme - Galton - Watson processes. Package: r-cran-bravo Architecture: arm64 Version: 3.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), 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-bravo_3.2.2-1.ca2604.1_arm64.deb Size: 153904 MD5sum: c6942ea6d15408a8cda3835359044771 SHA1: 9f8e2136faab3b991739d1a3f12d9e539601e197 SHA256: c15566025ed3fd8c8c4764c060f4a29584a9e9c8ff082b816a41db94efd1baf5 SHA512: c43a657d40b236dfa3292a4d48b62bbb8a6291780a1f80d1a6b6d109c61e7ed9b27f1d67c9843e494e03ef1380ea5719a328e72e3df0702ecc802450d9f54177 Homepage: https://cran.r-project.org/package=bravo Description: CRAN Package 'bravo' (Bayesian Screening and Variable Selection) Performs Bayesian variable screening and selection for ultra-high dimensional linear regression models. Package: r-cran-breakfast Architecture: arm64 Version: 2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1159 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_arm64.deb Size: 341308 MD5sum: 2731a4983e5af79a8008a8152620ab1f SHA1: 4613e6b30501d730b4b45bfa1cc807f0e514373e SHA256: 78bf62bae7bf5b5c1443639f399ab13c55ccb6bea501a3e120250f868cd41f22 SHA512: 4b78c47ae97645927c2389787947a8642331ad24664fac2196a46c537cc56821944615635b108fd97eb9df8640415022465840fed10283410d1d97902f994faa Homepage: https://cran.r-project.org/package=breakfast Description: CRAN Package 'breakfast' (Methods for Fast Multiple Change-Point/Break-Point Detection andEstimation) A developing software suite for multiple change-point and change-point-type feature detection/estimation (data segmentation) in data sequences. Package: r-cran-breathteststan Architecture: arm64 Version: 0.8.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1556 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-breathtestcore, r-cran-dplyr, r-cran-purrr, r-cran-rstan, r-cran-rstantools, r-cran-stringr, r-cran-tidyr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-ggplot2, r-cran-shinystan, r-cran-igraph, r-cran-bayesplot, r-cran-testthat, r-cran-covr, r-cran-knitr, r-cran-parallelly, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-breathteststan_0.8.9-1.ca2604.1_arm64.deb Size: 591522 MD5sum: c0255ec5cbeff065b4b282c8199802c5 SHA1: 75e11a70bda7f5605a967e52b9ce844e67dd4577 SHA256: aa56c003a4e33d9ebe0ad29bfb8d0dd076ece6a2ec5a956ab1914912a53a649b SHA512: 8d256ab72893b1fe6d480cfea71b61d8f83a4eeae919e865f6f5f67c3de2eff238727d81a4edfdc23d111c40c753e77a87b7853b471a0632f91d99f00998a111 Homepage: https://cran.r-project.org/package=breathteststan Description: CRAN Package 'breathteststan' (Stan-Based Fit to Gastric Emptying Curves) Stan-based curve-fitting function for use with package 'breathtestcore' by the same author. Stan functions are refactored here for easier testing. 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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. Package: r-cran-brglm Architecture: arm64 Version: 0.7.3-1.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-profilemodel Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-brglm_0.7.3-1.ca2604.1_arm64.deb Size: 126386 MD5sum: cb3a892725ed24c6c0c7bab7605d5680 SHA1: 3cca5ef352fab0b5e495072932204fd59c24f237 SHA256: 51fb02a2277c964c1e4683d9b6bc4a6b24c4014a31cc463808d62fc139c291ab SHA512: a4c5d4cc345ea6154ed069d536a36bd1028ca454f51dd193ef943fd2595a40b55fded073c63ac51f9c16a687a979c93bfafe84529ec8c89cf736f62d3893a76a Homepage: https://cran.r-project.org/package=brglm Description: CRAN Package 'brglm' (Bias Reduction in Binomial-Response Generalized Linear Models) Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as 'glm'. More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates. 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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: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 213308 MD5sum: 996a4ab5da398ea00563a9156743eaf1 SHA1: 467f155041446e835a5b4f95843c9f6ac55d4793 SHA256: 6b7297ec4619c7c69a9104a6232caa53bd97e38372d0f17500ddfd5202ab1eb4 SHA512: 0adb4afa900c67359e38bd0fa0dd6d88a4d6de3b7cd60da78480c638c232891df02f1d0be4651c386482e8193f7c6374b9b9b504526a6eb7faddc18595a9db2b Homepage: https://cran.r-project.org/package=bsnsing Description: CRAN Package 'bsnsing' (Build Decision Trees with Optimal Multivariate Splits) Functions for training an optimal decision tree classifier, making predictions and generating latex code for plotting. Works for two-class and multi-class classification problems. The algorithm seeks the optimal Boolean rule consisting of multiple variables to split a node, resulting in shorter trees. Use bsnsing() to build a tree, predict() to make predictions and plot() to plot the tree into latex and PDF. See Yanchao Liu (2022) for technical details. Source code and more data sets are at . Package: r-cran-bspbss Architecture: arm64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 694 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 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_arm64.deb Size: 466694 MD5sum: acf1f95af1b2113177d282da23024ea1 SHA1: fe6fd7a2dc0f78af3b23c10c06b956f732212e9e SHA256: 5083045a7929a0a3f56eafab52d1a0bff6308ff2e52a2f5737fe6433cb5e4c4e SHA512: 8fe625a38a80af3dbf9669048f2439d16ffc62732dfbd2118f337011f7b94d38bca4533cd07282876dd6d233d96e0afdf1742edd7decd2f2d7b65a775c6a8691 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: arm64 Version: 2.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 153802 MD5sum: 05c03d4fff556ffb9d4fe1dcafaf2fdf SHA1: 4c3b8bccd3ce3118a4c499b1ed447fcc77f286d9 SHA256: e79db38652385ba0bff0045a2490548a5bd0d016a9df4dfca7520877c7270fa6 SHA512: 284f00f162796e931d4eface12048ef7b6e31a21c162927b6605ed2e940a6e75c4a0f3935ad3c73b56fcbfcf74cf05fc27ae0327adaeb4a42749f7a9f6d8fd39 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: arm64 Version: 1.2-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 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_arm64.deb Size: 184926 MD5sum: 53949894e89851567f8749233ae28331 SHA1: 4a513aa92c1c9f98ee6b590b3e9b11d576261a70 SHA256: cde3c0f7d1aaff1f6fdec23533e873bc96ce9da955f50b5cd9c439f59224b325 SHA512: 661602a4c0d9aef357f9c75ae288f8ef9762625f3951228b16fd0ac8e1e16869e5282db90bf840082601b09f3424fdc4593c8f5bc66e9d5a687ab0e8232e7e86 Homepage: https://cran.r-project.org/package=BSSasymp Description: CRAN Package 'BSSasymp' (Asymptotic Covariance Matrices of Some BSS Mixing and UnmixingMatrix Estimates) Functions to compute the asymptotic covariance matrices of mixing and unmixing matrix estimates of the following blind source separation (BSS) methods: symmetric and squared symmetric FastICA, regular and adaptive deflation-based FastICA, FOBI, JADE, AMUSE and deflation-based and symmetric SOBI. Also functions to estimate these covariances based on data are available. Package: r-cran-bssm Architecture: arm64 Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6754 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_arm64.deb Size: 2729476 MD5sum: 9e7d8d2ba2d3f0896398ae7d866cb051 SHA1: 1633e78e592782cea9a7dd8636c3f875a94872ab SHA256: c032a568b2cc70f22cd78c6daa6fa3cf253fb266c395972d95d0543a6b2cf32a SHA512: 96e3bb565033122a102b71d2182cb62af6f936352033c14c1a8a23d24d1f30afdc593920bbcbf76de0f6f9c2e5f063dd979b813819a1b5e4d6f35bd6e68edece Homepage: https://cran.r-project.org/package=bssm Description: CRAN Package 'bssm' (Bayesian Inference of Non-Linear and Non-Gaussian State SpaceModels) Efficient methods for Bayesian inference of state space models via Markov chain Monte Carlo (MCMC) based on parallel importance sampling type weighted estimators (Vihola, Helske, and Franks, 2020, ), particle MCMC, and its delayed acceptance version. Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and basic stochastic volatility models with linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretised diffusion models are supported. See Helske and Vihola (2021, ) for details. Package: r-cran-bssprep Architecture: arm64 Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 39898 MD5sum: 1e86e1a3fb0cb9b9e5123fdb0b6fea31 SHA1: 8fa5eb2f45aa450ccc6250659ca55f5dacb3d9e0 SHA256: 2d46fd042b205809301ebfe9b69a3f31e5127a82f54f9d9a9021bbb0a4cbcc46 SHA512: 16bfb9e5b54079a2819bbb9651cbb3cfcac814310e633bc814cd751c3bbf4b035513c23bd0e561947da2e13c8028cd4c9d8c9f4eef82aa34f11ec332eada508a Homepage: https://cran.r-project.org/package=BSSprep Description: CRAN Package 'BSSprep' (Whitening Data as Preparation for Blind Source Separation) Whitening is the first step of almost all blind source separation (BSS) methods. A fast implementation of whitening for BSS is implemented to serve as a lightweight dependency for packages providing BSS methods. Package: r-cran-bstfa Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2788 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 2583144 MD5sum: 28ec0bc55dd72a2de574af0c0784c140 SHA1: e4e8f7152a36af3e926c056e8a787f959903474a SHA256: cd40308dd99aa908b80bdb7bae01ffd6fa403abd3924074c04139396b2e0a63c SHA512: 2e1e9f67e4b76091487bf26f1e28e14bcc463fa16a0756a5981fa356d4585f45d92fc594760d65a28e2fa8a1ae33a6f520fd0fc4a14d49ac3f5fbcf0eb7a22e5 Homepage: https://cran.r-project.org/package=BSTFA Description: CRAN Package 'BSTFA' (Bayesian Spatio-Temporal Factor Analysis Model) Implements Bayesian spatio-temporal factor analysis models for multivariate data observed across space and time. The package provides tools for model fitting via Markov chain Monte Carlo (MCMC), spatial and temporal interpolation, and visualization of latent factors and loadings to support inference and exploration of underlying spatio-temporal patterns. Designed for use in environmental, ecological, or public health applications, with support for posterior prediction and uncertainty quantification. Includes functions such as BSTFA() for model fitting and plot_factor() to visualize the latent processes. Functions are based on and extended from methods described in Berrett, et al. (2020) . Package: r-cran-bsts Architecture: arm64 Version: 0.9.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8048 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_arm64.deb Size: 2348320 MD5sum: 10d5f51835ab66046781b1a98608e2e9 SHA1: 0c2d98ec975d434af4ac0bb977224ac983ea6e58 SHA256: 4075dadf33cc4db5ac6f8e1a1c3e1565fbf2902414151dae2c6dac59fb37280e SHA512: 273f334087c07e519f94e946eea2d255b207ded3fc08f85a3c33f4537ac6dd4041e155a4bcf7de943527765ed13e950740c025fbbca397998c59efb9d3b0b9c0 Homepage: https://cran.r-project.org/package=bsts Description: CRAN Package 'bsts' (Bayesian Structural Time Series) Time series regression using dynamic linear models fit using MCMC. See Scott and Varian (2014) , among many other sources. Package: r-cran-bsvars Architecture: arm64 Version: 3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3232 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 2169626 MD5sum: d79d5edd2b27c0ad39860b5cad87c3a9 SHA1: 0e2b7c4ce100a0550cc87cecb8e3fa4e5b0f7ecc SHA256: f20b1948120fb8f898c9e494ef44609b75391e50ed5a34ac6cf97bbbcd08838e SHA512: 49c380a25434bedeb1df663772ab2a4d91e1e4b61c5f1b039f7252974d9a6b805f2a7e1618d6202e13086a1c3168680bd98193dee4d618b96c2d66534d9eb30d Homepage: https://cran.r-project.org/package=bsvars Description: CRAN Package 'bsvars' (Bayesian Estimation of Structural Vector Autoregressive Models) Provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic, and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity, non-normality, and hypotheses on autoregressive parameters, as well as analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation including the vignette by Woźniak (2024) complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2024) , Lütkepohl & Woźniak (2020) , and Song & Woźniak (2021) . The 'bsvars' package is aligned regarding objects, workflows, and code structure with the R package 'bsvarSIGNs' by Wang & Woźniak (2024) , and they constitute an integrated toolset. Package: r-cran-bsvarsigns Architecture: arm64 Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1605 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1036700 MD5sum: 2239ead3503c2960873557790d5b0814 SHA1: 73b44eda8fbab7af98900198b5a223313d1e2d81 SHA256: ef438e79882348e6cc15a7fc565b7c2c3c6d0dc5937476d90610674df013b4a2 SHA512: 6dcfdc792f5ef9457054c836830cf913406ec5e38268f222be13299c1fb6538615c139fb78df1f298fd1445e90e9c351dad3125f6d02ec279f38cc8cfccf53d5 Homepage: https://cran.r-project.org/package=bsvarSIGNs Description: CRAN Package 'bsvarSIGNs' (Bayesian SVARs with Sign, Zero, and Narrative Restrictions) Implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions (SVARs) identified by sign, zero, and narrative restrictions. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors as in Giannone, Lenza, Primiceri (2015) . The sign restrictions are implemented employing the methods proposed by Rubio-Ramírez, Waggoner & Zha (2010) , while identification through sign and zero restrictions follows the approach developed by Arias, Rubio-Ramírez, & Waggoner (2018) . Furthermore, our tool provides algorithms for identification via sign and narrative restrictions, in line with the methods introduced by Antolín-Díaz and Rubio-Ramírez (2018) . Users can also estimate a model with sign, zero, and narrative restrictions imposed at once. The package facilitates predictive and structural analyses using impulse responses, forecast error variance and historical decompositions, forecasting and conditional forecasting, as well as analyses of structural shocks and fitted values. All this is complemented by colourful plots, user-friendly summary functions, and comprehensive documentation including the vignette by Wang & Woźniak (2024) . The 'bsvarSIGNs' package is aligned regarding objects, workflows, and code structure with the R package 'bsvars' by Woźniak (2024) , and they constitute an integrated toolset. It was granted the Di Cook Open-Source Statistical Software Award by the Statistical Society of Australia in 2024. Package: r-cran-bsynth Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9191 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_arm64.deb Size: 2090872 MD5sum: 41c42bd73e289a08e1bf7727afbabfd0 SHA1: 99f761972ac1a1974d619c6f2315914feea44eac SHA256: 722aaf5d8f147560da66047df08381663564922b37a0cf717b55b5b52cfd495c SHA512: 24244f686a6f1b2c6b423477ca979d735b4a8506b0f3290a90ac7bbc5c0f85e0dece1be660926ed85ca9d5c47cc55bc4e36b80341a94a5ec2e4ee3fc542b2e8d Homepage: https://cran.r-project.org/package=bsynth Description: CRAN Package 'bsynth' (Bayesian Synthetic Control) Implements the Bayesian Synthetic Control method for causal inference in comparative case studies. This package provides tools for estimating treatment effects in settings with a single treated unit and multiple control units, allowing for uncertainty quantification and flexible modeling of time-varying effects. The methodology is based on the paper by Vives and Martinez (2022) . Package: r-cran-btb Architecture: arm64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1878 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_arm64.deb Size: 796718 MD5sum: ad03844c8f13d312c43f29d64682b320 SHA1: 619ad9a6226aeb78a43bb3f669333ca44e7d883a SHA256: de721137ba01e808f25884a00e82557171f37f52d197816f00ae9874d133fda3 SHA512: 3c7d14845ee33df96e054d34365123909f3ed2c923ef6486154db3d69fb779585fce2fb94b6c5e2a11bcd4053df2bcaaf725cc822d1c9a37e74089647d3b13b1 Homepage: https://cran.r-project.org/package=btb Description: CRAN Package 'btb' (Beyond the Border - Kernel Density Estimation for UrbanGeography) The kernelSmoothing() function allows you to square and smooth geolocated data. It calculates a classical kernel smoothing (conservative) or a geographically weighted median. There are four major call modes of the function. The first call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth) for a classical kernel smoothing and automatic grid. The second call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles) for a geographically weighted median and automatic grid. The third call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, centroids) for a classical kernel smoothing and user grid. The fourth call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles, centroids) for a geographically weighted median and user grid. Geographically weighted summary statistics : a framework for localised exploratory data analysis, C.Brunsdon & al., in Computers, Environment and Urban Systems C.Brunsdon & al. (2002) , Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition, Diggle, pp. 83-86, (2003) . Package: r-cran-btllasso Architecture: arm64 Version: 0.1-14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 594 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 421822 MD5sum: fb588f01fad4c822894e2efac61d3b1d SHA1: b203e5375218d77ba2b37a96acce5fed1b71d4dc SHA256: 024590ceec54a3b0fd9b9ac3a05a1b1d4433e585b71bc3065d5d298539aaaed5 SHA512: b67f0c0e3709ff3d3de7f5f5f5627cad5717e1720d97f1bb0f8562a52341bcdcd2b49dc8230d371dcb2022cba73384db7b8c466bb97322430019136b1c8f535e 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: arm64 Version: 0.3.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 117392 MD5sum: f83176f8ebbf756eac8826a629253dbd SHA1: e0a6fa2107fbc4efd7f1e4a2fc19af2469402734 SHA256: 96f2551ddeec0266e2bb6bd45f0b2130fb461ad7da237acf403a70ce4567a9bf SHA512: 68fcc7dcd1dde5081df1ca1d5a12711894dee0d04574453df4e3031eff4459cba9c5cdc8c145ffd785d66118f185402fb70e853fb9e8ef21bcfae0fcdc5bbd62 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-btsr Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 756 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack Filename: pool/dists/resolute/main/r-cran-btsr_1.0.2-1.ca2604.1_arm64.deb Size: 537734 MD5sum: 2691a6f9e093781b5c3d93f9ad3ad425 SHA1: a7a6ab25506cf755d6937886d4f4ce5e6ba67cb2 SHA256: 9b678b388813683cce191d8e828aee7ca5e80a7874cd75f9419376e3883700ad SHA512: e12212abb632bf2d3cc5e6173236f9821107392291bb3ecaf40332c9aa757d824ac301ea66c0b84d578d73c49ba6951152a4ee141a58f2d0fa69a738fe00086d Homepage: https://cran.r-project.org/package=BTSR Description: CRAN Package 'BTSR' (Bounded Time Series Regression) Simulate, estimate and forecast a wide range of regression based dynamic models for bounded time series, covering the most commonly applied models in the literature. 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Package: r-cran-buddle Architecture: arm64 Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 203086 MD5sum: 000a58762bc814685b5ece6d63de71e5 SHA1: a3d4736e92213e395a5afbc8878d1f278e3e9126 SHA256: f2ac71469bbb05737dd80c8898a869b2d8a4ab053a267f82b6cf225583c5a12e SHA512: 6f04ce7fb2f5e48a746ccf2a868feaacc13e3a2805cb8aa63b4bddbdd0c3b208f453adf314fdc9b5dc0abdd46890c687c218e8fbb34ee52093a957388934ca3c Homepage: https://cran.r-project.org/package=Buddle Description: CRAN Package 'Buddle' (A Deep Learning for Statistical Classification and RegressionAnalysis with Random Effects) Statistical classification and regression have been popular among various fields and stayed in the limelight of scientists of those fields. Examples of the fields include clinical trials where the statistical classification of patients is indispensable to predict the clinical courses of diseases. Considering the negative impact of diseases on performing daily tasks, correctly classifying patients based on the clinical information is vital in that we need to identify patients of the high-risk group to develop a severe state and arrange medical treatment for them at an opportune moment. Deep learning - a part of artificial intelligence - has gained much attention, and research on it burgeons during past decades: see, e.g, Kazemi and Mirroshandel (2018) . It is a veritable technique which was originally designed for the classification, and hence, the Buddle package can provide sublime solutions to various challenging classification and regression problems encountered in the clinical trials. The Buddle package is based on the back-propagation algorithm - together with various powerful techniques such as batch normalization and dropout - which performs a multi-layer feed-forward neural network: see Krizhevsky et. al (2017) , Schmidhuber (2015) and LeCun et al. (1998) for more details. This package contains two main functions: TrainBuddle() and FetchBuddle(). TrainBuddle() builds a feed-forward neural network model and trains the model. FetchBuddle() recalls the trained model which is the output of TrainBuddle(), classifies or regresses given data, and make a final prediction for the data. Package: r-cran-bunsen Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 361 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-boot, r-cran-clustermq, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-bunsen_0.1.0-1.ca2604.1_arm64.deb Size: 204494 MD5sum: b6a2d1cd4f83805c9aeda29a9e8f4fd4 SHA1: bffd7ccc9c17399a22fdd99958156e8033b048c4 SHA256: f18e3f6e56c1c66e9042d93fc486d07c9426385178510a0a92a2cd0a3f912325 SHA512: 3ddefb8ee7141a7a9fb7c1eaa12e4ec6ff32249fbf6c79b85b7b3a6ee03aba2c188ee4dc4f2b24d9a9c2db749bdfdd65290bda753ff6aa9e157c16622c8043e3 Homepage: https://cran.r-project.org/package=bunsen Description: CRAN Package 'bunsen' (Marginal Survival Estimation with Covariate Adjustment) Provides an efficient and robust implementation for estimating marginal Hazard Ratio (HR) and Restricted Mean Survival Time (RMST) with covariate adjustment using Daniel et al. 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GPC compare two groups of observations (intervention vs. control group) regarding several prioritized endpoints to estimate the probability that a random observation drawn from one group performs better/worse/equivalently than a random observation drawn from the other group. Summary statistics such as the net treatment benefit, win ratio, or win odds are then deduced from these probabilities. Confidence intervals and p-values are obtained based on asymptotic results (Ozenne 2021 ), non-parametric bootstrap, or permutations. The software enables the use of thresholds of minimal importance difference, stratification, non-prioritized endpoints (O Brien test), and can handle right-censoring and competing-risks. Package: r-cran-bvarsv Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 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-bvarsv_1.1-1.ca2604.1_arm64.deb Size: 170208 MD5sum: 781465c12514fe7b13acd9930ecd7e67 SHA1: 40b2ff801ae9c5fe97fad94547f3aee1e38578f3 SHA256: cfa7595ccc53fb7a73ec7402f170a87cb9b035750f0b03d362834ea630a972b0 SHA512: 5da93cb43b4058ae0e072976b5b4e20979f777f04527f4d248f47fc807dd31e8ccae6dd8f3a82bde7e68e4d18927520c94ca995377db2c6215ea95cfcdee36e8 Homepage: https://cran.r-project.org/package=bvarsv Description: CRAN Package 'bvarsv' (Bayesian Analysis of a Vector Autoregressive Model withStochastic Volatility and Time-Varying Parameters) R/C++ implementation of the model proposed by Primiceri ("Time Varying Structural Vector Autoregressions and Monetary Policy", Review of Economic Studies, 2005), with functionality for computing posterior predictive distributions and impulse responses. 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Package: r-cran-bwstest Architecture: arm64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 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_arm64.deb Size: 136022 MD5sum: 9006d1e5b657320d7246591c164a9e7a SHA1: a4b5afb6bc25a45f134212655aaecc77563574fb SHA256: 0fb7ba3d7778945960c2d7f13ef1de3374e3a46832f99a360ce0382d910ec1ae SHA512: 2f1f066ff03c0579aaad69bd0803cb7623662f6191791e74b6a8820f05900c70a7113a81d0c341520cf893a8aca0c1d76e7bdbf3bc51f8ab0034a69a99715d4a 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: arm64 Version: 1.0.8-1.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-bh Filename: pool/dists/resolute/main/r-cran-bzinb_1.0.8-1.ca2604.1_arm64.deb Size: 192628 MD5sum: 7b298f65bb5bd55edbe964d1bf294fcc SHA1: 9ef2f27b2d27ed7bbef9914a607ae8b0418735f3 SHA256: 0465eb0f258133a1d9041e5ad321ba72b734b1820f1bbea6f02566e80a1f8eb1 SHA512: da3347b1a692f9a094882b28e91abbe4ca742fccbfc42e8c0fac9f70493beb53fd29f19a356c2c67bb8df77263c049d036a78d24e9ea825420c9afa4fb8b039f 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: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1261 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_arm64.deb Size: 914514 MD5sum: a25e34a8b31328861ec100d3c81f3818 SHA1: fef663a807c0b6e785289090853db7d49685a7c6 SHA256: 513cf3294a8ad3fc00f6a2e91f1156468e7c6088db71da95d9455725e9be9a8c SHA512: 3ff1fe553a9f67979c3bb181111a53da00e2538f56c845909e1600cdf251c33f6f3c22f95a0d90fa29e4ba17010b1f4ced46a6dc0b7385cd8621019d830d3e97 Homepage: https://cran.r-project.org/package=c212 Description: CRAN Package 'c212' (Methods for Detecting Safety Signals in Clinical Trials UsingBody-Systems (System Organ Classes)) Provides a self-contained set of methods to aid clinical trial safety investigators, statisticians and researchers, in the early detection of adverse events using groupings by body-system or system organ class. This work was supported by the Engineering and Physical Sciences Research Council (UK) (EPSRC) [award reference 1521741] and Frontier Science (Scotland) Ltd. The package title c212 is in reference to the original Engineering and Physical Sciences Research Council (UK) funded project which was named CASE 2/12. Package: r-cran-c3dr Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1967 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-testthat, r-cran-quarto Filename: pool/dists/resolute/main/r-cran-c3dr_0.2.0-1.ca2604.1_arm64.deb Size: 994874 MD5sum: ee8c9606e7f1737de07858164554af4d SHA1: 2611c9fea3f053223f2ed664d562c7aa90403c93 SHA256: 075699f775fbf1dae67c0d439e544ab62a0f841e16bb0aa15837a50a7e795fc6 SHA512: 27eb9db1bc3ff7f2a823cc55bca7011af02e377d0a62513a48f289353d2de1b65b39b2ed903ac8c0d41db11d90fe48489afff63dac012f5d19c42672ca8b7f49 Homepage: https://cran.r-project.org/package=c3dr Description: CRAN Package 'c3dr' (Read and Write C3D Motion Capture Files) A wrapper for the 'EZC3D' library to work with C3D motion capture data. Package: r-cran-c50 Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 592 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_arm64.deb Size: 316346 MD5sum: 0178c58cba27ddd50049667dab344da8 SHA1: edd1affc49f4720add947487d476a6c8b3b13d85 SHA256: 71af6a65ffa8cb8d1af0b38f09f2bc6ddba79a6ca398c92a2e99443e71470a03 SHA512: f78c8941bd56a097b7234e26b22018ac6d4ac03fe4b9c64377992b84e72cdb3642b4686a66d9d8bf6cb8a987abc239a5bb05925e74794b66afae172d7b964271 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: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 68352 MD5sum: 384a3a6758e7e8b53eacf89af761f4a3 SHA1: b5c6f9137ab0f006cf6e59ee547db81b68b799c7 SHA256: ea27b041522aacc8121594c6e4b07a82b3c0938e393e00e74e79afb3bfa67e5b SHA512: 481d3557daad6670c61526f08e40f74b34a579482979fe48dbc8cc595b6b0e5e48d54ecc8128ccfe8dbaa8f171af31a3ece5f92dd0ff10e443ac9fc24ea00f14 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: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2244 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_arm64.deb Size: 1867482 MD5sum: 792a75a525aa04474c79e89d0ee4c339 SHA1: d476171520931009acbf9dc024597f8131aace64 SHA256: 2c82f2ab9042944abdbbd973be2bee2eca566273e38edb57ae316069e6c81f9b SHA512: 0f666fabe07b9681be514b2bc5c3c79c0d9183b00ccd9848c9e225a5c98912d82f1c2f7f6c0733fe03affe0804389d71d6ccee3eeaa2ae668ca7867a5484ccd5 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: arm64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 157932 MD5sum: 8a472cf829e6058f390168cad8db4e5a SHA1: 878709d8178ec63f955ccacb3345e9a0046d9573 SHA256: 31a5a5d1938c5e8365397d37328ab58a93fc9cb5a92cf896b5a562c80953c392 SHA512: 7acb4d8cf508e079d8416a6da0dfe7f9082447d728be738a00ff13c3108b838574be0f2c2ddd4adf5e97635dbeec96c0642d7a403977e43597ee34407cc67dfe 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: arm64 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_arm64.deb Size: 210198 MD5sum: 29805f4708c15cbab9841839cb5b5e49 SHA1: fa07e783ca67ff2dd573f456bbcfdb0fe6c25f56 SHA256: a3f8f223f182a6ced7fe0a071f5651cd7705db20aa7442af797cbf5d18788e2f SHA512: 704cc6921cef46334a81c5e084a5d98d273cacc6c709551859b6ee784d8e86fc7c94d7013ddf8f8c59d78c3067469ca03dfc627c2b62eea8306f8a36013d6493 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) . Package: r-cran-camelup Architecture: arm64 Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1119 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-dplyr, r-cran-ggplot2, r-cran-magrittr, r-cran-rcpp, r-cran-shiny Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-camelup_2.0.3-1.ca2604.1_arm64.deb Size: 381108 MD5sum: 6e16269d5becf3f4d9a1b85f523f8662 SHA1: 749c07645655b5ccd9efa0fd554322305fd84de5 SHA256: f59c992203ec64116e9e9b5af439c78a82b4e8ac327f4796a1a0d3a62b92acc3 SHA512: 5c83407f75634ef7dba255969420b031e82c42f0fff165cc0d9589dc9ec44f0d497158d061bfcec7f9d11411ae250434b9bcc65a0ba32bc96ff3e901893a9b6f Homepage: https://cran.r-project.org/package=CamelUp Description: CRAN Package 'CamelUp' ('CamelUp' Board Game as a Teaching Aid for IntroductoryStatistics) Implements the board game 'CamelUp' for use in introductory statistics classes using a Shiny app. Package: r-cran-capr Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 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 Suggests: r-cran-testthat, r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-capr_0.2.0-1.ca2604.1_arm64.deb Size: 191574 MD5sum: 537a572933de6cd03041a82079188eeb SHA1: 6086ad33df1b608389bea651ef2c4a0be54a74c0 SHA256: 3b11cadd20d8426ea016d57d0511a99dcdf293617de331812595edd10cb3f730 SHA512: f7aed4f9317939de689f88e3f4dcafdfb99608345f8b63bfbe701e9a6c9a815039801e37736cf22e5986d994c7b13faf952973263aec9ba700ebca9c6b10b7aa Homepage: https://cran.r-project.org/package=capr Description: CRAN Package 'capr' (Covariate Assisted Principal Regression) Covariate Assisted Principal Regression (CAPR) for multiple covariance-matrix outcomes. 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" . Package: r-cran-capybara Architecture: arm64 Version: 1.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1165 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-formula, r-cran-generics, r-cran-ggplot2, r-cran-kendallknight, r-cran-mass, r-cran-cpp11, r-cran-cpp11armadillo Suggests: r-cran-broom, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-units Filename: pool/dists/resolute/main/r-cran-capybara_1.8.0-1.ca2604.1_arm64.deb Size: 645688 MD5sum: 249cc96dc47c0aeee47951e0159175fb SHA1: 3ad7414826cf8146212feacf61110bdbf77cdfcb SHA256: ca2886ee29fbae13ed07a3799fe7be6ee6024929a8642218f3ca85441f3c87d3 SHA512: efdabb543a70857f4acd9ac82720335631bb42cb3b066461e715f65825d51db3036b390b85d2a1ba8f3f39c979aaaeb28493899f501f20ac6d4d884fb6c828a7 Homepage: https://cran.r-project.org/package=capybara Description: CRAN Package 'capybara' (Fast and Memory Efficient Fitting of Linear Models withHigh-Dimensional Fixed Effects) Fast and user-friendly estimation of generalized linear models with multiple fixed effects and cluster the standard errors. 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: arm64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5149 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 739158 MD5sum: fd9a42d824001a5ae5af39689e7a1aa6 SHA1: 82a50cac07b16957b3d89f7bcf0d64c659f09686 SHA256: 3e4261450d87c5c34609c3b8298742d093905b7915c339d20b6ca57a53adfa82 SHA512: 3ab891c93f7e3468d5b37f2b0189f86c9fd5d4f8e5935c93529fa7fcf798f043ce0061043daae102d9e713ee94517b9e13a61d3586e70baf07801fce668baa46 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: arm64 Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1227 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_arm64.deb Size: 625994 MD5sum: a2e20e17c0f2a672e97faec54e67328d SHA1: 87a3084d6277aa9d716bc1fe40941a4d55e3c548 SHA256: cafb8638824ff5d7eb98dd84903c1ba2d43f75c73fa299581f579e6f44f93e54 SHA512: b929358fa3f03f6dd1736818952aa840ca67d98b524e5813cf01ccde402cfff2abe2fb5a30f2ca564be94500ec8946895bb3358706212d99a570a9c3d874d78b Homepage: https://cran.r-project.org/package=carat Description: CRAN Package 'carat' (Covariate-Adaptive Randomization for Clinical Trials) Provides functions and command-line user interface to generate allocation sequence by covariate-adaptive randomization for clinical trials. The package currently supports six covariate-adaptive randomization procedures. Three hypothesis testing methods that are valid and robust under covariate-adaptive randomization are also available in the package to facilitate the inference for treatment effect under the included randomization procedures. Additionally, the package provides comprehensive and efficient tools to allow one to evaluate and compare the performance of randomization procedures and tests based on various criteria. See Ma W, Ye X, Tu F, and Hu F (2023) for details. Package: r-cran-carbayes Architecture: arm64 Version: 6.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1618 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_arm64.deb Size: 1339468 MD5sum: 1bac3b8bcc9103b710e4ca99416a2777 SHA1: f089cb0bd58c92f02420aac9507ac8a1cd2c39ed SHA256: f4bd28eb63f8538e468757dc33f108f2a556d4f90c23ae3638e0d0e860f81a08 SHA512: ee1b20d11de63acc928488f2db614ab6a72c6a4f7bcfa98fd2582f49356ee9184af4c3aeb00bc8d03520d0d20b7cbdda4e73b12a663f4aef82ef1d8f8f309fdb Homepage: https://cran.r-project.org/package=CARBayes Description: CRAN Package 'CARBayes' (Spatial Generalised Linear Mixed Models for Areal Unit Data) Implements a class of univariate and multivariate spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation using a single or multiple Markov chains. The response variable can be binomial, Gaussian, multinomial, Poisson or zero-inflated Poisson (ZIP), and spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. A number of different models are available for univariate spatial data, including models with no random effects as well as random effects modelled by different types of CAR prior, including the BYM model (Besag et al., 1991, ) and Leroux model (Leroux et al., 2000, ). Additionally, a multivariate CAR (MCAR) model for multivariate spatial data is available, as is a two-level hierarchical model for modelling data relating to individuals within areas. Full details are given in the vignette accompanying this package. The initial creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256, and on-going development has been supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1, ESRC grant ES/K006460/1, Innovate UK / Natural Environment Research Council (NERC) grant NE/N007352/1 and the TB Alliance. Package: r-cran-carbayesst Architecture: arm64 Version: 4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2773 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_arm64.deb Size: 2216988 MD5sum: 80e4fa03c329ced786510dec1781805f SHA1: 7d234095b0fe36d07b9e990beabaab26cf88c91e SHA256: 85b484894ea3ced69edd70d3f45490ddd1d505863e0e4af8de38a9fd988f0f77 SHA512: 24c6fcfd30071ca68cd62ea8db8f12c7fb7a3a3a6591978f87fa65f008827de306d9ed23a77a64ba0cf0b42fe7abf5a29e05b23a8e443cea6cad35668a5c6194 Homepage: https://cran.r-project.org/package=CARBayesST Description: CRAN Package 'CARBayesST' (Spatio-Temporal Generalised Linear Mixed Models for Areal UnitData) Implements a class of univariate and multivariate spatio-temporal generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian, or Poisson, but for some models only the binomial and Poisson data likelihoods are available. The spatio-temporal autocorrelation is modelled by random effects, which are assigned conditional autoregressive (CAR) style prior distributions. A number of different random effects structures are available, including models similar to Rushworth et al. (2014) . Full details are given in the vignette accompanying this package. The creation and development of this package was supported by the Engineering and Physical Sciences Research Council (EPSRC) grants EP/J017442/1 and EP/T004878/1 and the Medical Research Council (MRC) grant MR/L022184/1. Package: r-cran-carbondate Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2127 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_arm64.deb Size: 1574870 MD5sum: 34fb118672f95b258524a8dd8719734b SHA1: 4af7882f796260aa93a80c87377a2daaf56853f0 SHA256: aa3ead7b2c5583d5d797916a8fe9ded1a201d724f0089d992045a67e2e37132f SHA512: ff014aa570bac27fc89f3f32f79c472143e44b9c60562c9d9d496183a92e30688a20d00deb1aa0a1cc62985110323f9920abde5e650cabeccd88dafcf6534b65 Homepage: https://cran.r-project.org/package=carbondate Description: CRAN Package 'carbondate' (Calibration and Summarisation of Radiocarbon Dates) Performs Bayesian non-parametric calibration of multiple related radiocarbon determinations, and summarises the calendar age information to plot their joint calendar age density (see Heaton (2022) ). Also models the occurrence of radiocarbon samples as a variable-rate (inhomogeneous) Poisson process, plotting the posterior estimate for the occurrence rate of the samples over calendar time, and providing information about potential change points. Package: r-cran-caret Architecture: arm64 Version: 7.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3876 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-lattice, r-cran-e1071, r-cran-foreach, r-cran-modelmetrics, r-cran-nlme, r-cran-plyr, r-cran-proc, r-cran-recipes, r-cran-reshape2, r-cran-withr Suggests: r-cran-bradleyterry2, r-cran-covr, r-cran-cubist, r-cran-dplyr, r-cran-earth, r-cran-ellipse, r-cran-fastica, r-cran-gam, r-cran-ipred, r-cran-kernlab, r-cran-klar, r-cran-knitr, r-cran-mass, r-cran-matrix, r-cran-mda, r-cran-mgcv, r-cran-mlbench, r-cran-mlmetrics, r-cran-nnet, r-cran-pamr, r-cran-party, r-cran-pls, r-cran-proxy, r-cran-randomforest, r-cran-rann, r-cran-rmarkdown, r-cran-rpart, r-cran-spls, r-cran-superpc, r-cran-testthat, r-cran-themis Filename: pool/dists/resolute/main/r-cran-caret_7.0-1-1.ca2604.1_arm64.deb Size: 3566702 MD5sum: aeef00a513a7e4eb60653e66ca90c6ca SHA1: eb3f02d076ba049f04ceda13b207e2faaa0fbf99 SHA256: 53c214feb0fdb9792ecae1fb7874d45e522be50e372ebfd7166005cd41aea3a2 SHA512: efa872989e0a9eed7a89678a8f3368d9932dc382ef9556267bf2c2bff546777684cce3084b71e426fdfba4060fd3677394c3d0fcf365fd3c6ab9985eba93296b Homepage: https://cran.r-project.org/package=caret Description: CRAN Package 'caret' (Classification and Regression Training) Misc functions for training and plotting classification and regression models. Package: r-cran-carlson Architecture: arm64 Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-gsl, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-carlson_3.0.0-1.ca2604.1_arm64.deb Size: 70198 MD5sum: 03b3556f028216dc225fd7d422379d7c SHA1: 1519fd464468079c1771190639e7c9eb6cc689e9 SHA256: 953f4348ce1daa64c136334ba353c88fbadd29d1a9f289a7e6f4339e43ddd4e6 SHA512: 319d251203d2a8a97224025f6ac2f01ec90eaebc88ced3f7d300b76c47a7ee34be7852a18b8b9064167d62e1dc291fe348a5a71fa84f40b0a4451e02460fb43a Homepage: https://cran.r-project.org/package=Carlson Description: CRAN Package 'Carlson' (Carlson Elliptic Integrals and Incomplete Elliptic Integrals) Evaluation of the Carlson elliptic integrals and the incomplete elliptic integrals with complex arguments. The implementations use Carlson's algorithms . Applications of elliptic integrals include probability distributions, geometry, physics, mechanics, electrodynamics, statistical mechanics, astronomy, geodesy, geodesics on conics, and magnetic field calculations. Package: r-cran-carme Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1493 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-mass, r-cran-expm, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Filename: pool/dists/resolute/main/r-cran-carme_0.1.1-1.ca2604.1_arm64.deb Size: 550806 MD5sum: 8c711342c619d9c2c653b2046fd5f05b SHA1: 3020499caff4dcb7d3e3968f2be1e311dc14ef5b SHA256: ced0969d24d7d69186b0b178cdbc1d6102baf1ec4233d415db00eeedf95ce91d SHA512: 3253bc3d90a48c9420d8ef16ab408ea3c9b6c3f43e1597f7abb01fad8ce7163281db1f0127741fbcc93521f30db63f01631daeba66535c7d91fb14aedd18ea2a Homepage: https://cran.r-project.org/package=CARME Description: CRAN Package 'CARME' (CAR-MM Modelling in Stan) 'Stan' based functions to estimate CAR-MM models. These models allow to estimate Generalised Linear Models with CAR (conditional autoregressive) spatial random effects for spatially and temporally misaligned data, provided a suitable Multiple Membership matrix. The main references are Gramatica, Liverani and Congdon (2023) , Petrof, Neyens, Nuyts, Nackaerts, Nemery and Faes (2020) and Gramatica, Congdon and Liverani . Package: r-cran-carms Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 370 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-diagram, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-carms_1.0.1-1.ca2604.1_arm64.deb Size: 113274 MD5sum: 7ef8056ca2df56c85ff2665843a60326 SHA1: ca37eaf1ea4cf02ba5133d09843312dabeca4047 SHA256: 0389addb61dc0b5393a03a816563d932d4d12159bd37c8566bc0008f780643c6 SHA512: d5d241dc3bc23df8fc0018c27bbd540dd25c8ee24aabbf1add2c4a8e97cecfed740727ab8fe1c6d23d1676c6b236cb6eb4f6e5bbca60cf450e640ac5ee7f95bc Homepage: https://cran.r-project.org/package=CARMS Description: CRAN Package 'CARMS' (Continuous Time Markov Rate Modeling for Reliability Analysis) Emulation of an application originally created by Paul Pukite. Computer Aided Rate Modeling and Simulation. Jan Pukite and Paul Pukite, (1998, ISBN 978-0-7803-3482), William J. Stewart, (1994, ISBN: 0-691-03699-3). 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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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The method is described in Welchowski, T. and Zuber, V. and Schmid, M., (2018), Correlation-Adjusted Regression Survival Scores for High-Dimensional Variable Selection, . 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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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For mathematical details and software tutorial, see Mahani and Sharabiani (2019) . 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Package: r-cran-cglasso Architecture: arm64 Version: 2.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 652 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 524616 MD5sum: 1b4da6ca5a85e0538e922dc68f6a304f SHA1: 7a167993627783412111b0aef7fe442b5747cfc4 SHA256: 11ea903c6dbeaf4966a9dceabbd991a55f8b99ee0d60ef2a5aa600f87eef3e09 SHA512: 2d67c72938fa677465a7ce9e946cdba9ef152af2ed870a0a5208bddc7204255bdf559f58fc7d95cc7fee358a77ccea118f5b4f58f5e456845111f1cff0832b59 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: arm64 Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1067 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_arm64.deb Size: 212702 MD5sum: 34675c49b75edcc2d9daaac1a4fb6cf8 SHA1: f0a818ef7ad0d2ebea0743da720729411be7d44c SHA256: 7476bdd76b202198c012b82362ceb911133329c123a85557662d86f60338f89b SHA512: c02ee063867758619b6e01a1d4a2071e00aa275cde7ccfc96aa61bcfa3d74abe3e133f3b07986a8b856487e9471daa8eec75eba8d0507ce5bdf003252c144dfa Homepage: https://cran.r-project.org/package=CGManalyzer Description: CRAN Package 'CGManalyzer' (Continuous Glucose Monitoring Data Analyzer) Contains all of the functions necessary for the complete analysis of a continuous glucose monitoring study and can be applied to data measured by various existing 'CGM' devices such as 'FreeStyle Libre', 'Glutalor', 'Dexcom' and 'Medtronic CGM'. It reads a series of data files, is able to convert various formats of time stamps, can deal with missing values, calculates both regular statistics and nonlinear statistics, and conducts group comparison. It also displays results in a concise format. Also contains two unique features new to 'CGM' analysis: one is the implementation of strictly standard mean difference and the class of effect size; the other is the development of a new type of plot called antenna plot. It corresponds to 'Zhang XD'(2018)'s article 'CGManalyzer: an R package for analyzing continuous glucose monitoring studies'. Package: r-cran-cgmguru Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2097 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_arm64.deb Size: 704528 MD5sum: 1c1059585b6ce51fa31d29bbedb79dc0 SHA1: d1d9c45959030b324fd613a6c1674df53fc6cdc5 SHA256: c5e26a37ab399489fa857e9a9d3ddfff223b57fcde7199160879cdc7b39d7c85 SHA512: cb99f416fb6ddaf8ee65834e31fcddea24678487766f03a9f5f84477e8fecad9f68c1f712f2767189172e01345f2d2d7bd8a84b2f4a4f6c387fae09d2840cd44 Homepage: https://cran.r-project.org/package=cgmguru Description: CRAN Package 'cgmguru' (Advanced Continuous Glucose Monitoring Analysis withHigh-Performance C++ Backend) Tools for advanced analysis of continuous glucose monitoring (CGM) time-series, implementing GRID (Glucose Rate Increase Detector) and GRID-based algorithms for postprandial peak detection, and detection of hypoglycemic and hyperglycemic episodes (Levels 1/2/Extended) aligned with international consensus CGM metrics. Core algorithms are implemented in optimized C++ using 'Rcpp' to provide accurate and fast analysis on large datasets. Package: r-cran-cgvr Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2097 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_arm64.deb Size: 267174 MD5sum: bd38461083c71082e6fc0fdc83ec8c38 SHA1: 63a8f7877bad957c84a934facf67d487bfbf0352 SHA256: 0136ca650295d9787dc5ed2655f7f5d904bddddca985b8a7d8ab71830715ea51 SHA512: 388923d808e8cea756ac3d571e153242ddf05acb630049f7a26867782f011eb02e4344780905d6b397d989a6fccb0ff4d797e197f2ea2de95bed16298b6406b0 Homepage: https://cran.r-project.org/package=cgvR Description: CRAN Package 'cgvR' (Interactive 3D Visualization of Large Cayley Graphs via Vulkan) Provides interactive 3D visualization for large-scale Cayley graphs. Specifically designed for analyzing state spaces of the 'TopSpin' puzzle. Leverages the 'Datoviz' library and Vulkan-based GPU rendering for smooth real-time exploration of large graphs and complex state transitions. Implements efficient coordinate mapping for high-dimensional permutation groups, allowing users to visualize the connectivity and structural properties of the puzzle's state space. The rendering engine provides high-performance visuals and interactive camera controls, making it suitable for mathematical analysis of group-theoretic puzzles within the R environment. Package: r-cran-changepoint.np Architecture: arm64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 222042 MD5sum: 7f4bad841b5be1efa1914c505c913290 SHA1: 17f8c6f4229c711b2b4829d65d5566260e7f2670 SHA256: a9d8e0b2df6f98c2eed64c4153ae7cdec3bad66dfa6d5f4a25cf6363ed690aac SHA512: c2cba2472287ee419155bea51b0ead6caa3b26002fb818610ac5401d1b60f394cf000372252be085d7546bf10f211f6d2591b4a3fc2366038af7a8ed0b128cc3 Homepage: https://cran.r-project.org/package=changepoint.np Description: CRAN Package 'changepoint.np' (Methods for Nonparametric Changepoint Detection) Implements the multiple changepoint algorithm PELT with a nonparametric cost function based on the empirical distribution of the data. This package extends the changepoint package (see Killick, R and Eckley, I (2014) ). Package: r-cran-changepoint Architecture: arm64 Version: 2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 950 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_arm64.deb Size: 754514 MD5sum: 2306f904dfc314e110fc0ed42d83837c SHA1: c6d6a272957d3d258ca48599d213087d09423119 SHA256: ee081acbbaaca81f01fb5e6c162f677d9fdb8b4b290da19be2643d48036b7f6d SHA512: 6b887ef8f2c7d5a0829c22b3efc80956bc14b53b73649e6e6ca97d11a74686575897bd65be47a1d05c0c646ec7c5d4e48f0904a5915069f7b86330982a7df8d7 Homepage: https://cran.r-project.org/package=changepoint Description: CRAN Package 'changepoint' (Methods for Changepoint Detection) Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data. Many popular non-parametric and frequentist methods are included. The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call. Package: r-cran-changepointga Architecture: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1620 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 366486 MD5sum: 2eb648eb3df41db49fda627f07513ed2 SHA1: 75ecc30e0cf20e7fa85b814fedf5d8a5e533062d SHA256: 17e024613ed1aed338dddfd671382124f9ce3f87a1070e17c2f4831fdb39d370 SHA512: 3d6277600a563ebe654a62fcdb89997a0145adfbc0e52871da20d9343f0fdf3a12f7f0d0077d9dbea6fd452e019a27e11e2f8ebf1f9cb53580f94dc26783fde5 Homepage: https://cran.r-project.org/package=changepointGA Description: CRAN Package 'changepointGA' (Changepoint Detection via Modified Genetic Algorithms) The Genetic Algorithm (GA) is used to perform changepoint analysis in time series data. The package also includes an extended island version of GA, as described in Lu, Lund, and Lee (2010, ). By mimicking the principles of natural selection and evolution, GA provides a powerful stochastic search technique for solving combinatorial optimization problems. In 'changepointGA', each chromosome represents a changepoint configuration, including the number and locations of changepoints, hyperparameters, and model parameters. The package employs genetic operators—selection, crossover, and mutation—to iteratively improve solutions based on the given fitness (objective) function. Key features of 'changepointGA' include encoding changepoint configurations in an integer format, enabling dynamic and simultaneous estimation of model hyperparameters, changepoint configurations, and associated parameters. The detailed algorithmic implementation can be found in the package vignettes and in the paper of Li (2024, ). Package: r-cran-changepoints Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 882 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 522018 MD5sum: 0d5507dfc425a43bfa91befaf984892a SHA1: b6ed85eb11dcc20542746a3594cd3b0c8f7d64dd SHA256: 4a148b250e8bdc15bae5bdab22eb3ad074047561ab80dbc4a1ee4985a6fd65d3 SHA512: 1a2e551bfbaa07aea3ce53036d4e0df1562f2f8da32cd9070d176173c43eb9d579bb5ca4662c07ec97dc965c76c43044e3611a5e3ed9f380666280a0f17643be Homepage: https://cran.r-project.org/package=changepoints Description: CRAN Package 'changepoints' (A Collection of Change-Point Detection Methods) Performs a series of offline and/or online change-point detection algorithms for 1) univariate mean: , ; 2) univariate polynomials: ; 3) univariate and multivariate nonparametric settings: , ; 4) high-dimensional covariances: ; 5) high-dimensional networks with and without missing values: , , ; 6) high-dimensional linear regression models: , ; 7) high-dimensional vector autoregressive models: ; 8) high-dimensional self exciting point processes: ; 9) dependent dynamic nonparametric random dot product graphs: ; 10) univariate mean against adversarial attacks: . Package: r-cran-changepointtaylor Architecture: arm64 Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 109730 MD5sum: 76c6eec3dc8960503516258ee2badd35 SHA1: fc23af722142bc0f3c30d9e5dd8d981a1b098e49 SHA256: b83d29944adf3a08211c11b4563b88daaf711c751da0f21cff47c90324af5de4 SHA512: 1a048b62ed1bb313a3141a059eafacbda7ae9a929e35a64824f5eec4dd15bb159f275708249fd99714680a639c091eb3a6d723749cc5f48ab6efa439f94ca43a Homepage: https://cran.r-project.org/package=ChangePointTaylor Description: CRAN Package 'ChangePointTaylor' (Identify Changes in Mean) A basic implementation of the change in mean detection method outlined in: Taylor, Wayne A. (2000) . The package recursively uses the mean-squared error change point calculation to identify candidate change points. The candidate change points are then re-estimated and Taylor's backwards elimination process is then employed to come up with a final set of change points. Many of the underlying functions are written in C++ for improved performance. Package: r-cran-changepointtests Architecture: arm64 Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 61732 MD5sum: ad6696773a26d6f85bc4905dfa007fe7 SHA1: 78aedcb0c74e53bc4a0fb3b9a67c991c0038b1c2 SHA256: a8ecd7d35a07f129512a008ad8850cd156136df0f88424fa29eefcf09da7c932 SHA512: 3aa0be08ae4be845ec31d09a147c740ac910dfd2d7fe25ffb71fcbbd591a731608e3acf198c440947711cde60414e7a1e8de993239af2f9bb91fe97b77ad9474 Homepage: https://cran.r-project.org/package=changepointTests Description: CRAN Package 'changepointTests' (Change Point Tests for Joint Distributions and Copulas) Change point tests for joint distributions and copulas using pseudo-observations with multipliers or bootstrap. The processes used here have been defined in Bucher, Kojadinovic, Rohmer & Segers and Nasri & Remillard . Package: r-cran-channelattribution Architecture: arm64 Version: 2.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-curl, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-channelattribution_2.2.4-1.ca2604.1_arm64.deb Size: 249790 MD5sum: 0f8f7da6f651bd01db4ae40ee37689ed SHA1: 9c5533c4db24a3454c8928dc905b0379b8089d34 SHA256: a93474155eb772e207bcf48d2f00806877fe61fbd0e788dd6f6664de5867a5f8 SHA512: 176bd8dd631bf750ed109979c66d2d521a842c7777522f604a857d5bd3fdc67dd200426de867ff39e9ff6d2902797e98fc3acf8a9f25d4f152acbc3df4c3fa3e Homepage: https://cran.r-project.org/package=ChannelAttribution Description: CRAN Package 'ChannelAttribution' (Markov Model for Online Multi-Channel Attribution) Advertisers use a variety of online marketing channels to reach consumers and they want to know the degree each channel contributes to their marketing success. This is called online multi-channel attribution problem. This package contains a probabilistic algorithm for the attribution problem. The model uses a k-order Markov representation to identify structural correlations in the customer journey data. The package also contains three heuristic algorithms (first-touch, last-touch and linear-touch approach) for the same problem. The algorithms are implemented in C++. Package: r-cran-chaos01 Architecture: arm64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 76212 MD5sum: d6a38e025c8973fc40b0d7f70dc70748 SHA1: 14caff4caa07c4370108c33b4c2792a6a2be40da SHA256: 5deeb620de3fa4272c455acf0bcb534b10ebbccc93fb9e9afe7738d23386c54b SHA512: 0202e91039a0e259f9743484f9956387f0f28a7944034f017b1923cac9d8075ad531811997aaa6ccd1b048180de0f99e161334cfd36df03a2f7257a13b3e0f84 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: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 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_arm64.deb Size: 158248 MD5sum: 82453f85db82190eb5d785783d9857cf SHA1: 64e1fdba580d585297a2a66449679dbba338490b SHA256: f33b81c28852f392c2aa96588c9cf6ed742538e4af64b592498486b9568d3d7c SHA512: e715c3c313ffca6c6c4fb3f883238b95ad72cfaabaf4dcdad25c10b166f58da691c49974c561de15928ee389e470fa922af460f675a6c216d231a05b974b23dd Homepage: https://cran.r-project.org/package=checkglobals Description: CRAN Package 'checkglobals' (Static Analysis of R-Code Dependencies) A minimal R-package to approximately detect global and imported functions or variables from R-source code or R-packages by static code analysis. 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A substantial part of the package was written in C to minimize any worries about execution time overhead. Package: r-cran-cheddar Architecture: arm64 Version: 0.1-639-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2917 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_arm64.deb Size: 1855018 MD5sum: 1f724b280361a1a4f330dfef2904f948 SHA1: d7c23f000d819bdd9515c9845c4ae4607a976ae9 SHA256: 633698d7f1e0e4f75c9e85f14a2877fcfa29345557578d2d6177131daf999d8e SHA512: 7a652fe36f44cb1028650afa4588aa0f9dcff4784f034ad7d4d9c01c58dd26e424f75d689710b3e4eb71470482df2c185b270df69b81894ab2f84c4add2e0490 Homepage: https://cran.r-project.org/package=cheddar Description: CRAN Package 'cheddar' (Analysis and Visualisation of Ecological Communities) Provides a flexible, extendable representation of an ecological community and a range of functions for analysis and visualisation, focusing on food web, body mass and numerical abundance data. Allows inter-web comparisons such as examining changes in community structure over environmental, temporal or spatial gradients. Package: r-cran-chillr Architecture: arm64 Version: 0.77-1.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-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_arm64.deb Size: 1511544 MD5sum: 8fd8b567049edf04114d1d9cbab7b857 SHA1: 8ae14cb83ee59051f8543564a0ce2371ca8e3d6f SHA256: c1fb99b259801203f458e491e0db1818a8470a79a51ba1ef6e96e6055c8222b4 SHA512: ff31c7663c7c202d7f67b98e6c5b184ea09a91c47c899bda9dc3ba20f4cd8c45819d9c9e1442ed27ed7b7e60505ac5a45c00c6bd5c195f6ad892887c6eea39f0 Homepage: https://cran.r-project.org/package=chillR Description: CRAN Package 'chillR' (Statistical Methods for Phenology Analysis in Temperate FruitTrees) The phenology of plants (i.e. the timing of their annual life phases) depends on climatic cues. For temperate trees and many other plants, spring phases, such as leaf emergence and flowering, have been found to result from the effects of both cool (chilling) conditions and heat. Fruit tree scientists (pomologists) have developed some metrics to quantify chilling and heat (e.g. see Luedeling (2012) ). 'chillR' contains functions for processing temperature records into chilling (Chilling Hours, Utah Chill Units and Chill Portions) and heat units (Growing Degree Hours). Regarding chilling metrics, Chill Portions are often considered the most promising, but they are difficult to calculate. This package makes it easy. 'chillR' also contains procedures for conducting a PLS analysis relating phenological dates (e.g. bloom dates) to either mean temperatures or mean chill and heat accumulation rates, based on long-term weather and phenology records (Luedeling and Gassner (2012) ). As of version 0.65, it also includes functions for generating weather scenarios with a weather generator, for conducting climate change analyses for temperature-based climatic metrics and for plotting results from such analyses. Since version 0.70, 'chillR' contains a function for interpolating hourly temperature records. Package: r-cran-chmm Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 204 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 110014 MD5sum: 87ebb5a35e5fa1c782dc1dc689644029 SHA1: fe5b90dee7913053de96b0b0e4f8e89f769ee4d8 SHA256: 33b897789d8d16239dbf5feed0ace119c08127932f06592edd31e3f44fcfd1ab SHA512: 7c41f020aa7bbe3cff26ed4b55d3b20de56e2a6fb73de24a6a3d11afe84442fcb32dfccc3a8a2dfb0861ea208373170c74ca98ae124e5afff1e35d3eefb3a16a Homepage: https://cran.r-project.org/package=CHMM Description: CRAN Package 'CHMM' (Coupled Hidden Markov Models) An exact and a variational inference for coupled Hidden Markov Models applied to the joint detection of copy number variations. Package: r-cran-chngpt Architecture: arm64 Version: 2024.11-15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 848 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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_arm64.deb Size: 578432 MD5sum: 30f230079d5f4d5843d0d02f8f4733fb SHA1: c066e6db8c2589b5265e7498699e45ea11f01ff9 SHA256: 03e2d7297a15e280de2361840669724c907cabd5893bca41637612b59745ade6 SHA512: e5a9639fee1e0baec08673d989c1971706ff71c7f6a84f73461bcb26cff5deeca42b4f2f8aa62c06d4791b69bebb1d5c1e4dc45750b7ee8ad457a89d25462822 Homepage: https://cran.r-project.org/package=chngpt Description: CRAN Package 'chngpt' (Estimation and Hypothesis Testing for Threshold Regression) Threshold regression models are also called two-phase regression, broken-stick regression, split-point regression, structural change models, and regression kink models, with and without interaction terms. Methods for both continuous and discontinuous threshold models are included, but the support for the former is much greater. This package is described in Fong, Huang, Gilbert and Permar (2017) and the package vignette. Package: r-cran-chnosz Architecture: arm64 Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4633 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-knitr, r-cran-rmarkdown, r-cran-tufte, r-cran-canprot Filename: pool/dists/resolute/main/r-cran-chnosz_2.2.0-1.ca2604.1_arm64.deb Size: 2182736 MD5sum: c50c0f6159500c2a4976943a4386b0d1 SHA1: 1c4aabf979f91cf17cf9b013a66ca851e87824e8 SHA256: 56a165c177cf758454312d5a0a4b35994274774b395dd58f70cd36ed709fc7b6 SHA512: 4e8e35efd97b4a65c81868f71d2730764aa126336041b0a1683a0c7a2709945d5d27f3e1a1e5e47e6231173fa0448c5cc6a4811c31fe7910469e85029a9d52ca Homepage: https://cran.r-project.org/package=CHNOSZ Description: CRAN Package 'CHNOSZ' (Thermodynamic Calculations and Diagrams for Geochemistry) An integrated set of tools for thermodynamic calculations in aqueous geochemistry and geobiochemistry. Functions are provided for writing balanced reactions to form species from user-selected basis species and for calculating the standard molal properties of species and reactions, including the standard Gibbs energy and equilibrium constant. Calculations of the non-equilibrium chemical affinity and equilibrium chemical activity of species can be portrayed on diagrams as a function of temperature, pressure, or activity of basis species; in two dimensions, this gives a maximum affinity or predominance diagram. The diagrams have formatted chemical formulas and axis labels, and water stability limits can be added to Eh-pH, oxygen fugacity- temperature, and other diagrams with a redox variable. The package has been developed to handle common calculations in aqueous geochemistry, such as solubility due to complexation of metal ions, mineral buffers of redox or pH, and changing the basis species across a diagram ("mosaic diagrams"). CHNOSZ also implements a group additivity algorithm for the standard thermodynamic properties of proteins. Package: r-cran-choicer Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 818 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-data.table, r-cran-nloptr, r-cran-randtoolbox, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-numderiv, r-cran-future.apply, r-cran-goftest Filename: pool/dists/resolute/main/r-cran-choicer_0.1.0-1.ca2604.1_arm64.deb Size: 469094 MD5sum: 82c5d927ad4aef5cc964203a2ecf1de1 SHA1: 2948b5b72d31bdaf24d1aae18fc7902511818fef SHA256: f864ab0f85110ce68d27a21d6de9809412d9de60ea5e505741fbfaf46e7d882d SHA512: a1d19262a346f270b43098b6ef7c5415c4fc49f96770eaeb9e7bf8501db3ee474ad62f1e0e37dfaf55d9f4bfba2729a09bed1809304bc2235304d59d84c5d7a5 Homepage: https://cran.r-project.org/package=choicer Description: CRAN Package 'choicer' (Discrete Choice Models for Economic Applications) Fast estimation of discrete-choice models for applied economics. 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Package: r-cran-cholwishart Architecture: arm64 Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: libblas3 | libblas.so.3, libc6 (>= 2.23), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), 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_arm64.deb Size: 67490 MD5sum: a4879e43f498cc13fc2825d28372e73f SHA1: 93d68dcf38f877b6c4195389d31207b93be771b8 SHA256: ee3834e46c5433ed31d55459ff5397ad634c73188680dccb7cfc34e22a41866f SHA512: 8b210af3ca272d55c210d0640bf9ec6677d98374218b4d5aa3d0ec52c3f9f96c557e645c4688e3616baffe52774b503f956a4e228ade88069f1948bd20ca7602 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. Package: r-cran-chomper Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1339 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-rcpp, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-blink, r-cran-ggplot2, r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-salso, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-chomper_0.1.3-1.ca2604.1_arm64.deb Size: 791880 MD5sum: a2c2d020f9e13704bbaf0ce9c71b1854 SHA1: 6e630d51c12fbf7f02626116ab1107be26f70807 SHA256: af07c93e15109424864dcd831a625587378443f6dae03a24f8ba9422395e9e15 SHA512: ca0fe10b2b6777cbee38430519ea993968015d06bd3fe581679610354f465b4e195bcbbdf9f8b2b284b6e52e3aa986d9424c94f30cc029c5661fccd701d2db67 Homepage: https://cran.r-project.org/package=chomper Description: CRAN Package 'chomper' (A Comprehensive Hit or Miss Probabilistic Entity ResolutionModel) Provides Bayesian probabilistic methods for record linkage and entity resolution across multiple datasets using the Comprehensive Hit Or Miss Probabilistic Entity Resolution (CHOMPER) model. 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Package: r-cran-chopthin Architecture: arm64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: libc6 (>= 2.17), 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-chopthin_0.2.2-1.ca2604.1_arm64.deb Size: 40304 MD5sum: 9050a362ad00888bc7e7322e72e8d13f SHA1: f13ad61f6f7840280eb900f0ca419d68920221ac SHA256: b5afe662524ba24a47e12dc37f49f6dc77b3ae8b0cdaeab82735142cb76036e4 SHA512: 3d972632ce94ade79fc6e17010e93fc562d84281a90f0d2381478264a266ad9def32aaf21fe6525dc521485b8d7ff6f4cd3d52ad3bd7e2729e9c75e280d2120e Homepage: https://cran.r-project.org/package=chopthin Description: CRAN Package 'chopthin' (The Chopthin Resampler) Resampling is a standard step in particle filtering and in sequential Monte Carlo. 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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: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 28376 MD5sum: b1d51be0335bd494c7d4a353142b2b20 SHA1: c2d022ee00aa4569fbc5e9aed310c6900fcc12c7 SHA256: 036cbe01679f8b654f7c3a0f408a02a142630c6dd9ff768b8d4dcef7c96a34d4 SHA512: 7b12e70b8f6092cc8cb393f02589d3e60e9394456d1243130101610bbd2cfda158feb2d95ac639696e61ca8a46b33436e91553e57a39f2b993a437658e0b6ee6 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: arm64 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_arm64.deb Size: 3054798 MD5sum: fafbaa0e1fd80e2a14c7894fb61f945d SHA1: f716541912c82e0300de3e488c17ede18ed5dfb3 SHA256: cce8e704b3ee6e355acb8a06a75c9c894a9028d82b5fc483db09d537eef0a499 SHA512: 94e2fd17bbf7513d427a63b2b844db64c50971a19db17b1e77c2f0d93380635d1bbdda9cab3e7184b6e1e37db512a8bab6ab5e65dbfe06d8508b76d59f87a3b7 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: arm64 Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 535 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 191990 MD5sum: 72e23ea588aecfb5c91abc6b31c18904 SHA1: 2f11c8ce977cb8acaeb012525d4c16e655028a34 SHA256: 39b7cfb2841713dd1040fe6928a1b59a9af3fd0d4dd862a36169fa0e9a6933b2 SHA512: 9b4e43ed752a5bbd29f6000c915518fe5befe0ff1ff338486d48c296769604d3c681727238356d60917ff2fefcf67f1ec7d86c504207e436cbdbd1381e4416fe Homepage: https://cran.r-project.org/package=cIRT Description: CRAN Package 'cIRT' (Choice Item Response Theory) Jointly model the accuracy of cognitive responses and item choices within a Bayesian hierarchical framework as described by Culpepper and Balamuta (2015) . In addition, the package contains the datasets used within the analysis of the paper. Package: r-cran-cit Architecture: arm64 Version: 2.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 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_arm64.deb Size: 99276 MD5sum: 42a05d0b2261aefc2f7991f199ae902f SHA1: fd82b39d77209b4b40856102f5a81d560e468961 SHA256: 80352df973cc9ed91cb82422efa007bb8eb615bb50877b5d8a62f7823aa650fc SHA512: d126756301338fe7172bcc94b10fd15e95c649f91466c79703735a407196f97558b111960e7c605d300e5039df952e401ba54fd6439da1bc47fd44a62a0fea6f Homepage: https://cran.r-project.org/package=cit Description: CRAN Package 'cit' (Causal Inference Test) A likelihood-based hypothesis testing approach is implemented for assessing causal mediation. Described in Millstein, Chen, and Breton (2016), , it could be used to test for mediation of a known causal association between a DNA variant, the 'instrumental variable', and a clinical outcome or phenotype by gene expression or DNA methylation, the potential mediator. Another example would be testing mediation of the effect of a drug on a clinical outcome by the molecular target. The hypothesis test generates a p-value or permutation-based FDR value with confidence intervals to quantify uncertainty in the causal inference. The outcome can be represented by either a continuous or binary variable, the potential mediator is continuous, and the instrumental variable can be continuous or binary and is not limited to a single variable but may be a design matrix representing multiple variables. Package: r-cran-cklrt Architecture: arm64 Version: 0.2.3-1.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_arm64.deb Size: 301314 MD5sum: e589295bc07805fc45f624c6ea0c40ef SHA1: 7e23f8fc7a5fa0039647e5c9bd45c533c7f9956e SHA256: ed630073e4fadf72143e775dca762c8b9be7d216474e8dda501afc4ca2f82acb SHA512: f258ef8790d94d182e7984c3246c13ccee4ee4a04d81419c0a064296bbe3d8255dcf4e0ef78a5c0fb37fcbe171022a933525d8ae81309908b0789959e74f595c Homepage: https://cran.r-project.org/package=CKLRT Description: CRAN Package 'CKLRT' (Composite Kernel Machine Regression Based on Likelihood RatioTest) Composite Kernel Machine Regression based on Likelihood Ratio Test (CKLRT): in this package, we develop a kernel machine regression framework to model the overall genetic effect of a SNP-set, considering the possible GE interaction. Specifically, we use a composite kernel to specify the overall genetic effect via a nonparametric function and we model additional covariates parametrically within the regression framework. The composite kernel is constructed as a weighted average of two kernels, one corresponding to the genetic main effect and one corresponding to the GE interaction effect. We propose a likelihood ratio test (LRT) and a restricted likelihood ratio test (RLRT) for statistical significance. We derive a Monte Carlo approach for the finite sample distributions of LRT and RLRT statistics. (N. Zhao, H. Zhang, J. Clark, A. Maity, M. Wu. Composite Kernel Machine Regression based on Likelihood Ratio Test with Application for Combined Genetic and Gene-environment Interaction Effect (Submitted).) Package: r-cran-ckmeans.1d.dp Architecture: arm64 Version: 4.3.5-1.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_arm64.deb Size: 591466 MD5sum: b8b6f18895044229ec3de91e36955d17 SHA1: abf46eedf2645cf02b248fc61c7fdf1317495abb SHA256: e62eb66c21c98601c798b039b66c704cab8b0adc867d576e404a0c0fc949c1e6 SHA512: a6d0726ec1ccad9daf0f34bbd86d118bbeeab521628511c2fe9f4e8f876605035605664f5779d7bb7ce4de7c60f0853e813c45ffbc500716ca292e244048fa4a 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: arm64 Version: 0.15.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 174808 MD5sum: ca48cfe4d76435096fbb98cf68601122 SHA1: 47c1b786e028be8bf1353b806f3e556f465319b9 SHA256: 67055f26bef53f62a6567a08b0f78e60aab6d697571c19d8901b2702f8169cba SHA512: 0f35cd8ca3651967d7673ed011b0d0a92c5a8a4a8c865af6fbc4779f380decc083cd83c83761fc61fa57be419f48d7566fa76b556949b102454d961faab04801 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. 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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: arm64 Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 626 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 311132 MD5sum: fcbc593a2d37baeb2c80971dbd408a9e SHA1: c0532b923316629e8e6dd19af32e2b44e6d0e6f6 SHA256: aee610e5426c10e159d5e197c664807b9a6a016fe07b71abdf40669345cf5efe SHA512: 531fef5668763bf22ab7e465fd139304266b349fb966e85030f55dcba85bac36cb3578b326858cfaa294f88f5be1e24e88efa03d832235417476fee50bd619ed 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: arm64 Version: 0.3-68-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1223 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster Suggests: r-cran-e1071, r-cran-lpsolve, r-cran-quadprog, r-cran-relations Filename: pool/dists/resolute/main/r-cran-clue_0.3-68-1.ca2604.1_arm64.deb Size: 985838 MD5sum: 08b2841d2641c59b4381460c86cd8648 SHA1: 8012ad003e2f92cef133b1c4afaf860f3663af74 SHA256: 620aad37dbc78f276f538a329d99a2207cff1ce4c68732b43339fda7554f0fdc SHA512: e01f4414c8bfd1142644953121311dc3c093c04b24b8241c38fe11a322bf778a5b2880b10b549d049b3f4391024a2791dedb92000aaa0371540ba21292f762ae Homepage: https://cran.r-project.org/package=clue Description: CRAN Package 'clue' (Cluster Ensembles) CLUster Ensembles. Package: r-cran-cluspred Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 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_arm64.deb Size: 109340 MD5sum: 3c767cb0f81a2b583b7a0e5d7d19909c SHA1: 7b45ea9b31df69246cf977afb4453a4bcd6e724b SHA256: e27c2f77a4fceee386160d8cfd4d5d7437e05437eb624a20d706c8d6754aa182 SHA512: d7959923cb201dff3f02719633bbd49c9cf402a6f2d06ffe93f29ce73366fe926815af6bc3d53242902a922ac11976bafa088c9e70e90cfa35a4452bcfb68bc5 Homepage: https://cran.r-project.org/package=ClusPred Description: CRAN Package 'ClusPred' (Simultaneous Semi-Parametric Estimation of Clustering andRegression) Parameter estimation of regression models with fixed group effects, when the group variable is missing while group-related variables are available. Parametric and semi-parametric approaches described in Marbac et al. (2020) are implemented. Package: r-cran-clusrank Architecture: arm64 Version: 1.0-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 381 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_arm64.deb Size: 201644 MD5sum: 1b46150a2101c770c4c421f35b703d67 SHA1: 5b97c13888564dd7de65583051c8faeae2de22c8 SHA256: 6358ddf267167af57288481cc4951f745b1109709f41e69bb96c10b2bb73364a SHA512: ce81b148068bbdaf4b36953b958f6218d7cfbacc2599564644a8d6636b1b39c422824ea00ccc96313f85f97f8fecc87acd46a83adfacce45be77a6fc40315a1f Homepage: https://cran.r-project.org/package=clusrank Description: CRAN Package 'clusrank' (Wilcoxon Rank Tests for Clustered Data) Non-parametric tests (Wilcoxon rank sum test and Wilcoxon signed rank test) for clustered data documented in Jiang et. al (2020) . Package: r-cran-clusroc Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 437 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 295488 MD5sum: dd885ab07525b1873e782544ad16dc6a SHA1: f0aa69e4f6a55b0a81b27f4648240697eff60d46 SHA256: 15308b045e78ced304ef7841da639a68892cc2aa38cb2563e5d8940d23a7d136 SHA512: 68a6eed40f179936b9b64f73baf8e5cfea40df3527c18cfdeaed88370391c25a8dcdd565f67b3719b1d9512b3df75be09d70816e0d57da515a24c589d0eb4304 Homepage: https://cran.r-project.org/package=ClusROC Description: CRAN Package 'ClusROC' (ROC Analysis in Three-Class Classification Problems forClustered Data) Statistical methods for ROC surface analysis in three-class classification problems for clustered data and in presence of covariates. In particular, the package allows to obtain covariate-specific point and interval estimation for: (i) true class fractions (TCFs) at fixed pairs of thresholds; (ii) the ROC surface; (iii) the volume under ROC surface (VUS); (iv) the optimal pairs of thresholds. Methods considered in points (i), (ii) and (iv) are proposed and discussed in To et al. (2022) . Referring to point (iv), three different selection criteria are implemented: Generalized Youden Index (GYI), Closest to Perfection (CtP) and Maximum Volume (MV). Methods considered in point (iii) are proposed and discussed in Xiong et al. (2018) . Visualization tools are also provided. We refer readers to the articles cited above for all details. 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Package: r-cran-clustassess Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1275 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1070214 MD5sum: d06728e018f72d38496acd52957c5115 SHA1: bf690c33e29b3f255a2d7326b4b7e8c8236068af SHA256: c0b947d38572a31b9211441c5638cb5faeb6024c3f247e9110a83f07ea87e616 SHA512: abd5e3b692bd783ff38fd3add0d0abad290138bc13da47471cb14695dc34214365a6e1c0e759d0518d54e613a7b11440281fc60ed6ff398e90d7fbaf2a5b8552 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: arm64 Version: 2.1.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 754 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 568208 MD5sum: cb12bc33f2359fb1f7875b0050c9d3df SHA1: 3e189b63bd1dcf0fda69de37db9bcf6efb1872bf SHA256: 7087a31befe7ddfd3066d27e7b1f91965ffc72a74502409cb769a793165ac056 SHA512: c048a86dc085f840aea46313e19b0823d7ab3533f182d66715b456da9126e7f6df692dc36da035af23a05573e52bc2480a4d79fd5d957860b63bc46fecffe05f 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: arm64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 736 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_arm64.deb Size: 460992 MD5sum: 4824278b46f17aa82143b492bc3e50fb SHA1: b20a7c93f283478f76ac4b4a316df0b8b2b46e04 SHA256: 6164ac27692fafb5839a7c5c3ab0fa64f1973a0956a22741024ec06e1c9e3ab3 SHA512: 6f1efd03d62f514821c58df4ef9bede9fa23905194101d65fa417507e449d610eeb615e028110c6431eceb778097b02b3e3c271d3582563ead8173f351bcfcc3 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: arm64 Version: 0.1.1-1.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-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_arm64.deb Size: 218686 MD5sum: 209f694cd991338013cd28fd134f0d3a SHA1: 4e02da9e9fb90255032925781ccef18e0ee4b1cf SHA256: 4a68a2ef0494e92d2ca0e411ed6babaa98bbab591c9af7196f83b176ff78a3cd SHA512: 90eb9c861677a3f039e091c148a61505828dfc4016b31056348ded7348c4efd7dd835d9cdfe39de15570a9b27abf81316be77e80168622aae099c8b80c4aa716 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: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-clustering.sc.dp_1.1-1.ca2604.1_arm64.deb Size: 39856 MD5sum: 81cbfd65d60568e7e6b0bbc4efbb3abc SHA1: 22a20127bf5dda5404c85ed7939b69714fbe7647 SHA256: 22d354801a23ec8a234aa6c0cebf0a337b22a79f0e33b2a649a954a4f1df38b8 SHA512: dd7397222c3cd676bcc7cbaa984d7e3952357907b19802ed6bb03a4aa02748569a11293a5403866de5053cc04577eec9fa144122c2bd1bc90625d21cb8dd7b31 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: arm64 Version: 0.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1041 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 459588 MD5sum: ef10c4462771ce7c5a96369c8f6edf7b SHA1: 213541052456f0801ad4cae7dda634891e760041 SHA256: a67bddbe0cebb32ac26e47b97473ee0ebe2f35c629cfccef021e08071ac626cc SHA512: 85ec8f456ce292ed95a4316f1996fedfea7d98cefcce01d2e6547423e3a735da9a9250904a29fb1b2ecb25a5ea31548ca26c26830c745b791ce340d083b3d861 Homepage: https://cran.r-project.org/package=clustermq Description: CRAN Package 'clustermq' (Evaluate Function Calls on HPC Schedulers (SLURM, LSF, SGE, GCS,OCS, PBS, Torque)) Evaluate arbitrary function calls using workers on HPC schedulers in single line of code. All processing is done on the network without accessing the file system. Remote schedulers are supported via SSH. Package: r-cran-clusterr Architecture: arm64 Version: 1.3.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1965 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1114738 MD5sum: bd037d1768bedf2ddbdbf9eab44e0269 SHA1: 73b95af76af3cb361d5d07a5305f32ff629e7f20 SHA256: 2ded3ba04eafa681f84ef7758a86fc8a90aefa0f86f95bf0550acc4585b70cdf SHA512: 35df2e2270572c1734666e3aa88c529620abb6651b37345b4dc9876368d4bd636799e5de89421d5a72821175f903348769b5bb0f5c8bcf9bbdc985ca6b3f4c43 Homepage: https://cran.r-project.org/package=ClusterR Description: CRAN Package 'ClusterR' (Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoidsand Affinity Propagation Clustering) Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to speed up the computationally intensive parts of the functions. For more information, see (i) "Clustering in an Object-Oriented Environment" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, ; (ii) "Web-scale k-means clustering" by D. Sculley (2010), ACM Digital Library, ; (iii) "Armadillo: a template-based C++ library for linear algebra" by Sanderson et al (2016), The Journal of Open Source Software, ; (iv) "Clustering by Passing Messages Between Data Points" by Brendan J. Frey and Delbert Dueck, Science 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976, . Package: r-cran-clustersim Architecture: arm64 Version: 0.51-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4065 Depends: libc6 (>= 2.17), libstdc++6 (>= 4.3), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-mass, r-cran-ade4, r-cran-e1071 Suggests: r-cran-mlbench, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-clustersim_0.51-6-1.ca2604.1_arm64.deb Size: 3596724 MD5sum: 3ecb2e69fd2bf43d99f18bfcc5aff4a9 SHA1: a78aa1fdfc36dd54d2cb1e100acd164ae6c9a5ee SHA256: 65292c255305b811d8cd1acf599383bd36595fdae35bda750f597397e74e0083 SHA512: 2849c5f4bff41ef98ff0a9451822f40b43dcf36ae9aa765411d01282124ab4cf19359e6a21ecaea643163a132edaaebca8735816ff51e4ecc73cfd486192182d Homepage: https://cran.r-project.org/package=clusterSim Description: CRAN Package 'clusterSim' (Searching for Optimal Clustering Procedure for a Data Set) Distance measures (GDM1, GDM2, Sokal-Michener, Bray-Curtis, for symbolic interval-valued data), cluster quality indices (Calinski-Harabasz, Baker-Hubert, Hubert-Levine, Silhouette, Krzanowski-Lai, Hartigan, Gap, Davies-Bouldin), data normalization formulas (metric data, interval-valued symbolic data), data generation (typical and non-typical data), HINoV method, replication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorical and symbolic interval-valued data). (MILLIGAN, G.W., COOPER, M.C. (1985) , HUBERT, L., ARABIE, P. (1985) , RAND, W.M. (1971) , JAJUGA, K., WALESIAK, M. (2000) , MILLIGAN, G.W., COOPER, M.C. (1988) , JAJUGA, K., WALESIAK, M., BAK, A. (2003) , DAVIES, D.L., BOULDIN, D.W. (1979) , CALINSKI, T., HARABASZ, J. (1974) , HUBERT, L. (1974) , TIBSHIRANI, R., WALTHER, G., HASTIE, T. (2001) , BRECKENRIDGE, J.N. (2000) , WALESIAK, M., DUDEK, A. (2008) ). Package: r-cran-clusterstability Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 214 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_arm64.deb Size: 91016 MD5sum: f8466d600c99f5c1f4267bc11de6f175 SHA1: 5cfbe4db90fcec456ce7095182297626e715982f SHA256: 5ed67f279e4b2d3ecfcd89809c6c4f6f71578f8f619c3288b3975456b5fa1c0f SHA512: 84f071f006f2e6d4625b807475d6e26d11fea36cc3381457812bd559cae264dd26f02590f9865691511555ebab1afcb935b528ebef0f6a8654d5e750c937c011 Homepage: https://cran.r-project.org/package=ClusterStability Description: CRAN Package 'ClusterStability' (Assessment of Stability of Individual Objects or Clusters inPartitioning Solutions) Allows one to assess the stability of individual objects, clusters and whole clustering solutions based on repeated runs of the K-means and K-medoids partitioning algorithms. Package: r-cran-clustord Architecture: arm64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1677 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_arm64.deb Size: 1008628 MD5sum: 7977c0af29ca4a683e357bf3d7339477 SHA1: 24319f3302b67bd7e37d8d530ea921bfb89da714 SHA256: 5be717f6c60362a483e0e24a21085c53bafdfee6569902ea973c3b3fe29901c3 SHA512: 0c263468d2bc364b9c7f4623e561ac64129c5ae5826ec246f188d582d7efdd2f3c5f7593879d4e213caf2dd4fe4e6adb4eb9e81c823c1b91e3cd865576b4a78e Homepage: https://cran.r-project.org/package=clustord Description: CRAN Package 'clustord' (Cluster Ordinal Data via Proportional Odds or Ordered Stereotype) Biclustering, row clustering and column clustering using the proportional odds model (POM), ordered stereotype model (OSM) or binary model for ordinal categorical data. Fernández, D., Arnold, R., Pledger, S., Liu, I., & Costilla, R. (2019) . Package: r-cran-clusttmb Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4208 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_arm64.deb Size: 1080620 MD5sum: 0b351c4d6b94de80c74f557dc30610fc SHA1: 02c216b8124c64b9aacddf5f608c65c34267d398 SHA256: 0504e0db0e87cd031224e51f45ff035538d146afe4b34f7920f6651bc7a2dfef SHA512: c7ce14625bb1f6667c2e43a109e859b25280ef10485613dfdf5d3698ea90ff6e22b80953af9096528e7ebed21b0e61b1be62105ec38293c5431f1b9820791cb0 Homepage: https://cran.r-project.org/package=clustTMB Description: CRAN Package 'clustTMB' (Spatio-Temporal Finite Mixture Model using 'TMB') Fits a spatio-temporal finite mixture model using 'TMB'. Covariate, spatial and temporal random effects can be incorporated into the gating formula using multinomial logistic regression, the expert formula using a generalized linear mixed model framework, or both. Package: r-cran-clustur Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1635 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_arm64.deb Size: 518834 MD5sum: 95aff94dac51e3e1e2ad6c1c5e08cd34 SHA1: 77845ec3eae8e11178fa58a968c616e0e816987c SHA256: 2b0ff097b829adff3f9d5d45e46bc48af13acb6d5273f0760ca7c18eb4eaf268 SHA512: f00ac41a142c4da04fcb8d393ee5e0b5205f81252258e904a131c43e14e88326ac684066388931199eefd82ba9b0dad2dc7b79987e55ede42994c872824e517b Homepage: https://cran.r-project.org/package=clustur Description: CRAN Package 'clustur' (Clustering) A tool that implements the clustering algorithms from 'mothur' (Schloss PD et al. (2009) ). 'clustur' make use of the cluster() and make.shared() command from 'mothur'. Our cluster() function has five different algorithms implemented: 'OptiClust', 'furthest', 'nearest', 'average', and 'weighted'. 'OptiClust' is an optimized clustering method for Operational Taxonomic Units, and you can learn more here, (Westcott SL, Schloss PD (2017) ). The make.shared() command is always applied at the end of the clustering command. This functionality allows us to generate and create clustering and abundance data efficiently. Package: r-cran-clustvarlv Architecture: arm64 Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 788 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 548560 MD5sum: 62b65733d274b3e124ee3cfeca5a499e SHA1: 1d68ef44d078ed6958f24592b4cae92cb6918ea1 SHA256: 0b8414c49c63666e2505341e47e130f1681f6d7589f49bf119c78ce89ae8da33 SHA512: dc40021a1ce8684746ee943393d34da1dec2d94a336bbbc1b4938a77ef7b2afb8fd4b8b1aeab4720b18b8cdde02eebe32a1cd0925c6df0cfec028dedc1f81140 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: arm64 Version: 0.12.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3329 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_arm64.deb Size: 2095104 MD5sum: 319e25e5665f591e56876d7af9f5c38c SHA1: 580cf2fd22c6028730e0d794f7a6c5a707df7473 SHA256: 23daf606cbc660345ea4636ce5adbf1ea13b4f782141297e05b4ec8ddb68eddd SHA512: 871696c7a6d2e5e22b9fe0df68894d7606b6302a73a11f9bae6f9fd26a6ca5e742022e1cf6b07b59e9bab41fd1aa839ce5925ae21576931927e0a262f3f5fb41 Homepage: https://cran.r-project.org/package=CLVTools Description: CRAN Package 'CLVTools' (Tools for Customer Lifetime Value Estimation) A set of state-of-the-art probabilistic modeling approaches to derive estimates of individual customer lifetime values (CLV). Commonly, probabilistic approaches focus on modelling 3 processes, i.e. individuals' attrition, transaction, and spending process. Latent customer attrition models, which are also known as "buy-'til-you-die models", model the attrition as well as the transaction process. They are used to make inferences and predictions about transactional patterns of individual customers such as their future purchase behavior. Moreover, these models have also been used to predict individuals’ long-term engagement in activities such as playing an online game or posting to a social media platform. The spending process is usually modelled by a separate probabilistic model. Combining these results yields in lifetime values estimates for individual customers. This package includes fast and accurate implementations of various probabilistic models for non-contractual settings (e.g., grocery purchases or hotel visits). All implementations support time-invariant covariates, which can be used to control for e.g., socio-demographics. If such an extension has been proposed in literature, we further provide the possibility to control for time-varying covariates to control for e.g., seasonal patterns. Currently, the package includes the following latent attrition models to model individuals' attrition and transaction process: [1] Pareto/NBD model (Pareto/Negative-Binomial-Distribution), [2] the Extended Pareto/NBD model (Pareto/Negative-Binomial-Distribution with time-varying covariates), [3] the BG/NBD model (Beta-Gamma/Negative-Binomial-Distribution) and the [4] GGom/NBD (Gamma-Gompertz/Negative-Binomial-Distribution). Further, we provide an implementation of the Gamma/Gamma model to model the spending process of individuals. Package: r-cran-cmapss Architecture: arm64 Version: 0.1.1-1.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_arm64.deb Size: 5404700 MD5sum: 97ccafbcccc2876c15c0b7b66e676963 SHA1: 3f5a058a0740a412c84242c27eb89bf38e4605b9 SHA256: b391a4527c6f0b8f27ad319760a4dc044ef68f3aaecdee37b89e91cad34f5909 SHA512: 1dd2a7d349f49b95fb0e8ca15e74c61732e799f67f05026df6dcef2e2b994c09f2c92ae69df6d279ce97268b82b299ef9bead07614e36a2bb945198a8b255a75 Homepage: https://cran.r-project.org/package=CMAPSS Description: CRAN Package 'CMAPSS' (Commercial Modular Aero-Propulsion System Simulation Data Set) Contains the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data set. Package: r-cran-cmbclust Architecture: arm64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 225 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_arm64.deb Size: 142332 MD5sum: 8efdca9c656a6ba552e9308e6d442e0c SHA1: e64e4dd2da1ab116d6e84d43937b3e9ef502e3cf SHA256: 66df261fe6a5daafe0010f61dada44877efa0ef680458f00f49b17494b899c0b SHA512: 0c5c3d683fee50a64a5426462d19b84f54aa95fb5b3829ce1cb72ab8ca419702ed81d2fc086720d4e007fd30bc3a9f2af94fd691e951b6ceec8b99f773fa593c 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: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Filename: pool/dists/resolute/main/r-cran-cmf_1.0.3-1.ca2604.1_arm64.deb Size: 82020 MD5sum: bdade194a619aa077a2cc9e52525a97f SHA1: a39a2da694af922e67072db194a40767715573bc SHA256: 289ec624d4cc159e55e359d9d8d99283905341787390f4ce14a801cb49318760 SHA512: 1a5604c931127063cf5109e2f1a5324aa7c77ed2b44755ba5b7b1f3807fffd845e1375dcc2cfc8d8fb158ebe50505a192efbf6d1298a63505f3a4189c7b3e474 Homepage: https://cran.r-project.org/package=CMF Description: CRAN Package 'CMF' (Collective Matrix Factorization) Collective matrix factorization (CMF) finds joint low-rank representations for a collection of matrices with shared row or column entities. This code learns a variational Bayesian approximation for CMF, supporting multiple likelihood potentials and missing data, while identifying both factors shared by multiple matrices and factors private for each matrix. For further details on the method see Klami et al. (2014) . The package can also be used to learn Bayesian canonical correlation analysis (CCA) and group factor analysis (GFA) models, both of which are special cases of CMF. This is likely to be useful for people looking for CCA and GFA solutions supporting missing data and non-Gaussian likelihoods. See Klami et al. (2013) and Virtanen et al. (2012) for details on Bayesian CCA and GFA, respectively. Package: r-cran-cmfrec Architecture: arm64 Version: 3.5.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 910 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 6), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), 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_arm64.deb Size: 540436 MD5sum: 48281c3be5e444663bd523acb1886d05 SHA1: 1f429b0107a837dc13577c1abe2b148070c25af5 SHA256: 2b102d74f398a05028ce96ab396b4455e5f804642d7bb19cbbf809bd6913f6cc SHA512: 614fb84641831eebec8a1a12a12000caf44462abd2a48598bd09c8e12cdfa12ac8b4b09af542090b36d748504aecb9c825e8d33d560bd9e1dc8ac4d2a21ae5d3 Homepage: https://cran.r-project.org/package=cmfrec Description: CRAN Package 'cmfrec' (Collective Matrix Factorization for Recommender Systems) Collective matrix factorization (a.k.a. multi-view or multi-way factorization, Singh, Gordon, (2008) ) tries to approximate a (potentially very sparse or having many missing values) matrix 'X' as the product of two low-dimensional matrices, optionally aided with secondary information matrices about rows and/or columns of 'X', which are also factorized using the same latent components. The intended usage is for recommender systems, dimensionality reduction, and missing value imputation. Implements extensions of the original model (Cortes, (2018) ) and can produce different factorizations such as the weighted 'implicit-feedback' model (Hu, Koren, Volinsky, (2008) ), the 'weighted-lambda-regularization' model, (Zhou, Wilkinson, Schreiber, Pan, (2008) ), or the enhanced model with 'implicit features' (Rendle, Zhang, Koren, (2019) ), with or without side information. Can use gradient-based procedures or alternating-least squares procedures (Koren, Bell, Volinsky, (2009) ), with either a Cholesky solver, a faster conjugate gradient solver (Takacs, Pilaszy, Tikk, (2011) ), or a non-negative coordinate descent solver (Franc, Hlavac, Navara, (2005) ), providing efficient methods for sparse and dense data, and mixtures thereof. Supports L1 and L2 regularization in the main models, offers alternative most-popular and content-based models, and implements functionality for cold-start recommendations and imputation of 2D data. Package: r-cran-cmgfm Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 402 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 150302 MD5sum: 0057133cd7a6e737eadff5f62fa31780 SHA1: 6f5db93c92d85037461ad22d6a02168eeedf8c20 SHA256: ad25e9dafe1a3b084eaa137fd5103734b4a502e3e6fcd0bf053fb2dc310699e3 SHA512: 50e4ac704879b53cfcf831a3ff3fc40bb11c4dbeee421426e0ec6f10e060b26474d7e143a71cf07598b2c67769a6f39bb77ef3c396f9eb453034425a32764cb2 Homepage: https://cran.r-project.org/package=CMGFM Description: CRAN Package 'CMGFM' (Covariate-Augumented Generalized Factor Model) Covariate-augumented generalized factor model is designed to account for cross-modal heterogeneity, capture nonlinear dependencies among the data, incorporate additional information, and provide excellent interpretability while maintaining high computational efficiency. Package: r-cran-cmpp Architecture: arm64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 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-dplyr, r-cran-tidyr, r-cran-numderiv, r-cran-cmprsk, r-cran-tidyselect, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-cmpp_0.0.2-1.ca2604.1_arm64.deb Size: 196874 MD5sum: b7a5e2ec110faab9c05fc6739271b471 SHA1: 6c72bf638bf0a54ae808891a3e0738b8a9b17cc1 SHA256: be1f3cff33f9a9a5e0d524234289440c06931545e606c13479181f4f60e5b46e SHA512: a03a5d0bce67c296996d477f613e2ac6562e9a792a824515b0fa9b2b165dc28d709c90985f833efec750aa7ad83dd8f2db0d55be9aacd691e6d8fec3eb1c2ae6 Homepage: https://cran.r-project.org/package=cmpp Description: CRAN Package 'cmpp' (Direct Parametric Inference for the Cumulative IncidenceFunction in Competing Risks) Implements parametric (Direct) regression methods for modeling cumulative incidence functions (CIFs) in the presence of competing risks. Methods include the direct Gompertz-based approach and generalized regression models as described in Jeong and Fine (2006) and Jeong and Fine (2007) . The package facilitates maximum likelihood estimation, variance computation, with applications to clinical trials and survival analysis. Package: r-cran-cmprsk Architecture: arm64 Version: 2.2-12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 168 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_arm64.deb Size: 84954 MD5sum: b46bd5ab386516df3d3b11fa34c77a3b SHA1: a1afb6ad944b110ce8f8ac93ff20bf5da46945d1 SHA256: 383b780b4eff94151fde9f59d3337efdc8324b085de8a2ec02dd42537689ed4d SHA512: 230493b3c2eabe300bf9f60517cd9b4c170139ec2d37c09f4eacf665209dba15307ac48bb4cfb59678d51ca793bf41e126549a6478fc5d33d5665044d3b6f485 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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'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. 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Package: r-cran-cna Architecture: arm64 Version: 4.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1923 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1352170 MD5sum: 96171133a41d663d97d55ebf4be8d78d SHA1: de6ff71ed7f42db2139285e99b0422f5b19bdfbd SHA256: 52cb9e700fd2ddb50d56c946e8bd6ea7c9dcc6d4440bcd3fc4d930185ddb9b47 SHA512: 0b0ffcda307d8528e33a28074bd57cd8b1bbe0d374ae733fcf3b2399f5e0e83f4b7e70a1f53d93eea37bd6b11538b5b10d387b3a73619d8937291b9b90743a0a Homepage: https://cran.r-project.org/package=cna Description: CRAN Package 'cna' (Causal Modeling with Coincidence Analysis) Provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) , and generalized in Baumgartner & Ambuehl (2020) . CNA is designed to recover INUS-causation from data, which is particularly relevant for analyzing processes featuring conjunctural causation (component causation) and equifinality (alternative causation). CNA is currently the only method for INUS-discovery that allows for multiple effects (outcomes/endogenous factors), meaning it can analyze common-cause and causal chain structures. Moreover, as of version 4.0, it is the only method of its kind that provides measures for model evaluation and selection that are custom-made for the problem of INUS-discovery. Package: r-cran-cnaopt Architecture: arm64 Version: 0.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 321 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 153736 MD5sum: 1a18a64f2a16be323cbbcf29209e3fa5 SHA1: 204f6f4d767719f813d92f7d71232b845bef85b2 SHA256: ca8f0e9851f89667996395a6b9e4ee8d0ce5d274e005b9b4703cab26bfa175bd SHA512: d5ffd469e64a061f64503e053927da743ca6b1f81f3d6b1fd4981ad74c6147ea28e2e9c71fd3537f001ea4eec9e9bffdc6aeb6f21bb92c63cfe5c6aa2c4e4fad Homepage: https://cran.r-project.org/package=cnaOpt Description: CRAN Package 'cnaOpt' (Optimizing Consistency and Coverage in Configurational CausalModeling) This is an add-on to the 'cna' package comprising various functions for optimizing consistency and coverage scores of models of configurational comparative methods as Coincidence Analysis (CNA) and Qualitative Comparative Analysis (QCA). The function conCovOpt() calculates con-cov optima, selectMax() selects con-cov maxima among the con-cov optima, DNFbuild() can be used to build models actually reaching those optima, and findOutcomes() identifies those factor values in analyzed data that can be modeled as outcomes. For a theoretical introduction to these functions see Baumgartner and Ambuehl (2021) . 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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: arm64 Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 890 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_arm64.deb Size: 440540 MD5sum: 16652af861c2ce9775b48e735031d3fd SHA1: 80ca910aca7c2ec4633197b813434b3798074d63 SHA256: fd5383aa4e53793be9d8be2d4eb8fb1fd7a6a29980864cbbf1a9eda0b0491484 SHA512: af9083a539e06e9db05fb0c201cb278cc7016f79c9c3d9d6d82c3bcdc4916a13a45366ff1f69e864144a03ea673f12f3b760c20193f818a9fe8745ea7681c243 Homepage: https://cran.r-project.org/package=coga Description: CRAN Package 'coga' (Convolution of Gamma Distributions) Evaluation for density and distribution function of convolution of gamma distributions in R. Two related exact methods and one approximate method are implemented with efficient algorithm and C++ code. A quick guide for choosing correct method and usage of this package is given in package vignette. For the detail of methods used in this package, we refer the user to Mathai(1982), Moschopoulos(1984), Barnabani(2017), Hu et al.(2020). Package: r-cran-coglasso Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 529 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_arm64.deb Size: 284556 MD5sum: 8a688b0af71bdbc25269893dd4484f0a SHA1: 0844cfd21b4391f829fc7e4beff4832edfa5e4bc SHA256: df99ae8c7ce3b8d86ec0c8d6bc6db7e3d5e27b3b74d1d992ce24246b193cf567 SHA512: c9e9f5b2aa6709d58f1e7458ae0a6613fc377ded414bb01377f9356fab5bf37426a8f5057f01b4aefd02f5f12f76e60f914b2016296ab8b5dad6bc33ac33344e Homepage: https://cran.r-project.org/package=coglasso Description: CRAN Package 'coglasso' (Collaborative Graphical Lasso - Multi-Omics NetworkReconstruction) Reconstruct networks from multi-omics data sets with the collaborative graphical lasso (coglasso) algorithm described in Albanese, A., Kohlen, W., and Behrouzi, P. 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Can extract all necessary data from a database. This implements large-scale propensity scores (LSPS) as described in Tian et al. (2018) , using a large set of covariates, including for example all drugs, diagnoses, procedures, as well as age, comorbidity indexes, etc. Large scale regularized regression is used to fit the propensity and outcome models as described in Suchard et al. (2013) . Functions are included for trimming, stratifying, (variable and fixed ratio) matching and weighting by propensity scores, as well as diagnostic functions, such as propensity score distribution plots and plots showing covariate balance before and after matching and/or trimming. Supported outcome models are (conditional) logistic regression, (conditional) Poisson regression, and (stratified) Cox regression. Also included are Kaplan-Meier plots that can adjust for the stratification or matching. 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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: arm64 Version: 0.3.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-collections_0.3.12-1.ca2604.1_arm64.deb Size: 67070 MD5sum: 791cf911a1abd645be64fe6190a35f69 SHA1: f5a31a0c8e2632ec4a044ea70a6c6b66f7b3bcb1 SHA256: f180a82349592b90deefc7d0ccba95922ba9e81dd8be45118156bf467ba3d79e SHA512: 048131c6d6c877b861943e64664aff420068406552daef9de089a868f37cd47580c8bc4c5ae99fc014333360f1d292692b8a508d9e9355ad4405b45ef7119785 Homepage: https://cran.r-project.org/package=collections Description: CRAN Package 'collections' (High Performance Container Data Types) Provides high performance container data types such as queues, stacks, deques, dicts and ordered dicts. 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Package: r-cran-collpcm Architecture: arm64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 675 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 570312 MD5sum: e33d7cbfbaee61c7113998c68f650627 SHA1: a87d51204235f73cebd6e54b8873c8b45204f13f SHA256: 4cf1451b61c67c9bcd6efc1c2ce808c6d75ec0b42a160d5ca9c5ed64f9d343c3 SHA512: 4db178436798556211213cefb3b3b6bede7858284176cd8e95a3f8f7dea05a7f81ccf10868c5585165b2d845a936a6ced0fb45b222bee811ba147ac3bd943298 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: arm64 Version: 1.1.2-1.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-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_arm64.deb Size: 246184 MD5sum: 186fbb7e0b5ec06e4e42fbe51c134ec1 SHA1: c6347270b617def2c9b9e6b6ba90804e5ec5a647 SHA256: 88c99b485218dabdf978c3d1898a1c86a80097d788bdccabbb257d2f735d0dbf SHA512: 22e8f03ef01cd2cfd513ebe51f8dd2c4b815a9c0d5065e2704002d4b162038ce7093fb6d185b401ddcef3a550412730db12810bb50712db4c0a801b2bedcc1bc Homepage: https://cran.r-project.org/package=colorednoise Description: CRAN Package 'colorednoise' (Simulate Temporally Autocorrelated Populations) Temporally autocorrelated populations are correlated in their vital rates (growth, death, etc.) from year to year. It is very common for populations, whether they be bacteria, plants, or humans, to be temporally autocorrelated. This poses a challenge for stochastic population modeling, because a temporally correlated population will behave differently from an uncorrelated one. This package provides tools for simulating populations with white noise (no temporal autocorrelation), red noise (positive temporal autocorrelation), and blue noise (negative temporal autocorrelation). The algebraic formulation for autocorrelated noise comes from Ruokolainen et al. (2009) . Models for unstructured populations and for structured populations (matrix models) are available. Package: r-cran-colorfast Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-colorfast_1.0.1-1.ca2604.1_arm64.deb Size: 40694 MD5sum: f3b4beb6f3be6a22c92ebcfb70f94267 SHA1: 82efd2e8eadfc6952c607c0b8e1b5e1d0a6f2ea1 SHA256: a14600cbf8815b3d0166e70b4bd6f4f966b91e51c899d09970c05ce32261edd1 SHA512: 68073ab49ddfc347d93e5c0ff4f54f46e0972fee144ad4d102eb5bacb133779a7fa377cf96faab5ba439204d0db1646f3ec07529d472e5953633d19de8aa8c42 Homepage: https://cran.r-project.org/package=colorfast Description: CRAN Package 'colorfast' (Fast Conversion of R Colors to Color Component Values and NativePacked Integer Format) Color values in R are often represented as strings of hexadecimal colors or named colors. This package offers fast conversion of these color representations to either an array of red/green/blue/alpha values or to the packed integer format used in native raster objects. Functions for conversion are also exported at the 'C' level for use in other packages. This fast conversion of colors is implemented using an order-preserving minimal perfect hash derived from Majewski et al (1996) "A Family of Perfect Hashing Methods" . Package: r-cran-colorspace Architecture: arm64 Version: 2.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4061 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-kernsmooth, r-cran-mass, r-cran-kernlab, r-cran-mvtnorm, r-cran-vcd, r-cran-shiny, r-cran-shinyjs, r-cran-ggplot2, r-cran-dplyr, r-cran-scales, r-cran-png, r-cran-jpeg, r-cran-knitr, r-cran-rmarkdown, r-cran-rcolorbrewer, r-cran-rcartocolor, r-cran-scico, r-cran-viridis, r-cran-wesanderson Filename: pool/dists/resolute/main/r-cran-colorspace_2.1-2-1.ca2604.1_arm64.deb Size: 2522960 MD5sum: 6ffb04028fb33cceef502c9ff93b8a47 SHA1: 6d9a5f3a54417af11bc69818d48c9d43e0c01d9a SHA256: fc912197fb962a236e54eedb8d19ce171577ee331a6424a07f3b521af275682e SHA512: 6b98647f265e6fa722f23278fa46404edc51467ac814c15135568c2898490e8895f03e0f123c412fa688ab729b009874b8f259025afb97d47e49af5615772e88 Homepage: https://cran.r-project.org/package=colorspace Description: CRAN Package 'colorspace' (A Toolbox for Manipulating and Assessing Colors and Palettes) Carries out mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB, and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided along with corresponding ggplot2 color scales. Color palette choice is aided by an interactive app (with either a Tcl/Tk or a shiny graphical user interface) and shiny apps with an HCL color picker and a color vision deficiency emulator. Plotting functions for displaying and assessing palettes include color swatches, visualizations of the HCL space, and trajectories in HCL and/or RGB spectrum. Color manipulation functions include: desaturation, lightening/darkening, mixing, and simulation of color vision deficiencies (deutanomaly, protanomaly, tritanomaly). Details can be found on the project web page at and in the accompanying scientific paper: Zeileis et al. (2020, Journal of Statistical Software, ). Package: r-cran-colossus Architecture: arm64 Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4027 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_arm64.deb Size: 1743432 MD5sum: e698078eeb328babac61821c71e0f6b7 SHA1: a7523d88ea3bfd16d6fd8fed0ed9328afb78631a SHA256: 546a94c9e4e2ac6259d0867b3ea2b1321b40b956621d75d6de9827f090b2d33f SHA512: 804af00c969dff64f5794be1c700158a23759a4026e3f6f56a76b64583fd4349cb61c9a196048051e018190db2d1dea1b80785d5bdece75da98ac6b7bab1e7fa Homepage: https://cran.r-project.org/package=Colossus Description: CRAN Package 'Colossus' ("Risk Model Regression and Analysis with Complex Non-LinearModels") Performs survival analysis using general non-linear models. Risk models can be the sum or product of terms. Each term is the product of exponential/linear functions of covariates. Additionally sub-terms can be defined as a sum of exponential, linear threshold, and step functions. Cox Proportional hazards , Poisson , and Fine-Gray competing risks regression are supported. This work was sponsored by NASA Grants 80NSSC19M0161 and 80NSSC23M0129 through a subcontract from the National Council on Radiation Protection and Measurements (NCRP). The computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CNS-1006860, EPS-1006860, EPS-0919443, ACI-1440548, CHE-1726332, and NIH P20GM113109. Package: r-cran-colourvalues Architecture: arm64 Version: 0.3.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1948 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_arm64.deb Size: 534408 MD5sum: 1a0f90f674ff2c70f5e3ca55aac37782 SHA1: d9534b13331403ad5955ff70ad1a3fbdd7dad013 SHA256: 55c26b097494524678f38f53f3e4edd492d1a13531bb0a10f999f7d1a09d2df9 SHA512: 5297f1209f8c898c77faa6eba77ed097f7a50813d7a20a7543995c324fc900424b8d1828932d2016f35f7c591c76610c73c821fecc92e73233744afd587bc9f7 Homepage: https://cran.r-project.org/package=colourvalues Description: CRAN Package 'colourvalues' (Assigns Colours to Values) Maps one of the viridis colour palettes, or a user-specified palette to values. Viridis colour maps are created by Stéfan van der Walt and Nathaniel Smith, and were set as the default palette for the 'Python' 'Matplotlib' library . Other palettes available in this library have been derived from 'RColorBrewer' and 'colorspace' packages. Package: r-cran-comat Architecture: arm64 Version: 0.9.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 563 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_arm64.deb Size: 181440 MD5sum: f0e0272b67316562eeb57d93561b1ff7 SHA1: 8b132adf79250e2309f9b10725ba88d20da39ea6 SHA256: 69f05bf7f9db7c4e3a33153fdf523f32cd2cee6b7146917dc076af65cfe76ce5 SHA512: 83e44cb9af6a56651b91a370efadc29f22396062cb52f480be398305f337095d59e084b1179f2e10e90a68d34f38c59c7b51e17e3567b78421e577dc1b3a4200 Homepage: https://cran.r-project.org/package=comat Description: CRAN Package 'comat' (Creates Co-Occurrence Matrices of Spatial Data) Builds co-occurrence matrices based on spatial raster data. It includes creation of weighted co-occurrence matrices (wecoma) and integrated co-occurrence matrices (incoma; Vadivel et al. (2007) ). Package: r-cran-combinit Architecture: arm64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 477 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 229984 MD5sum: a16c57db2582e02c7f6a34c1f9ff14de SHA1: ae99747536f01d9d808419e1b2a9de7fd3b7ee26 SHA256: aae0eb85046b90a063f37690397903ff187da2539d1c8318db8fb31cd1163255 SHA512: 855a8dab2fd532869e15fc68e43fbad9e7cc07027644ec77ed7bb612a3de1948fcd6fc854de79f882e10c11ee8b8577f9ab6454430185612b287c35993252305 Homepage: https://cran.r-project.org/package=combinIT Description: CRAN Package 'combinIT' (A Combined Interaction Test for Unreplicated Two-Way Tables) There are several non-functional-form-based interaction tests for testing interaction in unreplicated two-way layouts. However, no single test can detect all patterns of possible interaction and the tests are sensitive to a particular pattern of interaction. This package combines six non-functional-form-based interaction tests for testing additivity. These six tests were proposed by Boik (1993) , Piepho (1994), Kharrati-Kopaei and Sadooghi-Alvandi (2007) , Franck et al. (2013) , Malik et al. (2016) and Kharrati-Kopaei and Miller (2016) . The p-values of these six tests are combined by Bonferroni, Sidak, Jacobi polynomial expansion, and the Gaussian copula methods to provide researchers with a testing approach which leverages many existing methods to detect disparate forms of non-additivity. This package is based on the following published paper: Shenavari and Kharrati-Kopaei (2018) "A Method for Testing Additivity in Unreplicated Two-Way Layouts Based on Combining Multiple Interaction Tests". In addition, several sentences in help files or descriptions were copied from that paper. Package: r-cran-combiter Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 67580 MD5sum: 3164e665bacd7504b0c32d380cf189f1 SHA1: a0a19f645559470843050662b68d277935f3c241 SHA256: 96034c070cb7eae2ee7eb7ea8f3f85d7ed0262261d7254cc7e57e50a8839f06e SHA512: 4393f34e5327e3d835935e82fed7c4fd893cf86f58fe832b87c16ccb99efcaf25258ca52fea67de7039e27a802844b98453e2357a22815720b7168b210e41df5 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: arm64 Version: 0.2-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 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_arm64.deb Size: 144828 MD5sum: d841c3da6ee70ef9343f6f0f3a602e6b SHA1: 24fb0dbf11eb9ce4f485aac38ce2e2ed369738e4 SHA256: 22053b43420d449b3444673f8ebb8d9be072f98d41148ce860b3e5f172384d5c SHA512: 6c80625df1eb57529ff42fdb59bcd01ea0a4f5ce4cba25bd9ab23e4381da30f87d5bd84de5fcad3059614945a9dda718fec618407b3c24416d7ced1cf20eea2b 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: arm64 Version: 0.8-1.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-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_arm64.deb Size: 206374 MD5sum: 85df18beb90ea668f8ea16072960bc71 SHA1: 62fbcae6b835f5f138152954c7c5b4b21763da6e SHA256: 6523a7440296b6c8fb7118da29f451294fca45f45f1a55df1dac73ebaa343974 SHA512: f7bcb6f84fedefff4a9dd4df4fc9cf4c7ec62f1e67edc08926674f6b4037bfa3b1271695fa32738a54078c0a18f7f606044a2cd420503b055c3e96c6d3e96164 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: arm64 Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-curl, r-cran-testthat, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-commonmark_2.0.0-1.ca2604.1_arm64.deb Size: 126860 MD5sum: 6580f288b775ec4a662accf1dda44332 SHA1: 53e54b891b99ca9b03f531a8698610c558112c67 SHA256: 687f532e38f2009e4363f5d7479ef4fec237d0bfc69b5b9803198cf065da8fa8 SHA512: ee53f7686867761ca01005c3aecd0b47a8432032b73c51564b05cf0712737b9a02349c7b6289869ea8d600e6bbfb97c73f016d576167d2140efd4dea1fa6e1fd Homepage: https://cran.r-project.org/package=commonmark Description: CRAN Package 'commonmark' (High Performance CommonMark and Github Markdown Rendering in R) The CommonMark specification defines a rationalized version of markdown syntax. This package uses the 'cmark' reference implementation for converting markdown text into various formats including html, latex and groff man. In addition it exposes the markdown parse tree in xml format. Also includes opt-in support for GFM extensions including tables, autolinks, and strikethrough text. Package: r-cran-comparator Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 632 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-proxy, r-cran-clue Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-comparator_0.1.4-1.ca2604.1_arm64.deb Size: 318340 MD5sum: 85118d86f0c6a9e6a5f771bf5f359c71 SHA1: 6bf7c55fd029628f17394004d5879a43f1ad972c SHA256: 360885889aa6f10e55076bad999e34d28103b05756fad005e83c0eb17c7e4c92 SHA512: 4cac0a576e0c375bb36f4f50a0a4fd0034647e0df46f06d92eea9449293cd96fc2077a5f703d69c13881c51b04082945fc64e0606ae425b9e08f3253f10f5884 Homepage: https://cran.r-project.org/package=comparator Description: CRAN Package 'comparator' (Comparison Functions for Clustering and Record Linkage) Implements functions for comparing strings, sequences and numeric vectors for clustering and record linkage applications. Supported comparison functions include: generalized edit distances for comparing sequences/strings, Monge-Elkan similarity for fuzzy comparison of token sets, and L-p distances for comparing numeric vectors. Where possible, comparison functions are implemented in C/C++ to ensure good performance. Package: r-cran-comparec Architecture: arm64 Version: 1.3.3-1.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-comparec_1.3.3-1.ca2604.1_arm64.deb Size: 27064 MD5sum: 92be09617ad8868c3e3e4ff9e783ca39 SHA1: 9c4dbefcc6235eb8e463395c84ac48dbd4f317ce SHA256: 00f5f3051f1d7fb8e032a6727e155caf81ce481f88628ec4475ab760199d3cae SHA512: ded2f3a798228118bb472238d8094469b89f140d5b732531544b0cf78a078b90ceb1ccdf53ab2d41b27b83bb7b8ff61682531fc5a881bc4798319c384a594fc3 Homepage: https://cran.r-project.org/package=compareC Description: CRAN Package 'compareC' (Compare Two Correlated C Indices with Right-Censored SurvivalOutcome) Proposed by Harrell, the C index or concordance C, is considered an overall measure of discrimination in survival analysis between a survival outcome that is possibly right censored and a predictive-score variable, which can represent a measured biomarker or a composite-score output from an algorithm that combines multiple biomarkers. This package aims to statistically compare two C indices with right-censored survival outcome, which commonly arise from a paired design and thus resulting two correlated C indices. Package: r-cran-compas Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1985 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1730354 MD5sum: 5c57f36be856f6ddf52957c3ef465b28 SHA1: 5ef30f6c931ca4c51c7e176a766013f01a966675 SHA256: 81f0637a4f2d1b75d452c3da36ef414abdd44efb9cba2595bb2fe6b2f99aea31 SHA512: e3f26d37453e25f860188bcb8e7272b79b4ed19a216ad50b66347bb9a864f0d9acc1dde3910964a8a2a92f3699c5688a9689080952c8c4e3e3f5c4e51d1f43a7 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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This is based on the monograph by Svetunkov & Svetunkov (2024) . Package: r-cran-complexlm Architecture: arm64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-pracma, r-cran-mathjaxr Suggests: r-cran-dplyr, r-cran-ggforce, r-cran-ggplot2, r-cran-reshape2 Filename: pool/dists/resolute/main/r-cran-complexlm_1.1.3-1.ca2604.1_arm64.deb Size: 232746 MD5sum: efff9d12df19a1382c9fbfa1ba5a3270 SHA1: 62880624b539db5595a58d23911b2ba545d81b52 SHA256: 23be123edcca76da2a7bc914e8bf5d29738d55e8aa0e2eb5d6d17cf234de17af SHA512: 37e8f9b9551521f3734ea51989b0fc5f2150d2137adce029ed32093e98139d202f94e688d510c59a49ca08280c274b583937774fec3d7fb322b3a0bf32815e29 Homepage: https://cran.r-project.org/package=complexlm Description: CRAN Package 'complexlm' (Linear Fitting for Complex Valued Data) Tools for linear fitting with complex variables. Includes ordinary least-squares (zlm()) and robust M-estimation (rzlm()), and complex methods for oft used generics. Originally adapted from the rlm() functions of 'MASS' and the lm() functions of 'stats'. 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Some of the functions have known functional forms, however, most are intended to serve as black-box functions where evaluation requires running computer code that reveals little about the functional forms of the objective and/or constraints. The primary goal of the package is to provide users (especially those who do not have access to real computer models) a source of reproducible and shareable examples that can be used for benchmarking algorithms. The package is a living repository, and so more functions will be added over time. For function suggestions, please do contact the author of the package. 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See Meira-Machado, Sestelo and Goncalves (2016) . 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See Mary C. Meyer (2013) for more details. 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The package started by merging and extending multiple packages and other published scripts on this econometric technique. It strongly emphasizes computational optimization. Details are available in the function documentation and in the vignette. 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This package provides the estimation of conditional mutual information (CMI) and its statistical significance with a focus on its application to multi-omics data. Utilizing B-spline functions (inspired by Daub et al. (2004) ), the package offers tools to estimate the association between heterogeneous multi- omics data, while removing the effects of confounding factors. This helps to unravel complex biological interactions. In addition, it includes methods to evaluate the statistical significance of these associations, providing a robust framework for multi-omics data integration and analysis. This package is ideal for researchers in computational biology, bioinformatics, and systems biology seeking a comprehensive tool for understanding interdependencies in omics data. Package: r-cran-conos Architecture: arm64 Version: 1.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2289 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1667874 MD5sum: c36e08def18a59bfa39ca9397d4bacac SHA1: 263b3f54640e71fc71e5bc4c52322244b066ab8e SHA256: c0cd3bdcb0e61374993fc7bfd5a9ec0ea38f8ebe3db943b553d819183e00e1af SHA512: 35ed12dcb97ff958bf4e63e4cd79b4f1d40e39de2f937ea0908a4f0dd3238dce1abed3cd6991ba04e42c248b799decfce130dc2db2f8321066c93277a22b5a06 Homepage: https://cran.r-project.org/package=conos Description: CRAN Package 'conos' (Clustering on Network of Samples) Wires together large collections of single-cell RNA-seq datasets, which allows for both the identification of recurrent cell clusters and the propagation of information between datasets in multi-sample or atlas-scale collections. 'Conos' focuses on the uniform mapping of homologous cell types across heterogeneous sample collections. For instance, users could investigate a collection of dozens of peripheral blood samples from cancer patients combined with dozens of controls, which perhaps includes samples of a related tissue such as lymph nodes. This package interacts with data available through the 'conosPanel' package, which is available in a 'drat' repository. To access this data package, see the instructions at . The size of the 'conosPanel' package is approximately 12 MB. Package: r-cran-conquer Architecture: arm64 Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1686 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_arm64.deb Size: 468650 MD5sum: e47989d894a003fbc78455e7eedc6ebe SHA1: af01f8c8ab3001e70db41e14110e57b694b54039 SHA256: 8de331b31ec605134abbf34378edf167e47e11b95f7da34fbb31975fdb5598ec SHA512: 085b5614fcf731c9242d2fa6011cbd45a4daccd7cb0a4b6a31d794910ca73947f030fbbda957cbcb2a05ed9cfed96cc0bb2ce632b8df984d54f0eae84fbe5dec Homepage: https://cran.r-project.org/package=conquer Description: CRAN Package 'conquer' (Convolution-Type Smoothed Quantile Regression) Estimation and inference for conditional linear quantile regression models using a convolution smoothed approach. In the low-dimensional setting, efficient gradient-based methods are employed for fitting both a single model and a regression process over a quantile range. Normal-based and (multiplier) bootstrap confidence intervals for all slope coefficients are constructed. In high dimensions, the conquer method is complemented with flexible types of penalties (Lasso, elastic-net, group lasso, sparse group lasso, scad and mcp) to deal with complex low-dimensional structures. Package: r-cran-conquestr Architecture: arm64 Version: 1.5.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3101 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1567380 MD5sum: c805b16908432b93031012461ce55d77 SHA1: 92744cfe5374ccfac1629693715c5ce82644b6c9 SHA256: 45d63bf605bb8a6ff7041bb04ebaff239d9e1cd9dbf8a231d96be36187da12da SHA512: 712989f0bc32504a01c01a303c77605be23676f081647854b82b53bc0d4757eddea7c81413767bcd34ced027642ac97d7e9f36b9c4dcf871472405ba0fb7e855 Homepage: https://cran.r-project.org/package=conquestr Description: CRAN Package 'conquestr' (An R Package to Extend 'ACER ConQuest') Extends 'ACER ConQuest' through a family of functions designed to improve graphical outputs and help with advanced analysis (e.g., differential item functioning). Allows R users to call 'ACER ConQuest' from within R and read 'ACER ConQuest' System Files (generated by the command `put` ). Requires 'ACER ConQuest' version 5.40 or later. A demonstration version can be downloaded from . Package: r-cran-consrank Architecture: arm64 Version: 3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 530 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 349110 MD5sum: 3fae71965a5d599b182129b1b2478527 SHA1: b0307fef3326b8fd652c3eb340ef9290e69d3f4d SHA256: dc89e6b3cc1224994b6ef94656d68ffb9ccf990207ac942e4edd75a2623955cb SHA512: 3c596ce50291549cd48be8d876ca47c94b4a4b2e8e23771b2384a8992f29fb0cc6b39937adb84496da3f28073a351b3e62a0091fcb3a3f723b68d579ccca5a64 Homepage: https://cran.r-project.org/package=ConsRank Description: CRAN Package 'ConsRank' (Compute the Median Ranking(s) According to the Kemeny'sAxiomatic Approach) Compute the median ranking according to the Kemeny's axiomatic approach. Rankings can or cannot contain ties, rankings can be both complete or incomplete. The package contains both branch-and-bound algorithms and heuristic solutions recently proposed. The searching space of the solution can either be restricted to the universe of the permutations or unrestricted to all possible ties. The package also provide some useful utilities for deal with preference rankings, including both element-weight Kemeny distance and correlation coefficient. This release includes also the median constrained bucket order algorithm. This release removes the functions previously declared as deprecated. These functions are now defunct and no longer available in the package. Essential references: Emond, E.J., and Mason, D.W. (2002) ; D'Ambrosio, A., Amodio, S., and Iorio, C. (2015) ; Amodio, S., D'Ambrosio, A., and Siciliano R. (2016) ; D'Ambrosio, A., Mazzeo, G., Iorio, C., and Siciliano, R. (2017) ; Albano, A., and Plaia, A. (2021) ; D'Ambrosio, A., Iorio, C., Staiano, M. and Siciliano, R (2019) . Package: r-cran-consreg Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 420 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 246058 MD5sum: cd629196071985a5e5fc142f7bc716f6 SHA1: e6eff8aebfadbdcf53e77f895524600673b3d41f SHA256: b9f7221d8da76d984e05774835f7ddab7ccf935fb00ead326231f4dc274734ca SHA512: aca868f12451c83e1806be4c840580d0526d0b6bdb9c5ef0b767c22d848cb54d18bb8e001782c972507f9d176172169c58dc7b8e0c116d4bbdb1e9a03faa57f1 Homepage: https://cran.r-project.org/package=ConsReg Description: CRAN Package 'ConsReg' (Fits Regression & ARMA Models Subject to Constraints to theCoefficient) Fits or generalized linear models either a regression with Autoregressive moving-average (ARMA) errors for time series data. The package makes it easy to incorporate constraints into the model's coefficients. The model is specified by an objective function (Gaussian, Binomial or Poisson) or an ARMA order (p,q), a vector of bound constraints for the coefficients (i.e beta1 > 0) and the possibility to incorporate restrictions among coefficients (i.e beta1 > beta2). The references of this packages are the same as 'stats' package for glm() and arima() functions. See Brockwell, P. J. and Davis, R. A. (1996, ISBN-10: 9783319298528). For the different optimizers implemented, it is recommended to consult the documentation of the corresponding packages. Package: r-cran-constrainedkriging Architecture: arm64 Version: 0.2-11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 510 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 375162 MD5sum: 7cd575b30dc45f6573d38ee40b90f41b SHA1: 906669db3eb4275fbc801e6d0b77cb07f26a6e08 SHA256: f10d6c3aa99cc26eb98152dbc3a7900b108c90698388f04e3d1299ef072f16eb SHA512: 6ef0fb7db8f311125583d2190c4c39e548a0c0aee08396faba39bc2fb76eb86c26a66cb274b8b04c570d9a3362ce8f455dc9918a4ce76f6eb70ff4865648ecd5 Homepage: https://cran.r-project.org/package=constrainedKriging Description: CRAN Package 'constrainedKriging' (Constrained, Covariance-Matching Constrained and Universal Pointor Block Kriging) Provides functions for efficient computation of non-linear spatial predictions with local change of support (Hofer, C. and Papritz, A. (2011) "constrainedKriging: An R-package for customary, constrained and covariance-matching constrained point or block kriging" ). This package supplies functions for two-dimensional spatial interpolation by constrained (Cressie, N. (1993) "Aggregation in geostatistical problems" ), covariance-matching constrained (Aldworth, J. and Cressie, N. (2003) "Prediction of nonlinear spatial functionals" ) and universal (external drift) Kriging for points or blocks of any shape from data with a non-stationary mean function and an isotropic weakly stationary covariance function. The linear spatial interpolation methods, constrained and covariance-matching constrained Kriging, provide approximately unbiased prediction for non-linear target values under change of support. This package extends the range of tools for spatial predictions available in R and provides an alternative to conditional simulation for non-linear spatial prediction problems with local change of support. Package: r-cran-construct Architecture: arm64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4471 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_arm64.deb Size: 1447202 MD5sum: b5a4f07fa75a8d4518e16eb65c167713 SHA1: 21fd8f1d8f920149e539c27e24414df5374ecc72 SHA256: 9e592a6fa758a84915165d09f3d8bc05dbff8f545c8861c5a1c6a8ca4bb4e0a9 SHA512: c548d0d9899545d7d27abe2569edba7fa15635d9224d4d737f2754ea159fe380fdae68af40a0294b9e41f568e4c186e5c02c270de7783e1091ef9bffc2915d59 Homepage: https://cran.r-project.org/package=conStruct Description: CRAN Package 'conStruct' (Models Spatially Continuous and Discrete Population GeneticStructure) A method for modeling genetic data as a combination of discrete layers, within each of which relatedness may decay continuously with geographic distance. This package contains code for running analyses (which are implemented in the modeling language 'rstan') and visualizing and interpreting output. See the paper for more details on the model and its utility. Package: r-cran-constructive Architecture: arm64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1557 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_arm64.deb Size: 1242586 MD5sum: bac8d939aebed0d7d33e32370ab8aafb SHA1: dd58e129bdb000833e7fd895bb63fa3617bc4c96 SHA256: 05228f1ccc518268a975931f14ea7ef66c079f21bb1144b05a6190d3d9d4cdd7 SHA512: 9077816442379e3aa25b74e76ebb5085d9b404de72e93139685ad74e788ef397fd80fa1dcd1371ecf6514032b54710fbcdce74b08033cab4bb9f41c43d74d45d Homepage: https://cran.r-project.org/package=constructive Description: CRAN Package 'constructive' (Display Idiomatic Code to Construct Most R Objects) Prints code that can be used to recreate R objects. In a sense it is similar to 'base::dput()' or 'base::deparse()' but 'constructive' strives to use idiomatic constructors. Package: r-cran-contaminatedmixt Architecture: arm64 Version: 1.3.8-1.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 (>= 4.5.0), 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_arm64.deb Size: 158580 MD5sum: 939bccdc3a2b85e4d277539effbcbfd9 SHA1: 374fc7326d96ba7285d8997af7bf279c7fa4864f SHA256: 7fc98f9f303f791d0596e883214d6df7adfdd2b871dc881f175b286e89685520 SHA512: fba16e3e3abec674068e5ce9e1bdb3e8c0e392bf6b42baa8cd016149c6e02756672c7f57940ea78722d36e349704a8e1a083064d999ebd51548001ac8570378a 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. Package: r-cran-coop Architecture: arm64 Version: 0.6-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 586 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgomp1 (>= 6), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-memuse Filename: pool/dists/resolute/main/r-cran-coop_0.6-3-1.ca2604.1_arm64.deb Size: 467816 MD5sum: dccaf1d6371ece4642fad16a23208a02 SHA1: 6232f8459babf7bd4ff721d7e04a264b1f5e5244 SHA256: 7fdbd435525f3d16daf4685e3f444df23ff036cb5176b9fb41ae1496ab38f2d7 SHA512: ea9b3f3af03d33f7c787d7610dbd00e99291a1ba30dbe5a6bd0d271bd1da91e72d8a1c64d017fbf51b7ff6ff753fd366b20995cd28902d2552cdb711f442406b Homepage: https://cran.r-project.org/package=coop Description: CRAN Package 'coop' (Co-Operation: Fast Covariance, Correlation, and CosineSimilarity Operations) Fast implementations of the co-operations: covariance, correlation, and cosine similarity. The implementations are fast and memory-efficient and their use is resolved automatically based on the input data, handled by R's S3 methods. Full descriptions of the algorithms and benchmarks are available in the package vignettes. Package: r-cran-copcar Architecture: arm64 Version: 2.0-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 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-mcmcse, r-cran-numderiv, r-cran-rcpp, r-cran-spam, r-cran-rcpparmadillo Suggests: r-cran-lattice, r-cran-pbapply Filename: pool/dists/resolute/main/r-cran-copcar_2.0-4-1.ca2604.1_arm64.deb Size: 132856 MD5sum: 0f13e619ba31a7d58585f8d2de94023f SHA1: f619bce57fa82cae3de466d9b589fbc32befb089 SHA256: ddc7a8851d32d9857c03c0598e65724abb07547308ad99eebb40502a233100aa SHA512: 5da36978f8e7479b5cf0549e6ae410edae0b999e7831d1f86b5c5c841b6524ae990673c5679f7e3bd2a96215c0b15397518c583faa5036639a959602eaf36887 Homepage: https://cran.r-project.org/package=copCAR Description: CRAN Package 'copCAR' (Fitting the copCAR Regression Model for Discrete Areal Data) Provides tools for fitting the copCAR (Hughes, 2015) regression model for discrete areal data. Three types of estimation are supported (continuous extension, composite marginal likelihood, and distributional transform), for three types of outcomes (Bernoulli, negative binomial, and Poisson). Package: r-cran-cophescan Architecture: arm64 Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4329 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-coloc, r-cran-data.table, r-cran-ggplot2, r-cran-ggrepel, r-cran-pheatmap, r-cran-viridis, r-cran-magrittr, r-cran-matrixstats, r-cran-dplyr, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-rcolorbrewer, r-cran-ggpubr Filename: pool/dists/resolute/main/r-cran-cophescan_1.4.3-1.ca2604.1_arm64.deb Size: 3973148 MD5sum: c552f5cecfc6d76cf6854e26b418e40e SHA1: c24aaa15ac7e77edccf8a0a629276b3ce8541b79 SHA256: c6b568494bf4731c546f202076530a480ba7b81e5c83c79964ab69cca14cf263 SHA512: 62380c64336bddcb2594f3a3465fd3ec7b557b1d1ed5a02ae20df5698367b32be2a912448bdd2cc3c04eda2b314e118ede29f655f90bb3533f5f2c63ca47f227 Homepage: https://cran.r-project.org/package=cophescan Description: CRAN Package 'cophescan' (Adaptation of the Coloc Method for PheWAS) A Bayesian method for Phenome-wide association studies (PheWAS) that identifies causal associations between genetic variants and traits, while simultaneously addressing confounding due to linkage disequilibrium. For details see Manipur et al (2024, Nature Communications) . Package: r-cran-copre Architecture: arm64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2948 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-pracma, r-cran-abind, r-cran-dirichletprocess, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-copre_0.2.2-1.ca2604.1_arm64.deb Size: 2624350 MD5sum: 90202b19b8eefe9d7abdf82c21f78f90 SHA1: cf12a018d39c4cbddf3d9182b572ad1ffa11f7cf SHA256: 1078fbb5ae921e18d2cf5a7096e976f38bcb4029ebebd2440da4f32532fba2e5 SHA512: 35b4c89b74cc78f89f2ff7f07bb3a16f6468ec28bedc0a43d2d934b476d00035236e2116321ee27eb5fc69e39e001332be747c1758076f4dea7cba0a11ad3e46 Homepage: https://cran.r-project.org/package=copre Description: CRAN Package 'copre' (Tools for Nonparametric Martingale Posterior Sampling) Performs Bayesian nonparametric density estimation using Martingale posterior distributions including the Copula Resampling (CopRe) algorithm. Also included are a Gibbs sampler for the marginal Gibbs-type mixture model and an extension to include full uncertainty quantification via a predictive sequence resampling (SeqRe) algorithm. The CopRe and SeqRe samplers generate random nonparametric distributions as output, leading to complete nonparametric inference on posterior summaries. Routines for calculating arbitrary functionals from the sampled distributions are included as well as an important algorithm for finding the number and location of modes, which can then be used to estimate the clusters in the data using, for example, k-means. Implements work developed in Moya B., Walker S. G. (2022). , Fong, E., Holmes, C., Walker, S. G. (2021) , and Escobar M. D., West, M. (1995) . Package: r-cran-copula Architecture: arm64 Version: 1.1-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7353 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-lattice, r-cran-cluster, r-cran-colorspace, r-cran-gsl, r-cran-adgoftest, r-cran-stabledist, r-cran-mvtnorm, r-cran-pcapp, r-cran-pspline, r-cran-numderiv Suggests: r-cran-mass, r-cran-kernsmooth, r-cran-sfsmisc, r-cran-scatterplot3d, r-cran-rmpfr, r-cran-bbmle, r-cran-knitr, r-cran-rmarkdown, r-cran-animation, r-cran-abind, r-cran-crop, r-cran-gridextra, r-cran-hac, r-cran-lcopula, r-cran-mev, r-cran-mvnormtest, r-cran-partitions, r-cran-polynom, r-cran-qrng, r-cran-randtoolbox, r-cran-rugarch, r-cran-runuran, r-cran-tseries, r-cran-vgam, r-cran-vinecopula, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-copula_1.1-7-1.ca2604.1_arm64.deb Size: 5231952 MD5sum: 56f4bc0937797ea822f6ec75103064fc SHA1: 45656634263e25faf9a9048370507ab65ad47400 SHA256: 9ccd4b229cc8502de19b0b0928bda97f738b827c31e2b65787041689559f46ee SHA512: 6870c67299b8288c274d78568394718d6e4fb0784d829bd253713922ddd7a6e8d6a257e609d6c0298dcf407d762c2e0204b68b3a36244e9443767a468018f2bf Homepage: https://cran.r-project.org/package=copula Description: CRAN Package 'copula' (Multivariate Dependence with Copulas) Classes (S4) of commonly used elliptical, Archimedean, extreme-value and other copula families, as well as their rotations, mixtures and asymmetrizations. Nested Archimedean copulas, related tools and special functions. Methods for density, distribution, random number generation, bivariate dependence measures, Rosenblatt transform, Kendall distribution function, perspective and contour plots. Fitting of copula models with potentially partly fixed parameters, including standard errors. Serial independence tests, copula specification tests (independence, exchangeability, radial symmetry, extreme-value dependence, goodness-of-fit) and model selection based on cross-validation. Empirical copula, smoothed versions, and non-parametric estimators of the Pickands dependence function. Package: r-cran-copulagamm Architecture: arm64 Version: 0.6.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-statmod, r-cran-matrixstats Filename: pool/dists/resolute/main/r-cran-copulagamm_0.6.5-1.ca2604.1_arm64.deb Size: 308300 MD5sum: 0482df66829f7a8184c345fe2ebd00b8 SHA1: 554c8f61cc5738af19d3ee383d5e1f18d0328a13 SHA256: 6affe11708ee3b6a843929f21ead45eeba713be399a434adfcaa6de7732cfef4 SHA512: cc1153da38cc647c10a4a421decf4c805d8582524936019b66281e9c91291ce31c98a6c5d6fa741c83171c0ebe403923be2c5207139b187b096cda7d5b460019 Homepage: https://cran.r-project.org/package=CopulaGAMM Description: CRAN Package 'CopulaGAMM' (Copula-Based Mixed Regression Models) Estimation of 2-level factor copula-based regression models for clustered data where the response variable can be either discrete or continuous. 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The copula families considered here are the Gaussian, Student, Clayton, Frank, Gumbel, Joe, Plackett, BB1, BB6, BB7,BB8, together with the following non-central squared copula families in Nasri (2020) : ncs-gaussian, ncs-clayton, ncs-gumbel, ncs-frank, ncs-joe, and ncs-plackett. For theoretical details, see, e.g., Nasri and Remillard (2023) . 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See the file 'AUTHORS' for a list of copyright holders and contributors. 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Package: r-cran-coxboost Architecture: arm64 Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 354 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_arm64.deb Size: 250792 MD5sum: ff37ff3a2445a037fd3ae14dcbd3dfcd SHA1: d46d52b6e98f4c6fc7c8fc33b8b6c3c27a04b7f3 SHA256: 74da477a09fd2f2def2c495aceb3ba35944e4b781f35cc8ec8471fba5d705457 SHA512: 2f9046ff5afe519985bef71688d5502897aaa4dbbdea218e79c7683890b47c820f67c18a46f8bf4d0c6a5677a39ebacd74b6f65c9a67e5fc7852857fbe7895c3 Homepage: https://cran.r-project.org/package=CoxBoost Description: CRAN Package 'CoxBoost' (Cox Models by Likelihood Based Boosting for a Single SurvivalEndpoint or Competing Risks) Provides routines for fitting Cox models by likelihood based boosting for single event survival data with right censoring or in the presence of competing risks. The methodology is described in Binder and Schumacher (2008) and Binder et al. (2009) . Package: r-cran-coxerr Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-coxerr_1.1-1.ca2604.1_arm64.deb Size: 25426 MD5sum: 4d5e7328166a4b7dd75a42a0fce0c510 SHA1: 9a471506d0c463c71b9775999afd97816abef5f1 SHA256: 07ca86504b9dd8b31225ce919420241508f7363cf9d019c8a2877bf19eaf1f2c SHA512: f9c1e4e1902a903b9c88102f11deeab9a55d495ee9cb8de21b4f537d523f7ca1b98f0649fda0d312f8e414bb7309efc6bdeea970fc530e078e186ed25209fb0d Homepage: https://cran.r-project.org/package=coxerr Description: CRAN Package 'coxerr' (Cox Regression with Dependent Error in Covariates) Perform the functional modeling methods of Huang and Wang (2018) to accommodate dependent error in covariates of the proportional hazards model. 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The random effects can have a general form, of which familial interactions (a "kinship" matrix) is a particular special case. Note that the simplest case of a mixed effects Cox model, i.e. a single random per-group intercept, is also called a "frailty" model. The approach is based on Ripatti and Palmgren, Biometrics 2002. Package: r-cran-coxmos Architecture: arm64 Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5630 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-caret, r-cran-cowplot, r-cran-furrr, r-cran-future, r-cran-ggrepel, r-cran-ggplot2, r-cran-ggpubr, r-cran-glmnet, r-cran-mass, r-bioc-mixomics, r-cran-patchwork, r-cran-progress, r-cran-purrr, r-cran-rdpack, r-cran-scattermore, r-bioc-survcomp, r-cran-survival, r-cran-survminer, r-cran-svglite, r-cran-tidyr Suggests: r-cran-ggforce, r-cran-knitr, r-cran-nsroc, r-cran-rcolorconesa, r-cran-risksetroc, r-cran-rmarkdown, r-cran-smoothroctime, r-cran-survivalroc Filename: pool/dists/resolute/main/r-cran-coxmos_1.1.5-1.ca2604.1_arm64.deb Size: 4150584 MD5sum: 57b80081e88a378caf758a09b5b4be6f SHA1: d0a8f2053bc99482ac4e6df9908e93b8d041bd14 SHA256: 9f61255e8339f15d84c31cbfb8964b0f6120343c1ae45c271a722b783330c06a SHA512: b6be32055f701f3d1c684fe291ea95223ba04ee7d7e9b9711c22b9edb3c689177d6dd9b11a0de12359b079a4dd629d3fb61ab90b42f5d2dbec3a89511a67404c Homepage: https://cran.r-project.org/package=Coxmos Description: CRAN Package 'Coxmos' (Cox MultiBlock Survival) This software package provides Cox survival analysis for high-dimensional and multiblock datasets. It encompasses a suite of functions dedicated from the classical Cox regression to newest analysis, including Cox proportional hazards model, Stepwise Cox regression, and Elastic-Net Cox regression, Sparse Partial Least Squares Cox regression (sPLS-COX) incorporating three distinct strategies, and two Multiblock-PLS Cox regression (MB-sPLS-COX) methods. This tool is designed to adeptly handle high-dimensional data, and provides tools for cross-validation, plot generation, and additional resources for interpreting results. While references are available within the corresponding functions, key literature is mentioned below. Terry M Therneau (2024) , Noah Simon et al. (2011) , Philippe Bastien et al. (2005) , Philippe Bastien (2008) , Philippe Bastien et al. (2014) , Kassu Mehari Beyene and Anouar El Ghouch (2020) , Florian Rohart et al. (2017) . Package: r-cran-coxphf Architecture: arm64 Version: 1.13.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 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_arm64.deb Size: 91972 MD5sum: 7010427f792d5c650a349bd8343c86a7 SHA1: a65c6fd1dee2393b7bb0bfb9e471a37c0a9a4514 SHA256: 066fc3a62271f65caea0e0dcedac87c12d6b1d310aea919259de5676cf7d6e2b SHA512: f48d31975e4e1a8506bafdc1a8b6b0f3cefcaeffc8d7c14d41a4e3199ae9b8154de2c88226226c5fee3910ac2f93e3436b1a74ec03c5c379c2f1f7ddbd248855 Homepage: https://cran.r-project.org/package=coxphf Description: CRAN Package 'coxphf' (Cox Regression with Firth's Penalized Likelihood) Implements Firth's penalized maximum likelihood bias reduction method for Cox regression which has been shown to provide a solution in case of monotone likelihood (nonconvergence of likelihood function), see Heinze and Schemper (2001) and Heinze and Dunkler (2008). The program fits profile penalized likelihood confidence intervals which were proved to outperform Wald confidence intervals. Package: r-cran-coxphw Architecture: arm64 Version: 4.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 612 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-coxphw_4.0.3-1.ca2604.1_arm64.deb Size: 268262 MD5sum: 945268b039ea101428a949abdf76a8c0 SHA1: 2c1b2370c6cbb63a55ee08038f623b84360a9ca1 SHA256: be67c0437435fc760ed872fc6e7984db4e70e525e69fe7abe7c717c67cae7b4d SHA512: e39b10a45527331cb1bcab6aee6ed93b0f97c35c4410d5c71c91abddaecfa105a5d1afb1519f4ad6fe946e425a20913cfbcc23ca1136941cc98b930eea8b92ff Homepage: https://cran.r-project.org/package=coxphw Description: CRAN Package 'coxphw' (Weighted Estimation in Cox Regression) Implements weighted estimation in Cox regression as proposed by Schemper, Wakounig and Heinze (Statistics in Medicine, 2009, ) and as described in Dunkler, Ploner, Schemper and Heinze (Journal of Statistical Software, 2018, ). 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Package: r-cran-cpm Architecture: arm64 Version: 2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1740 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-cpm_2.3-1.ca2604.1_arm64.deb Size: 1355196 MD5sum: 073f815c95f90798084eae47ed84fc66 SHA1: 3a885f2befde35ee5e0bf29351c66f98c4cb417c SHA256: fd5052000e0a991313e5ab1378e31a6cb480b476591029f48b9d3acfb7b006ca SHA512: 432e1bdfe96d5c61c41c362af13c41b255172b7ac13ca1a43f7a67712c7cf7fa1b9353ace69d315fe359562a750c16979d2c11fedf059f43e22f2f20e3302a18 Homepage: https://cran.r-project.org/package=cpm Description: CRAN Package 'cpm' (Sequential and Batch Change Detection Using Parametric andNonparametric Methods) Sequential and batch change detection for univariate data streams, using the change point model framework. Functions are provided to allow nonparametric distribution-free change detection in the mean, variance, or general distribution of a given sequence of observations. Parametric change detection methods are also provided for Gaussian, Bernoulli and Exponential sequences. Both the batch (Phase I) and sequential (Phase II) settings are supported, and the sequences may contain either a single or multiple change points. A full description of this package is available in Ross, G.J (2015) - "Parametric and nonparametric sequential change detection in R" available at . Package: r-cran-cpop Architecture: arm64 Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 458 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-crops, r-cran-pacman, r-cran-rdpack, r-cran-rcpp, r-cran-ggplot2, r-cran-mathjaxr, r-cran-pracma Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cpop_1.0.8-1.ca2604.1_arm64.deb Size: 285138 MD5sum: 40bd5c04be71b6bb05bc9004967fc9fc SHA1: 3589540d6804011db44a91049bccddd948e05a10 SHA256: f81081574c862a884fd226fb535fb977e144b74971f136f61b34d109024899bd SHA512: cfc48617f947131a5cfa03cf2d30bb3b4fa25cab66604cfa27e092be1bcc5bef3ee6c85f99351616fbc8bf6249bbcd05bd70532ff1a6cd5e4f803884b48427f9 Homepage: https://cran.r-project.org/package=cpop Description: CRAN Package 'cpop' (Detection of Multiple Changes in Slope in Univariate Time-Series) Detects multiple changes in slope using the CPOP dynamic programming approach of Fearnhead, Maidstone, and Letchford (2019) . This method finds the best continuous piecewise linear fit to data under a criterion that measures fit to data using the residual sum of squares, but penalizes complexity based on an L0 penalty on changes in slope. Further information regarding the use of this package with detailed examples can be found in Fearnhead and Grose (2024) . 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For more details see 'de Paz' (2024) and 'Loh' (2011) . Package: r-cran-cpp11bigwig Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-genomicranges, r-bioc-iranges, r-cran-tibble, r-cran-cpp11 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cpp11bigwig_0.1.3-1.ca2604.1_arm64.deb Size: 112602 MD5sum: 953d4e92586082289d3a02ee9cc75125 SHA1: 89b8e82c30db1aa2f8d16611e3e6c07fb329907e SHA256: 70e114661e5e45d1ef845f361d22672c889480d00569286bef9d5bfe0879d0ef SHA512: 5c5b6aa995321dccc6136593e5055d56e6e7101919ee7745633708c1302ca43ced4b635081c3947cd84a9634b08410a958304a236a0a921fe0482396f5fa29a8 Homepage: https://cran.r-project.org/package=cpp11bigwig Description: CRAN Package 'cpp11bigwig' (Read bigWig and bigBed Files) Read bigWig and bigBed files using "libBigWig" . 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Package: r-cran-cpp11qpdf Architecture: arm64 Version: 1.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1552 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libjpeg8 (>= 8c), libstdc++6 (>= 13.1), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-curl, r-cran-cpp11 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cpp11qpdf_1.3.5-1.ca2604.1_arm64.deb Size: 627994 MD5sum: 6e022fb94d38bd9f0c1d8f5de4622bc0 SHA1: 7019f4baf09263a91a7c667379025fb0f0f4689d SHA256: 6c52b71d2ebc2ebd25cf35eacc3f236277765402106227ef88b0a8e9250709dc SHA512: e2448da1edb748c5c6dcc21528b850b410b2a77f2b89bd28f5169d5a1473b6c58b5d46ff57bed3f1441e10a8e5a3bcf3b4917bd01f944b073dc60651478e9956 Homepage: https://cran.r-project.org/package=cpp11qpdf Description: CRAN Package 'cpp11qpdf' (Split, Combine and Compress PDF Files) Bindings to 'qpdf': 'qpdf' () is a an open-source PDF rendering library that allows to conduct content-preserving transformations of PDF files such as split, combine, and compress PDF files. 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Includes sets, unordered sets, multisets, unordered multisets, maps, unordered maps, multimaps, unordered multimaps, stacks, queues, priority queues, vectors, deques, forward lists, and lists. Package: r-cran-cppdoubles Architecture: arm64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-bench, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cppdoubles_0.4.0-1.ca2604.1_arm64.deb Size: 41180 MD5sum: 02072d5e75ca0e4346eb6f06c0ed03b0 SHA1: ff02bf42099a87855c4a8a337bb57a1b89c8a4c6 SHA256: f0af770e02431b940eeb4968ba3fa0ece2dbacfe40fe916a6b8ec8aed30c42f6 SHA512: bf7e9cc03330aede05c002f4bee2b3d956e930406be245c8812b98ee27b39b505aa7bce8cc544873acca7f9f2515a0d63035277954046014690ac6b698f78fd8 Homepage: https://cran.r-project.org/package=cppdoubles Description: CRAN Package 'cppdoubles' (Fast Relative Comparisons of Floating Point Numbers in 'C++') Compare double-precision floating point vectors using relative differences. All equality operations are calculated using 'cpp11'. Package: r-cran-cpprouting Architecture: arm64 Version: 3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 685 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_arm64.deb Size: 297570 MD5sum: 046abcf0849cd434bd26a0ec541e0fe5 SHA1: 9dd70a68f7f6a7b299d625f3be9e37a58af72bf6 SHA256: 99061535650ad6d86e6473125a652a73766777c1a177a2388a0f186c92c9dd6d SHA512: 30310ef308558266ef01c0c2b0dd5f6418c0521ffaabf29357d9be31762db5269cc46bde17776bb87faa29b19f81baa6e925052a9d48e375bfbdd60ac7e0f101 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-crandep Architecture: arm64 Version: 0.3.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4081 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-stringr, r-cran-dplyr, r-cran-igraph, r-cran-rcpp, r-cran-pracma, r-cran-gsl, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-tibble, r-cran-visnetwork, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-crandep_0.3.13-1.ca2604.1_arm64.deb Size: 2193942 MD5sum: b25c3ab19635a289a87d750c481246d7 SHA1: 2f628cdcdcc42bd244d1e3ec106de32faebbd2ad SHA256: 5bde581b2e1b07bc9ed7aaa0022591ba10f68f1dca3d6b829ea84c8603fb9e78 SHA512: 97ecbb4b4e8c9dc360f122c8a2cc558dc62d890ddfcad8417b1ec654dab35dcbe0d9c27405e5ddcb18fa2c238f6e437130e93c020527e84306b4629aa284ad51 Homepage: https://cran.r-project.org/package=crandep Description: CRAN Package 'crandep' (Network Analysis of Dependencies of CRAN Packages) The dependencies of CRAN packages can be analysed in a network fashion. For each package we can obtain the packages that it depends, imports, suggests, etc. By iterating this procedure over a number of packages, we can build, visualise, and analyse the dependency network, enabling us to have a bird's-eye view of the CRAN ecosystem. One aspect of interest is the number of reverse dependencies of the packages, or equivalently the in-degree distribution of the dependency network. This can be fitted by the power law and/or an extreme value mixture distribution , of which functions are provided. Package: r-cran-crawl Architecture: arm64 Version: 2.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1224 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-dplyr, r-cran-sf, r-cran-sp, r-cran-tibble, r-cran-magrittr, r-cran-lubridate, r-cran-purrr, r-cran-rlang, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-crawl_2.3.1-1.ca2604.1_arm64.deb Size: 858588 MD5sum: 1134248fd296b47bb7577e0492ee7d24 SHA1: 3c2bc6af0ed058139dda4cb60479b1921886d83f SHA256: b88a5753ac9d3c79b88678ea1019f6c9e2f24906ac81ca9aaa9e48d8c54e863a SHA512: 5488001b59d21d6861f9874b65e747d3990b75a6e0990992202b24b962495a92e2b8ba58eece89daf4c240649befaa7d95d45a2dec4b8aa82b0cbd05e57fab14 Homepage: https://cran.r-project.org/package=crawl Description: CRAN Package 'crawl' (Fit Continuous-Time Correlated Random Walk Models to AnimalMovement Data) Fit continuous-time correlated random walk models with time indexed covariates to animal telemetry data. The model is fit using the Kalman-filter on a state space version of the continuous-time stochastic movement process. Package: r-cran-crc32c Architecture: arm64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidycpp Filename: pool/dists/resolute/main/r-cran-crc32c_0.0.3-1.ca2604.1_arm64.deb Size: 24892 MD5sum: f0ed87a2c56c4f9b3c5b5e213abfd901 SHA1: 27cddcb92981d405702b0bcb22389fb040013af0 SHA256: 7306fbcff27e5e74a948aeb1e7180f1977bdcdf1e2973ef9739f7e414d4fdb71 SHA512: a5a97a47e0c336608fb9e643a5527fbbdb3eea60b48d83a4def9a618303c65196778f897eeb88b784f9c036b189f26a54756949c7eab847ab593cb909edc75f6 Homepage: https://cran.r-project.org/package=crc32c Description: CRAN Package 'crc32c' (Cyclic Redundancy Check with CPU-Specific Acceleration) Hardware-based support for 'CRC32C' cyclic redundancy checksum function is made available for 'x86_64' systems with 'SSE2' support as well as for 'arm64', and detected at build-time via 'cmake' with a software-based fallback. This functionality is exported at the 'C'-language level for use by other packages. 'CRC32C' is described in 'RFC 3270' at and is based on 'Castagnoli et al' . Package: r-cran-crch Architecture: arm64 Version: 1.2-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2352 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-ordinal, r-cran-sandwich, r-cran-scoringrules Suggests: r-cran-distributions3, r-cran-glmx, r-cran-knitr, r-cran-lmtest, r-cran-memisc, r-cran-quarto Filename: pool/dists/resolute/main/r-cran-crch_1.2-2-1.ca2604.1_arm64.deb Size: 1844032 MD5sum: a80e536c8f8c4b82b5730bc39741c2b1 SHA1: 4346617591586afded35182eb1322f54847b9046 SHA256: dc0230578296b90066d887a78b1ac16e28e7e3b90b358001dc67eeb6c373e0d0 SHA512: e0f06b04152620157845ee17aef8949ec1a9781ac30ea10f394925f4a70c7dec2c0d614d45c6f75dc738d854af3f72275527c3a6b4dea841298e1a3ce57d2cc0 Homepage: https://cran.r-project.org/package=crch Description: CRAN Package 'crch' (Censored Regression with Conditional Heteroscedasticity) Different approaches to censored or truncated regression with conditional heteroscedasticity are provided. First, continuous distributions can be used for the (right and/or left censored or truncated) response with separate linear predictors for the mean and variance. Second, cumulative link models for ordinal data (obtained by interval-censoring continuous data) can be employed for heteroscedastic extended logistic regression (HXLR). In the latter type of models, the intercepts depend on the thresholds that define the intervals. Infrastructure for working with censored or truncated normal, logistic, and Student-t distributions, i.e., d/p/q/r functions and distributions3 objects. Package: r-cran-crctstepdown Architecture: arm64 Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 670 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastglm, r-cran-rcpp, r-cran-ggplot2, r-cran-ggpubr, r-cran-stringr, r-cran-lme4, r-cran-reshape2, r-cran-rcppeigen, r-cran-rcppparallel Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-crctstepdown_0.5.2-1.ca2604.1_arm64.deb Size: 255634 MD5sum: 976647ab17aedfc3faf601c33f66b148 SHA1: 4b4fd357283de56ac508f7dac5757e0d2a50062e SHA256: 0b5a00a381529c2487ab7ae99c5e48f9d4d21f2453c408ee2cd11b0c1093481d SHA512: 51fded65d204ea603f07b831f29b08f7d4a1b42a779aa4480795f7007ccafbc2b750d8d9495f4ebf534cc7e470b1c3f31da79ef173eef401b11ab7f2d5e377a4 Homepage: https://cran.r-project.org/package=crctStepdown Description: CRAN Package 'crctStepdown' (Univariate Analysis of Cluster Trials with Multiple Outcomes) Frequentist statistical inference for cluster randomised trials with multiple outcomes that controls the family-wise error rate and provides nominal coverage of confidence sets. A full description of the methods can be found in Watson et al. (2023) . Package: r-cran-credule Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-credule_0.1.4-1.ca2604.1_arm64.deb Size: 58810 MD5sum: a13a07488847c5591e1521392d3c8596 SHA1: ba1faeaef44846e1ec01e220b17b7e2956a1798d SHA256: e6e0640d9ca35c9ec0dd9607dac7d1aa028327cdb24d5b99f77d907b1b492edd SHA512: ba4493a29c9794c162abf9ac5b4a4735e46a4f38b22fc261b9c55e2794581b0626c4e33ba42d8fa2134b167afc8fdeb4e5389836e73e1e797b1ee23cc7f4b888 Homepage: https://cran.r-project.org/package=credule Description: CRAN Package 'credule' (Credit Default Swap Functions) It provides functions to bootstrap Credit Curves from market quotes (Credit Default Swap - CDS - spreads) and price Credit Default Swaps - CDS. Package: r-cran-crimcv Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-crimcv_1.0.0-1.ca2604.1_arm64.deb Size: 227448 MD5sum: 5d97789257e3c43c5beab7ebbaba031e SHA1: cb81012ae34c9d0725c27caa9c1e17199e997b87 SHA256: 42853a96dc2d7deaf74273d5482631ac4358d47004825ee2e36887589a6b48e2 SHA512: c599f57cef9a1927f526a0bb5d2003cea3daf984108d796fcb2c48139855018f321abe562ccc68a7c0c8116f56ad26b3c79478a1eca6ebcbfc97d4cee3f72167 Homepage: https://cran.r-project.org/package=crimCV Description: CRAN Package 'crimCV' (Group-Based Modelling of Longitudinal Data) A finite mixture of Zero-Inflated Poisson (ZIP) models for analyzing criminal trajectories. Package: r-cran-crossover Architecture: arm64 Version: 0.1-22-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1568 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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-mass, r-cran-crossdes, r-cran-xtable, r-cran-matrix, r-cran-rjava, r-cran-commonjavajars, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-javagd, r-cran-multcomp, r-cran-digest Suggests: r-cran-knitr, r-cran-testthat, r-cran-nlme Filename: pool/dists/resolute/main/r-cran-crossover_0.1-22-1.ca2604.1_arm64.deb Size: 1074706 MD5sum: e7e27fd004c7ac305f216e7a5b030aee SHA1: 486ae261f8e7616b69b4b2003a1ee242e017d4ad SHA256: 00ec5a0d3774ea0cf461fc3b606bb423e71956e33c8f65925591e68a431af90d SHA512: cc27531b699f0837cd04a4acb72e77eb76eff3b9d3564169c292d2c2b3370a06d719e103bc2f520e2239481158d226d69bce93db3619886a3ca4955437a1ba37 Homepage: https://cran.r-project.org/package=Crossover Description: CRAN Package 'Crossover' (Analysis and Search of Crossover Designs) Generate and analyse crossover designs from combinatorial or search algorithms as well as from literature and a GUI to access them. Package: r-cran-crownscorchtls Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 640 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lidr, r-cran-randomforest, r-cran-tidyr, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-boruta Filename: pool/dists/resolute/main/r-cran-crownscorchtls_0.1.1-1.ca2604.1_arm64.deb Size: 497204 MD5sum: 8ec31f266d974d12a5361737bde4660b SHA1: 10b8d5d65ccb32b746f4b1a8a126c89ad91cf821 SHA256: 4392d799e6eace8aa0a65705fcdc8fa1ea3f99d37087fd2c7126fe7f7ca4123f SHA512: 76944456aef2ce43b3abccfde79f4925bdc99c55aa10eacf401dfef479827d1bec7cd268d09e7a20c5e93d4361ed5c55f1c70223924c347e71044b86bca7f2b9 Homepage: https://cran.r-project.org/package=CrownScorchTLS Description: CRAN Package 'CrownScorchTLS' (Estimate Crown Scorch from Terrestrial LiDAR Scans) Estimates tree crown scorch from terrestrial lidar scans collected with a RIEGL vz400i. The methods follow those described in Cannon et al. (2025, Fire Ecology 21:71, ). Package: r-cran-crownsegmentr Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-assertthat, r-cran-data.table, r-cran-dbscan, r-cran-lidr, r-cran-rcpp, r-cran-sf, r-cran-terra, r-cran-bh, r-cran-progress Suggests: r-bioc-ebimage, r-cran-future, r-cran-testthat, r-cran-raster Filename: pool/dists/resolute/main/r-cran-crownsegmentr_1.0.1-1.ca2604.1_arm64.deb Size: 208122 MD5sum: 82b555da22d0f27e2335ba32d0d8ae0e SHA1: 42ba5d05ded344d40b0c9ca90197db3dc43e363a SHA256: e126c4b3daa012a76147867a51bd06c82ff1a34dcfbee43294cf30555901dc9e SHA512: 5646022c3365a297a73ebab9dce207e60143d40470cb4e6ff8239186f5cd9d49034c696a154b14ee1b66af68a599b771ca96a58578d38611eab2f50f933528e5 Homepage: https://cran.r-project.org/package=crownsegmentr Description: CRAN Package 'crownsegmentr' (Tree Crown Segmentation in Airborne LiDAR Point Clouds) Provides a function that performs the adaptive mean shift algorithm for individual tree crown delineation in 3D point clouds as proposed by Ferraz et al. (2016) , as well as supporting functions. Package: r-cran-crqa Architecture: arm64 Version: 2.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-pracma, r-cran-rdist, r-cran-tserieschaos, r-cran-gplots, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-crqa_2.0.7-1.ca2604.1_arm64.deb Size: 321924 MD5sum: edd8b4371446cce6539c390c7a6ce3e1 SHA1: 398214fee6dfbea1ae4b4623b86effe9c76151c5 SHA256: 9751fa0d0966f7f4a613c92f04af4ffc6dd8a84e95193ebf1d4dbb21b92a3294 SHA512: c808da4ae8ae286c9d70c7198bdc0d579e93783d56d03b2f8168dad06dca2e059fcbb2a9107799d76005ef42e4e66580aaea411bed24939edd9273c3088615ae Homepage: https://cran.r-project.org/package=crqa Description: CRAN Package 'crqa' (Unidimensional and Multidimensional Methods for RecurrenceQuantification Analysis) Auto, Cross and Multi-dimensional recurrence quantification analysis. Different methods for computing recurrence, cross vs. multidimensional or profile iti.e., only looking at the diagonal recurrent points, as well as functions for optimization and plotting are proposed. in-depth measures of the whole cross-recurrence plot, Please refer to Coco and others (2021) , Coco and Dale (2014) and Wallot (2018) for further details about the method. Package: r-cran-crrsc Architecture: arm64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-crrsc_1.1.2-1.ca2604.1_arm64.deb Size: 85746 MD5sum: 7af47d8e1425f6d1c6eedd9821f46e7c SHA1: 3b215cb86b033e3fed074d817b02d544bfbe3828 SHA256: 64f1906a33222a7c8d0e3fde8f91ecc3d75d8c2212d0c97bc7bada837c80f972 SHA512: 0315930c33b6a0f40df1971bdd68bc989b451c3b8cf361c7fb92d073ade9bd596346e571f213d05f3f5aac3614f846900c4ae282a1c4ea3ce0aedf3c7528a6a1 Homepage: https://cran.r-project.org/package=crrSC Description: CRAN Package 'crrSC' (Competing Risks Regression for Stratified and Clustered Data) Extension of 'cmprsk' to Stratified and Clustered data. A goodness of fit test for Fine-Gray model is also provided. Methods are detailed in the following articles: Zhou et al. (2011) , Zhou et al. (2012) , Zhou et al. (2013) . Package: r-cran-crs Architecture: arm64 Version: 0.15-43-1.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-base-core (>= 4.6.0), r-api-4.0, r-cran-boot, r-cran-quantreg Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-logspline, r-cran-mass, r-cran-quadprog, r-cran-rgl, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-crs_0.15-43-1.ca2604.1_arm64.deb Size: 2609628 MD5sum: 35b9b55f8904837a84d29039c4f42f6c SHA1: a126311fee31fb0f5d8bfdb1d32d763630bee237 SHA256: 088d6df8b5846796b8e0e0993604bf52fb43cf59945a1e6ea2e671d2063a950d SHA512: 7ff6b1bec3e5469d317e73005c793eddebb463bf80e6172827fb66521d54773796480cc47a6cc929a1270484bca7d7e7640940a84a85dbe9cc807ab48f34ff8c Homepage: https://cran.r-project.org/package=crs Description: CRAN Package 'crs' (Categorical Regression Splines) Regression splines that handle a mix of continuous and categorical (discrete) data often encountered in applied settings. I would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, ), the Social Sciences and Humanities Research Council of Canada (SSHRC, ), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, ). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints. Package: r-cran-crtconjoint Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 374 Depends: libc6 (>= 2.29), 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 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-crtconjoint_0.1.0-1.ca2604.1_arm64.deb Size: 189940 MD5sum: c654056c20a3abbe575b092d74d31c37 SHA1: 753fe6e3bfbc5b3628301ff3107f4a510a611390 SHA256: 14ccde2ca4f8176e81b1630083981aa6e3269201b83be97ee74ed3a2013f93f7 SHA512: 8737720e12a271969b39e0e7556301826e67b9fe7a47ced4ed574f484838d193b6cf21ac99781ae0a42a697fdee577fd6f806d160fca966cdcd57519104eac76 Homepage: https://cran.r-project.org/package=CRTConjoint Description: CRAN Package 'CRTConjoint' (Conditional Randomization Testing (CRT) Approach for ConjointAnalysis) Computes p-value according to the CRT using the HierNet test statistic. For more details, see Ham, Imai, Janson (2022) "Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis" . Package: r-cran-cryptorng Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cryptorng_0.1.4-1.ca2604.1_arm64.deb Size: 15874 MD5sum: 7580524d4f59656c99f585839f95c834 SHA1: a61e15c32718cd766a946339219e64eaebdd8715 SHA256: d25bfefb0426e7a117d4e3be79b981986da14a38663bd941cbca95fea7abf898 SHA512: a74daa6b9c1cb1cdc63c3fadbbe6eb64f02b075edff8f076264fdd75e8dedefd6a6b6a8cac6e5d465777c91cfa8e3432fbd13e6d592bd2e3e36b6d9bcc534b2f Homepage: https://cran.r-project.org/package=cryptorng Description: CRAN Package 'cryptorng' (Access System Cryptographic Pseudorandom Number Generators) Generate random numbers from the Cryptographically Secure Pseudorandom Number Generator (CSPRNG) provided by the underlying operating system. System CSPRNGs are seeded internally by the OS with entropy it gathers from the system hardware. The following system functions are used: arc4random_buf() on macOS and BSD; BCryptgenRandom() on Windows; Sys_getrandom() on Linux. Package: r-cran-csem Architecture: arm64 Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2426 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-alabama, r-cran-cli, r-cran-crayon, r-cran-expm, r-cran-future.apply, r-cran-future, r-cran-lifecycle, r-cran-lavaan, r-cran-magrittr, r-cran-mass, r-cran-matrix, r-cran-matrixcalc, r-cran-matrixstats, r-cran-polycor, r-cran-progressr, r-cran-psych, r-cran-purrr, r-cran-rdpack, r-cran-rlang, r-cran-symmoments, r-cran-truncatednormal Suggests: r-cran-diagrammer, r-cran-diagrammersvg, r-cran-dplyr, r-cran-tidyr, r-cran-knitr, r-cran-nnls, r-cran-prettydoc, r-cran-plotly, r-cran-rsvg, r-cran-rmarkdown, r-cran-rootsolve, r-cran-listviewer, r-cran-testthat, r-cran-ggplot2, r-cran-openxlsx, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-csem_0.6.1-1.ca2604.1_arm64.deb Size: 1855568 MD5sum: a0bda7ae9ac7e448a5fc16808102c4c0 SHA1: 76d73eb1817413a0788791b2d5992c4eb35c533f SHA256: 727739c6ec8a069173dd45a739e6a71985d3aebf265d1717c1686c3040158801 SHA512: e8aff6d8cdf3387663816d43acd88ff61de91206fd021375c05ebcac2afd231486e13f022038a8f9aef60efea900e05a49085699b13855f3b1f48b831497e9f8 Homepage: https://cran.r-project.org/package=cSEM Description: CRAN Package 'cSEM' (Composite-Based Structural Equation Modeling) Estimate, assess, test, and study linear, nonlinear, hierarchical and multigroup structural equation models using composite-based approaches and procedures, including estimation techniques such as partial least squares path modeling (PLS-PM) and its derivatives (PLSc, ordPLSc, robustPLSc), generalized structured component analysis (GSCA), generalized structured component analysis with uniqueness terms (GSCAm), generalized canonical correlation analysis (GCCA), principal component analysis (PCA), factor score regression (FSR) using sum score, regression or Bartlett scores (including bias correction using Croon’s approach), as well as several tests and typical postestimation procedures (e.g., verify admissibility of the estimates, assess the model fit, test the model fit etc.). Package: r-cran-cseqtl Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2008 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-smarter, r-cran-ggplot2, r-cran-multcomp, r-cran-emdbook, r-cran-matrixeqtl, r-cran-data.table, r-cran-helpersmg, r-cran-r.utils, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-cseqtl_1.0.1-1.ca2604.1_arm64.deb Size: 824854 MD5sum: f437bae0ec952aa530849a9a67d72f93 SHA1: 5f961712335f1f6d1d0beb20e188dd916a281847 SHA256: bbe5d7ea7c55f5b3cea61c9e7a57b6d560f73f2cbd6fb020f294575cc10a5d04 SHA512: e37311ed7a6289ed235ce85aff8ead141b8df1ee95238e48dbc14e69a31ed13d72ff6404a0bec6296e1c793d651c14c81ce2a5c4be3936ae40eae2c1fcbdf7f8 Homepage: https://cran.r-project.org/package=CSeQTL Description: CRAN Package 'CSeQTL' (Cell Type-Specific Expression Quantitative Trait Loci Mapping) Perform bulk and cell type-specific expression quantitative trait loci mapping with our novel method (Little et al. 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Package: r-cran-ctmed Architecture: arm64 Version: 1.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 944 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-numderiv, r-cran-simstatespace, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-expm, r-cran-dynr, r-cran-betadelta, r-cran-bootstatespace Filename: pool/dists/resolute/main/r-cran-ctmed_1.0.9-1.ca2604.1_arm64.deb Size: 678098 MD5sum: a72994594c11ae99aa9b1b7d08670817 SHA1: 1895a502aaacea106423f19fb6e01a10b37e71e6 SHA256: fcb427290222ec1e10c686dc1340d5f2a0f0e6e05b10ee402a7537df06a1366c SHA512: b6f38f143531abe5d752d46550fe4d6dfe58b1860a15bd6b629b99ae1fb4a6bd366c3882ca25bd5c6011454ba4906d55130d97671f71275f8673a42d83a965ee Homepage: https://cran.r-project.org/package=cTMed Description: CRAN Package 'cTMed' (Continuous-Time Mediation) Computes effect sizes, standard errors, and confidence intervals for total, direct, and indirect effects in continuous-time mediation models as described in Pesigan, Russell, and Chow (2025) . 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Package: r-cran-ctsem Architecture: arm64 Version: 3.10.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10987 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_arm64.deb Size: 5397060 MD5sum: 4ad64236a52a559e6616cfaaaec2bc83 SHA1: 95842244d63c799ba99aeb9ee4cf6151595873de SHA256: a2b1f9ef8ca64dd97a2f41b01d3af7c21688ccebc614e3094788ff66430b8aac SHA512: 1c205847e4e85350a92c4383276d5a26ec24ab488196b6b8d1d7e48777051c183751d3b6f48bc61be22f1fa88e60a1df022195cdfca8ccb181b0aa230c08d92f Homepage: https://cran.r-project.org/package=ctsem Description: CRAN Package 'ctsem' (Continuous Time Structural Equation Modelling) Hierarchical continuous (and discrete) time state space modelling, for linear and nonlinear systems measured by continuous variables, with limited support for binary data. The subject specific dynamic system is modelled as a stochastic differential equation (SDE) or difference equation, measurement models are typically multivariate normal factor models. Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default. Nonlinearities, (state dependent parameters) and random effects on all parameters are possible, using either max likelihood / max a posteriori optimization (with optional importance sampling) or Stan's Hamiltonian Monte Carlo sampling. See for details. See for a detailed tutorial. Priors may be used. For the conceptual overview of the hierarchical Bayesian linear SDE approach, see . Exogenous inputs may also be included, for an overview of such possibilities see . contains some tutorial blog posts. Package: r-cran-ctsmtmb Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2351 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_arm64.deb Size: 1544110 MD5sum: 7e101eb13993adc93fdc16a63d57091f SHA1: e3f210e5069eefcd0e7b4a7396b3553651778479 SHA256: 76494c5f2dc179dc1b1160e3097420ed0d9f15f6d3690c9e5aaabe1ae3857465 SHA512: cb6b842c0ba76b101f68625d3b2b5b25b1e29110984a20c3c23c2152bc78be162dd963c9bf4f76cd3c4c9401c12d359bddbf7c52af3e8cde6e252a9b2f07b972 Homepage: https://cran.r-project.org/package=ctsmTMB Description: CRAN Package 'ctsmTMB' (Continuous Time Stochastic Modelling using Template ModelBuilder) Perform state and parameter inference, and forecasting, in stochastic state-space systems using the 'ctsmTMB' class. This class, built with the 'R6' package, provides a user-friendly interface for defining and handling state-space models. Inference is based on maximum likelihood estimation, with derivatives efficiently computed through automatic differentiation enabled by the 'TMB'/'RTMB' packages (Kristensen et al., 2016) . The available inference methods include Kalman filters, in addition to a Laplace approximation-based smoothing method. For further details of these methods refer to the documentation of the 'CTSMR' package and Thygesen (2025) . Forecasting capabilities include moment predictions and stochastic path simulations, both implemented in 'C++' using 'Rcpp' (Eddelbuettel et al., 2018) for computational efficiency. Package: r-cran-ctypesio Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 362 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-jpeg Filename: pool/dists/resolute/main/r-cran-ctypesio_0.1.3-1.ca2604.1_arm64.deb Size: 170220 MD5sum: 54a71d40f9ca006399c3b2db3c4701fe SHA1: 465c0b001c7bdc08b23079693250e8f089ff1028 SHA256: 6e6847512c5b905072983f07f0897c01f774d2d506c953a37e4afc0cd1e97163 SHA512: f80aaa05a395ab58bd38a4e50958d819ac7e581d738c7d720f9c1a80af851e18f5e05549c1b3f953a2535c77468a057d48ac7912e4e81c08866545430dd4d98c Homepage: https://cran.r-project.org/package=ctypesio Description: CRAN Package 'ctypesio' (Read and Write Standard 'C' Types from Files, Connections andRaw Vectors) Interacting with binary files can be difficult because R's types are a subset of what is generally supported by 'C'. This package provides a suite of functions for reading and writing binary data (with files, connections, and raw vectors) using 'C' type descriptions. These functions convert data between 'C' types and R types while checking for values outside the type limits, 'NA' values, etc. Package: r-cran-cubature Architecture: arm64 Version: 2.1.4-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3322 Depends: libc6 (>= 2.33), 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-mvtnorm, r-cran-bench, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-cubature_2.1.4-1-1.ca2604.1_arm64.deb Size: 1726662 MD5sum: 7ed349ab535a745fabca4c0c7db4213f SHA1: 3c8a9e2753f6a64781db778ad1656056a4be8ba7 SHA256: a346936ce800f71d4a354ef9c54bf2056c1c51312b4ba7d926985835eb415e54 SHA512: df8c6f04fb44a36fc09eaef2b7effd982fd34222f470c5de3954b722f49381a22f67c81b6c1fd5001a8c89b37bff93b8d1cc4c975a40eb5b05c4a45c50be0beb Homepage: https://cran.r-project.org/package=cubature Description: CRAN Package 'cubature' (Adaptive Multivariate Integration over Hypercubes) R wrappers around the cubature C library of Steven G. Johnson for adaptive multivariate integration over hypercubes and the Cuba C library of Thomas Hahn for deterministic and Monte Carlo integration. Scalar and vector interfaces for cubature and Cuba routines are provided; the vector interfaces are highly recommended as demonstrated in the package vignette. Package: r-cran-cubfits Architecture: arm64 Version: 0.1-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2429 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-foreach Suggests: r-cran-seqinr, r-cran-vgam, r-cran-emcluster Filename: pool/dists/resolute/main/r-cran-cubfits_0.1-4-1.ca2604.1_arm64.deb Size: 1664786 MD5sum: b11f558cde232f4dcc92f809b74598ac SHA1: 5baf32df0d66ffd2d6f9e6be9cc420099171c3a7 SHA256: 727892f57683c6acf15f6e90e7cb422fd72a335da7cac884fdac696f8a03ce4d SHA512: 929e1a21e7a7203b2ee52dfd53d8e3e274761fd0dd69c136a99f16df7f3ac7c6563f4058312ba2209d991583b294968b67caf53afb71fab6afb485ea0b16e40d Homepage: https://cran.r-project.org/package=cubfits Description: CRAN Package 'cubfits' (Codon Usage Bias Fits) Estimating mutation and selection coefficients on synonymous codon bias usage based on models of ribosome overhead cost (ROC). Multinomial logistic regression and Markov Chain Monte Carlo are used to estimate and predict protein production rates with/without the presence of expressions and measurement errors. Work flows with examples for simulation, estimation and prediction processes are also provided with parallelization speedup. The whole framework is tested with yeast genome and gene expression data of Yassour, et al. (2009) . Package: r-cran-cubicbsplines Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-cubicbsplines_1.0.0-1.ca2604.1_arm64.deb Size: 25574 MD5sum: 905e795587149ed45714f45f811c2794 SHA1: 377b6ae63d073e19dcd7f524bbdcbaac8e4c5fc8 SHA256: 357a4b549c3693d71b05efa282fb13b2beb12601bf4af9d2438d33fe46f4aab2 SHA512: 667386291cde8a237b6c82627e7d895c0fd080f4aa16fd78fb6d7e9a17cfd44632e9fb79c094d81518af5bbfbcc35dc66639e8d462f5df4d0ebe45aaf0ebc896 Homepage: https://cran.r-project.org/package=cubicBsplines Description: CRAN Package 'cubicBsplines' (Computation of a Cubic B-Spline Basis and Its Derivatives) Computation of a cubic B-spline basis for arbitrary knots. It also provides the 1st and 2nd derivatives, as well as the integral of the basis elements. 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Package: r-cran-cubing Architecture: arm64 Version: 1.0-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3035 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_arm64.deb Size: 2933126 MD5sum: 834de3e795d9045ee3c6a3786d066642 SHA1: 616ac4df3b4c8faa1e202acd5ea45f2f6e26d7eb SHA256: 36069d184ddbd5ef99a9d795b4022c213d737ebdd14e61ea680233cc1f6dc5db SHA512: 9b33564e9d4b3c06d73af42acae792512a972527d0e63f8552e254ec0c119bbe571ecbbf03d012cf5b2365a1b7be6cce173e721615e8706a5bf29b0267fedc87 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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Details of the method are seen in Hawkins and Olwell (2012): Cumulative sum charts and charting for quality improvement, Springer Science & Business Media. Package: r-cran-cutpointr Architecture: arm64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1346 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 812830 MD5sum: 6b40c55c842e2181ca75e674338c8d57 SHA1: cad202a89701d69d9ece357b3e82dd3afa1c45dd SHA256: 8ec2ed339b92506c496aed006577c875b864b6453eab19555a8c2ca5e77e049d SHA512: 7c59c7ad41b2f7ec421330c831d0225dfa562fc45cf8ff628d411f4fdc3ebfb28b72fcc7103c570aa19b0bd4123b571cba10fba093cf5bf5182732ead191081e 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. Some methods for more robust cutpoint estimation are supported, e.g. a parametric method assuming normal distributions, bootstrapped cutpoints, and smoothing of the metric values per cutpoint using Generalized Additive Models. Various plotting functions are included. For an overview of the package see Thiele and Hirschfeld (2021) . Package: r-cran-cvasi Architecture: arm64 Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1571 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1072094 MD5sum: cd51fb383c4ed66306db35ade9bd7c38 SHA1: 51a489b5e521a7217f2e1471abe313026d8fec26 SHA256: 1fa3470e929b1cdd96c9374233d99960fd6d512a52f372331a0ef564abab8d63 SHA512: 9c8c92c4eeb83f6aece2bb15a6a609e38fb0424a8a56c6aa005ff94cafc5d34ddea102fbdeb7227a2c40569c10f7f98d03c73d9782867deb29726041e88cff18 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-cvlm Architecture: arm64 Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 484 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-boot, r-cran-rhpcblasctl, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-cvlm_2.0.0-1.ca2604.1_arm64.deb Size: 173864 MD5sum: 380aaf961e94fe2552d7a02a4955ead2 SHA1: 60458642f820750bd3f21453ca25498a52034ecb SHA256: 3c176d3f2e1e077aba406e77bac102fe0313dfb7f607b69a79e5684044fd8f0d SHA512: f0726db34f5d8bc19fce21bf63ed0e93ffd137221c14aef0143a849c6b93d04874fa3095875f5971121650362d9b12c3c2d869890cabeff1fb744f11a734a100 Homepage: https://cran.r-project.org/package=cvLM Description: CRAN Package 'cvLM' (Cross-Validation for Linear and Ridge Regression Models) Implements cross-validation methods for linear and ridge regression models. 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Package: r-cran-cwt Architecture: arm64 Version: 0.2.1-1.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), 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_arm64.deb Size: 52220 MD5sum: 3b580cf4926e2cc3133f2382ce0d7ab6 SHA1: 37cb34f0deb5c718ad91380da8969ec22de43e53 SHA256: 0d6823a6726ff873960d0a8e483675c6cafb70277380afd34cd140df13a26160 SHA512: 1a77783f1466aab2a30b929d02523c1b37597ed7907fbe94d6b1c07f7825370063c3d6573e7da7ece39c7316546f012f9159dd04c757f49ce13f57ebee571a29 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: arm64 Version: 3.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3394 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_arm64.deb Size: 933914 MD5sum: e3b593ae772b76209840d55abbee5404 SHA1: 50c08f50a7585b0fbb927013b8aefca71269f27a SHA256: 946aa71cd3841c905036ed15bcb00c13dde239922a06e74fda8e2c4969cea9bc SHA512: 4be69aeb37e955e9b348319ce5806c2deeedfaef4ed41e7e8ee94e7d6bf424f24bdf2b7ca1ed85fed26d0c5739a849d71dc09b346ac8fff311c2a6fca069f136 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. Implementations focus on computational optimization and fine-scale parallelization to yield efficient inference in massive datasets. Please see: Suchard, Simpson, Zorych, Ryan and Madigan (2013) . 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Velocity information can be added as an additional layer. See Liu J, Wang Y et al (2023) for more details. Package: r-cran-daisie Architecture: arm64 Version: 4.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3533 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgfortran5 (>= 10), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ddd, r-cran-desolve, r-cran-doparallel, r-cran-foreach, r-cran-magrittr, r-cran-matrix, r-cran-pracma, r-cran-rcpp, r-cran-subplex, r-cran-tensor, r-cran-testit, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-ape, r-cran-covr, r-cran-dplyr, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-bioc-ggtree, r-cran-gridextra, r-cran-knitr, r-cran-phytools, r-cran-purrr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble, r-cran-tidytree, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-daisie_4.6.0-1.ca2604.1_arm64.deb Size: 2186068 MD5sum: d901df468949ed660b4b40ba4259ebeb SHA1: 3da6cf82206b132c6ebecc986634fad85a6dd283 SHA256: d55da8c7b142cf35f9bfd249a9a0cde92f07b68b02d4a7bd137f887a2e8dd2e2 SHA512: 64b39b1d58dc33cd2fc4ea5dfc235a4d87a8f8c64b0429b7ee9ecb6f6862ef85c593b1668aaf4deeea0ced594aad92013e14b118b379317ffc8787b143b41da9 Homepage: https://cran.r-project.org/package=DAISIE Description: CRAN Package 'DAISIE' (Dynamical Assembly of Islands by Speciation, Immigration andExtinction) Simulates and computes the (maximum) likelihood of a dynamical model of island biota assembly through speciation, immigration and extinction. 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Package: r-cran-dang Architecture: arm64 Version: 0.0.17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tidycpp Suggests: r-cran-anytime, r-cran-data.table, r-cran-zoo, r-cran-xts, r-cran-ttr, r-cran-quantmod Filename: pool/dists/resolute/main/r-cran-dang_0.0.17-1.ca2604.1_arm64.deb Size: 104810 MD5sum: 156120afef4d01acfe2df7476e52b329 SHA1: 29ec010066ffd0c0c6fbdc7f2619e8efa3646b54 SHA256: eac150313c27c74e35d9103e80405c4b318f7317fbd6a3558c13964a11140c46 SHA512: ee0d5d61d3302436845eb63cbe7bf530eb893c4ab1b51d960d2879ffba2b849a48926175a4a111e49e81be84616fd06e64e714a789f232a9091352a7f04db305 Homepage: https://cran.r-project.org/package=dang Description: CRAN Package 'dang' ('Dang' Associated New Goodies) A collection of utility functions. Package: r-cran-dann Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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-tibble, r-cran-ggplot2, r-cran-stringr, r-cran-rlang, r-cran-fpc, r-cran-rcpp, r-cran-hardhat, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-mlbench, r-cran-dplyr, r-cran-magrittr, r-cran-recipes Filename: pool/dists/resolute/main/r-cran-dann_1.1.0-1.ca2604.1_arm64.deb Size: 235950 MD5sum: 096187bbe9a2cd5b1286322cac311da4 SHA1: 6219ace2da87117e7f53e7aa00e11eab22dc85a7 SHA256: 1a48f213a2a88d0bc2a32bfe9e231a7197c77f2ebd1e726b6e3fec4290b55253 SHA512: 288bb4818a3954fed2d436d0ce4dde88810b2bd55d134286a13327bd97b3037655c140db0b7d097b6e7be79fc3684a4a56d2bcea1985a2d5d2d3d3e5f8a19bbe Homepage: https://cran.r-project.org/package=dann Description: CRAN Package 'dann' (Discriminant Adaptive Nearest Neighbor Classification) Discriminant Adaptive Nearest Neighbor Classification is a variation of k nearest neighbors where the shape of the neighborhood is data driven. This package implements dann and sub_dann from Hastie (1996) . Package: r-cran-data.table Architecture: arm64 Version: 1.18.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5353 Depends: libc6 (>= 2.38), libgomp1 (>= 6), zlib1g (>= 1:1.2.2), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-bit64, r-cran-bit, r-cran-r.utils, r-cran-xts, r-cran-zoo, r-cran-yaml, r-cran-knitr, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-data.table_1.18.4-1.ca2604.1_arm64.deb Size: 2509522 MD5sum: ec975e7a5f284f9400a32fe242e3af64 SHA1: 362c40a0b1436634a6a101f95f32fdbda7ef0d1d SHA256: 795805355913dda764d1ad7872d6d2de561f6aa084158483cd74910a6183f9d5 SHA512: 125abbb67911850c4f15dc2d7da3ddba88bf6e776d45d19ad11e456939f755963987c109c5cf62fc68e131f3a918ffd026af7b956fa8bca509631647c6eda355 Homepage: https://cran.r-project.org/package=data.table Description: CRAN Package 'data.table' (Extension of `data.frame`) Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. Offers a natural and flexible syntax, for faster development. Package: r-cran-databionicswarm Architecture: arm64 Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3428 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_arm64.deb Size: 826818 MD5sum: 4a37012358468bc3f5f690ba36183b69 SHA1: 34bade8490fcb5a4bf78fc789de7d9bff7327448 SHA256: e017a1285412ced31f5cd189d644ffc9abb423354be76951a1226239f8be7b6d SHA512: 7982c65802964f06799f0fd0473813bf2ad9ca47f3fdb18f97ce5d33c4d6ef92336385f00463485ee526f90f4a316374e2e6f95135057594ff76dd75175f5d36 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: arm64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2343 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1678156 MD5sum: 83d38e97e686d2be4fdea9a000646fb3 SHA1: f16e0e6f0159848a5dfd4c675c1346d8b2781cb3 SHA256: 0a133e05714394bc579c8d2d053d5c4a8fd47ace274172b52957bd926d2e1387 SHA512: 48ee72758c19acaa22362a8ba0813c748f67cf70b9c99cfb75b9cf4266c02c24d26363c6e7ee910fdc1d7e569ab4bc39c7538942d5d57ad71428695f71c746b1 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: arm64 Version: 1.2.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1020 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_arm64.deb Size: 291366 MD5sum: 5f450797c81ec222b1003acd7d9bae90 SHA1: 4e5f20540e5205c161e5207a4303566482ff379b SHA256: b03becafe21b55a927a4ee27aa96c046be5d248149179ef7ac493949a7d454d0 SHA512: 1a18f21ebc5067681573450b71d051603219976a7697d9a6d2d4cbe953d16899ad71658e5b823bf01b5a6484ab80955d43d3bca08dc72b1ac1e751da2a080ad6 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: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 397 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_arm64.deb Size: 143254 MD5sum: a65eb0acb35e3fdd5664acd9faac4551 SHA1: 8a91b1e09c8cbe14d444069b25e00ef0b8a47aef SHA256: 1ca0b32dd7504c689c58ed5aa70b00a8abcd6522fa43b8e14a52e6dc75e410f0 SHA512: 43476b572212ed0659858ef68471653f99c506a2fbbb53f9c8e7191fbb9ae065b9d3ad59319060f5bcba035bcb63818310fbc6c6c52a66649bb1ac8dfaa32614 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. Bundles the 'blast' decompressor from 'zlib' contrib/blast to decode 'PKWare DCL' compressed 'DBC' files and parses 'DBF' records directly for efficient import into tibbles. See the 'DATASUS' file transfer site and Adler (2003) for details on the underlying data and compression format. Package: r-cran-datavisualizations Architecture: arm64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5351 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_arm64.deb Size: 3766140 MD5sum: 3b01bbbae1d906dc5709047674347a37 SHA1: 3f41e58ad6d245e65bb2992c7272577d9a6d2826 SHA256: 21de98831a88bd2b229d02252e0c72fcc39ff591e262284821f6b0dff8e3b256 SHA512: 06283d472817ef295c9975479365315faf7e1349d4e091c8320a7bdce70c049259bdfa3b556a4662d70fa77c7592626479a4eb1dafa0399974eff6049b2f040c 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) . Package: r-cran-dataviz Architecture: arm64 Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-dataviz_0.2.8-1.ca2604.1_arm64.deb Size: 125992 MD5sum: 59ba9fefb05bdf36ae05f07439ff8c6d SHA1: 30317a3ed8ee4d74d53f7991399d3aed10d23032 SHA256: 4fe550c787356936656b9068dfc82fa758ee1c750887ab24fa9f3696e385b55f SHA512: 5b8b89f6eb0e412fa59aee5862b84cc33041a6a1d8b64c9a800739654d560a546ce491eaa367f1a5aa30ede8b9ca465424c93ab59b16b057239777da290f9f0a Homepage: https://cran.r-project.org/package=DataViz Description: CRAN Package 'DataViz' (Data Visualisation Using an HTML Page and 'D3.js') Gives access to data visualisation methods that are relevant from the statistician's point of view. Using 'D3''s existing data visualisation tools to empower R language and environment. 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Package: r-cran-dblockmodeling Architecture: arm64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 53896 MD5sum: 07c1bafd76ca500bc8b94ee292150199 SHA1: 67ec611e927339a2f3d5364338e1f5dac911bbeb SHA256: 478f0572c0e4621cc7b839a23d69c71a9f5cd758b89cf60ba0717f2e0ea580cc SHA512: 66eb9ac96ecdf1f1d6fc59778258a7a7eeccf2a68cd574b90a00a27cf509d0216851457d00db4b460a222109398c6e6d19588f83201f3e136e06bbe44610b003 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: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2086 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1389472 MD5sum: df6be711e54de5909190bc88b0aa8c71 SHA1: 6681fff2ab8e6db89380d30bccdc4a07e9b3bd75 SHA256: 348823c5ee7988bf9474b2a4f1be32d364b2338205449918648a3a1dca16354d SHA512: 39f35a47e345ce6f96a7935391cb4c3c95ddff79a69fe516435b5be50a8b68256b0bf60cccc0cad81ff0c90f00e813c5678bbaf56f6cd7f84f62bfbd238f9f00 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: arm64 Version: 2.11-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 796 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_arm64.deb Size: 605274 MD5sum: aa35a153e56d30eb92dd6fd3a89ed612 SHA1: 26f9e22e098f448a542ee732e20f1e7efb3d043c SHA256: 0cb3736ad61d68a019cd33f85e665f1fe3c68e0f6606d20a1fb00acb39b0c6ce SHA512: aeaa9a8391ff24ec21da7fd4d5a2e61d1a51ba49c019a7345fd28711d10f246f8cae3352845ba1cf7be483860a54c903d1e8b84dbba497a064501c2340239fde Homepage: https://cran.r-project.org/package=dbmss Description: CRAN Package 'dbmss' (Distance-Based Measures of Spatial Structures) Simple computation of spatial statistic functions of distance to characterize the spatial structures of mapped objects, following Marcon, Traissac, Puech, and Lang (2015) . Includes classical functions (Ripley's K and others) and more recent ones used by spatial economists (Duranton and Overman's Kd, Marcon and Puech's M). Relies on 'spatstat' for some core calculation. Package: r-cran-dbnr Architecture: arm64 Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1118 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_arm64.deb Size: 849580 MD5sum: 5f1f12dde9d31c1e923a56142a6bbfc4 SHA1: 832cfbaa62c1d3b592432481b930ae11e4ffff38 SHA256: f6ef60fb2c7d989f885e6ae0800a1de7b607550e187677ae31d08760d0eb5f73 SHA512: ddad135688648b6c93b60103c3843767c4f612cab9af73d432d5c38ad6fb5655a7d17711f5e63cf89803be594d6191277fc3f42e76a577556752fcd39034cc5b Homepage: https://cran.r-project.org/package=dbnR Description: CRAN Package 'dbnR' (Dynamic Bayesian Network Learning and Inference) Learning and inference over dynamic Bayesian networks of arbitrary Markovian order. Extends some of the functionality offered by the 'bnlearn' package to learn the networks from data and perform exact inference. It offers three structure learning algorithms for dynamic Bayesian networks: Trabelsi G. (2013) , Santos F.P. and Maciel C.D. (2014) , Quesada D., Bielza C. and Larrañaga P. (2021) . It also offers the possibility to perform forecasts of arbitrary length. A tool for visualizing the structure of the net is also provided via the 'visNetwork' package. Further detailed information and examples can be found in our Journal of Statistical Software paper Quesada D., Larrañaga P. and Bielza C. (2025) . Package: r-cran-dbscan Architecture: arm64 Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4347 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_arm64.deb Size: 2850696 MD5sum: 12057bf52b68e4b0913565aba3a2eaba SHA1: 29517be696f9c1e023487deed445068da3fa67d6 SHA256: 473dbf99d39a318712fc3a46e94fa96b9966158439022cfa8441a37928f6226c SHA512: 18ee70933446a92abf28814b635417c97cfc9c5ca58a127f2902a270ec8537ad2a87deb48810b6cdbfd93a6bdb12a6cce7de4b9b703ddc7a00a658f68f34f30a Homepage: https://cran.r-project.org/package=dbscan Description: CRAN Package 'dbscan' (Density-Based Spatial Clustering of Applications with Noise(DBSCAN) and Related Algorithms) A fast reimplementation of several density-based algorithms of the DBSCAN family. Includes the clustering algorithms DBSCAN (density-based spatial clustering of applications with noise) and HDBSCAN (hierarchical DBSCAN), the ordering algorithm OPTICS (ordering points to identify the clustering structure), shared nearest neighbor clustering, and the outlier detection algorithms LOF (local outlier factor) and GLOSH (global-local outlier score from hierarchies). The implementations use the kd-tree data structure (from library ANN) for faster k-nearest neighbor search. An R interface to fast kNN and fixed-radius NN search is also provided. Hahsler, Piekenbrock and Doran (2019) . Package: r-cran-dcca Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 95742 MD5sum: 7cf92278cd4f22b4affa9514b1aba8d7 SHA1: f5c9315448d0fd3f4f2dcee3c017f8bfbc4db4d8 SHA256: 56433e5941477a5d21c18af95ae52d0b158088e978fad7763be3e8c9dbd220b5 SHA512: 864d6b6719781cc05e7d1f93c777fc47e40cdc580b3687760036b05213f3a81ee2c0af89edc0103ea80bc910f000a0d89f1c0759b9e1cb9bd7ac8b73719d0b1b 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: arm64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1144 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_arm64.deb Size: 757112 MD5sum: dc5edfa3e931b5b9a529961057a85201 SHA1: 381bf4fb68180be2dd92dcc4695813083cef22f9 SHA256: 05844f444d5d88f51d9aac06891075f87270a6d7da7c70f87f04c2a86db248fb SHA512: a81123b3eba1e600ecc34eddbf5f49a8ed2353427ecb11eaea58d6b3420780378be9b4afe6356b507d82a90aa4d3faf4fbf7dc73adea3e2ad39bd910f35075e2 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: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 636 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 500788 MD5sum: 1cf0242c82531f8ab5d246e4196cec57 SHA1: 4bc948be5a222abf24ad1926799101e4a40a400c SHA256: c7908033ea00ab8e5f38c8aba62a279ce39854411c042357a1789c757f0275f0 SHA512: 92af6b048c1bb07807f5f5636ac8b49d639d52fa2a66e71c2403fa1ff357113aa08dbf23b28d0bff6e18ee30c70e45277179b7f60349b9bb457c318ea36ce9d0 Homepage: https://cran.r-project.org/package=dccmidas Description: CRAN Package 'dccmidas' (DCC Models with GARCH and GARCH-MIDAS Specifications in theUnivariate Step, RiskMetrics, Moving Covariance and Scalar andDiagonal BEKK Models) Estimates a variety of Dynamic Conditional Correlation (DCC) models. More in detail, the 'dccmidas' package allows the estimation of the corrected DCC (cDCC) of Aielli (2013) , the DCC-MIDAS of Colacito et al. (2011) , the Asymmetric DCC of Cappiello et al. , and the Dynamic Equicorrelation (DECO) of Engle and Kelly (2012) . 'dccmidas' offers the possibility of including standard GARCH , GARCH-MIDAS and Double Asymmetric GARCH-MIDAS models in the univariate estimation. Moreover, also the scalar and diagonal BEKK models can be estimated. Finally, the package calculates also the var-cov matrix under two non-parametric models: the Moving Covariance and the RiskMetrics specifications. Package: r-cran-dccpp Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 56638 MD5sum: 8612d7bf81d63d54aa93c62f6d57e545 SHA1: 4abe5ed6e5a30af3e639b4216eef2344f173d7f2 SHA256: 8b9cdce4c89b995533515863ba375eb80461fc12eabcd8ebf187408b7fd75eb8 SHA512: b25bf5b478a9bae30d29d89664bf7b107a38d82a900f96157f79429314fc042f0e7d3d05731e5de2c97ca1f13853c6d9ee69438e01576eb0483d1a26605e9a10 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: arm64 Version: 2.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 171858 MD5sum: f28af2ada8657cb9b8a485b0dd14a852 SHA1: 4a82929b4fe96d9511dd0c441a43bc0b1a9a019d SHA256: 14e893f36ecd1ffb3e7a2b21ebd3f3c021a4191d8e07a85f5095d99eac8208ca SHA512: b1370e34765ef6b09fc6de4763eb6d8446e23324140cef27b9dc13d11bda7a7fd03b49981a2696c4aff0b2bb5d5713f24355f68112db6f0f8d3495562ced59c1 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: arm64 Version: 1.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1085 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 588882 MD5sum: f558ff9afa516d4cf331f5362c3adfc1 SHA1: ca2a6b3fc05d106cdc62b6bef97215fd03da93f5 SHA256: a0d440911b898ac5fc85e1d16d665b5bf1d3fae65f7db053ddf5b4a8fbe6ca98 SHA512: 0d0bc84495668a2b17e1949fdcb2aed72abacab611e4a476b5334abd27d785dbb87e19ca0c076709a87e9d68c37f4258f399a7df79a29d69232be9a51eb86f84 Homepage: https://cran.r-project.org/package=dcifer Description: CRAN Package 'dcifer' (Genetic Relatedness Between Polyclonal Infections) An implementation of Dcifer (Distance for complex infections: fast estimation of relatedness), an identity by descent (IBD) based method to calculate genetic relatedness between polyclonal infections from biallelic and multiallelic data. The package includes functions that format and preprocess the data, implement the method, and visualize the results. Gerlovina et al. (2022) . Package: r-cran-dclear Architecture: arm64 Version: 1.0.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1079 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tensorflow, r-bioc-biocparallel, r-cran-dplyr, r-cran-matrix, r-cran-matrixstats, r-cran-ape, r-cran-phangorn, r-cran-rcpp, r-cran-igraph, r-cran-purrr, r-cran-stringr, r-cran-tidyr, r-cran-rbayesianoptimization, r-cran-rlang, r-bioc-biocgenerics, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-dclear_1.0.13-1.ca2604.1_arm64.deb Size: 865766 MD5sum: c56aeea5fbf9d9c52af19733c16145a9 SHA1: 85abf9ff1e7df646238cde77e664db8e8e39d340 SHA256: 94b8f0c116dbe86b1d2f72d1abfbc1a382fc57088625b702ef5b930a9530b890 SHA512: 40fdf04523591d6d3b4131bf014968aee9eee15ab3b393b8085ffb51ab6aea6388fdabccf32fccc40ebe6d03556ad411cec15b3dda38f595bd78727f18f1515b Homepage: https://cran.r-project.org/package=DCLEAR Description: CRAN Package 'DCLEAR' (Distance Based Cell Lineage Reconstruction) R codes for distance based cell lineage reconstruction. Our methods won both sub-challenges 2 and 3 of the Allen Institute Cell Lineage Reconstruction DREAM Challenge in 2020. References: Gong et al. (2021) , Gong et al. (2022) . 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More precisely, the use of a mutual reachability distance pulls peripheral points farther away from each other. Tree edges with weights beyond the detected elbow point are removed. All the resulting connected components whose sizes are smaller than a given threshold are deemed anomalous. The 'Python' version of 'deadwood' is available via 'PyPI'. 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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: arm64 Version: 3.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 120198 MD5sum: 6df97d82d3e0b0d6a87eb33a0d1cf798 SHA1: 4c5f4a316736b68fa760140eed40cc2bfc4bdc9d SHA256: e746a4ab373bc635e421868e86288665412c6b323606c7b9e0e53a57bfc1ef37 SHA512: 9183a59c1165e7a4ed588f42b77cda80f15850a78216e6ce020b9f07467242c3a4d25c1b8568f30bb1d451d27db32dbe1960dce7b6485e11271301948d7f3737 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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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. Package: r-cran-deductive Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 170 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lintools, r-cran-validate, r-cran-stringdist Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-deductive_1.0.1-1.ca2604.1_arm64.deb Size: 52328 MD5sum: e50625ab076d1dddaa9e22edf1f127fe SHA1: 7a892ac084b93cb1fc87b3f7c3879a60237e3a3d SHA256: b8f4f15783cb7ec61626110caca7620cec05e3bba16da2ca51f6f1994ff64024 SHA512: f9bf6e056868ce98f07c5a450232018fd36b151e1211e7205ecb1ee8829df6bb6ee802b42191a69b1f79d98429d1c20a565347b85cf33ca5a1dfa29d6f986353 Homepage: https://cran.r-project.org/package=deductive Description: CRAN Package 'deductive' (Data Correction and Imputation Using Deductive Methods) Attempt to repair inconsistencies and missing values in data records by using information from valid values and validation rules restricting the data. 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See Sauer (2023, ) for comprehensive methodological details and for a variety of coding examples. Models are trained through MCMC including elliptical slice sampling of latent Gaussian layers and Metropolis-Hastings sampling of kernel hyperparameters. Gradient-enhancement and gradient predictions are offered following Booth (2025, ). Vecchia approximation for faster computation is implemented following Sauer, Cooper, and Gramacy (2023, ). Optional monotonic warpings are implemented following Barnett et al. (2025, ). Downstream tasks include sequential design through active learning Cohn/integrated mean squared error (ALC/IMSE; Sauer, Gramacy, and Higdon, 2023), optimization through expected improvement (EI; Gramacy, Sauer, and Wycoff, 2022, ), and contour location through entropy (Booth, Renganathan, and Gramacy, 2025, ). Models extend up to three layers deep; a one layer model is equivalent to typical Gaussian process regression. Incorporates OpenMP and SNOW parallelization and utilizes C/C++ under the hood. 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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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This package provides functions to estimate and simulate these models using the Discrete Exponential-Family Models [DEFM] framework. In it, we implement the models described in Vega Yon, Valente, and Pugh (2023) . 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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 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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Package: r-cran-depcoeff Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 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-copula Suggests: r-cran-mass, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-depcoeff_0.1.1-1.ca2604.1_arm64.deb Size: 90786 MD5sum: a88f539ac6ad8017c20949ad6986b5fd SHA1: e1046d43bb1dea6feed3f9925cba0fc3d7a609f6 SHA256: 90797fe5831cb9da1077c7292b54477bcb02b874097decc927650a91757c54d2 SHA512: abba096dd8747b40c56bd1a378897022d0aa2aa1d44bee13f08936f2ac98a3f12caacc94a6c3d3d5767ef0836ea5109ea3102c069cbada6b0cae6f4547bea80f Homepage: https://cran.r-project.org/package=depcoeff Description: CRAN Package 'depcoeff' (Dependency Coefficients) Functions to compute coefficients measuring the dependence of two or more than two variables. The functions can be deployed to gain information about functional dependencies of the variables with emphasis on monotone functions. The statistics describe how well one response variable can be approximated by a monotone function of other variables. In regression analysis the variable selection is an important issue. In this framework the functions could be useful tools in modeling the regression function. Detailed explanations on the subject can be found in papers Liebscher (2014) ; Liebscher (2017) ; Liebscher (2021): ; Liebscher (2021): Kendall regression coefficient. Computational Statistics and Data Analysis 157. 107140. 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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: arm64 Version: 1.5-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1031 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_arm64.deb Size: 710062 MD5sum: d93b202e786cf12deab498d082f1aa74 SHA1: 6e0faeef5b873c01769a94c198b5867be7043e73 SHA256: ed7ec97c3ebdef221195a30ce4d3f644f396d77e7754067cd2bd2e7bf72cced7 SHA512: 6b1eb0a1c4f9810ee5c78e3e7a33c31c45ee92871520fdfd49a411c5a21ccb66c045386082f502265c59f7fa793994ab996e07c8ff4ce34f5b9db2d3240db34e 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: arm64 Version: 1.5-1.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-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_arm64.deb Size: 105432 MD5sum: 7a54d34d19e1eabe29e8a64aebc28f2f SHA1: 40023398d597dff1cfc5622458212651c8232c45 SHA256: 7392f851918d98884cef636df05d71f7f3562d35d399e4bcac66db5a407d9736 SHA512: 86c978a2ee6d2d7f35c0127e359d7e523f0bdd1cbaa60857344dba77aca656649ff1e9fc440c329d25df5780a8d052afdfc747c50d51da4da880d68142308eb8 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. 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The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). 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Package: r-cran-desla Architecture: arm64 Version: 0.3.1-1.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 (>= 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_arm64.deb Size: 635088 MD5sum: 9ee371668af6838e7334c70416a68580 SHA1: 08ca121d2a32d47859117707224ee5fd52710927 SHA256: 5195a74169c800feb3d02ccce8dce587edaf99110454a279b10dc281b2b4f5e0 SHA512: d2ceb2dee4531a1587dd97dfc3a6cfc4dbf860f217309054f6de7fc86e365876297bde46fabe7adae2ec2071bbc9af3e00d5a700f7f03dec5084819226b0a1aa Homepage: https://cran.r-project.org/package=desla Description: CRAN Package 'desla' (Desparsified Lasso Inference for Time Series) Calculates the desparsified lasso as originally introduced in van de Geer et al. 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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: arm64 Version: 4.5-1-1.ca2604.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 201660 MD5sum: 9606e98b01d2b58e53b96813c5698a81 SHA1: 607e8a7e34486074148a0a10b6c4598e1364ba61 SHA256: 55fe39f4c4f3063a9a485afc938cd4e11a04faea675456c10d0d65d8a4fe9261 SHA512: c99d97b12e7848b4dc3ec103c8d171b764847ae049bf323874427338aac09b77d35c8028476000cf099c8c747ee151c30260b332ab5fb408f82f2c0be95eee7c 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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The procedure is based on the maximum of the absolute value of the studentized residuals, which is a parameter-free statistic. This approach generalizes several procedures used to detect abnormal values during longitudinal monitoring of biological markers. For methodological details, see: Berthelot G., Saulière G., Dedecker J. (2025). "DEViaN-LM An R Package for Detecting Abnormal Values in the Gaussian Linear Model". HAL Id: hal-05230549. . 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Package: r-cran-dfcomb Architecture: arm64 Version: 3.1-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 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_arm64.deb Size: 95980 MD5sum: 28329791001441b9dc4a9603081a3b17 SHA1: f5da94fb58203c439dc92877e02b31991b2eec35 SHA256: ef46bfe760a62a68b04271bb9b96bea5f2d361f79d9d634fe810993cd4d04e6c SHA512: 4b60589c58b2d3d14a34a2ea5caa5f3e51d2c4540b3d5c99550795757211275c2713b4d7b396a621eeda839ab3610a4b21bc22768747116dbc7838490ba603c8 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: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2974 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_arm64.deb Size: 2182770 MD5sum: 2427f2b59d58cc4fff26c986d5ee935e SHA1: 66961e8bc10bf314543b8c2133ff5a7d4eb15b5f SHA256: 2810073df48663fa22ee7127292fc73e3f2bdf7bfe1100bfdcc3440a3b987c78 SHA512: dc8f19b919ab2a13c9fc05cbd499062a28443688f562ec5665e76d45542e03e48aa11ca50ea6b63eee17e84407eab4f9794ce23d32a297d0564f57a3a95bb5b2 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: arm64 Version: 1.7-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpparmadillo, r-cran-bh, r-cran-rcppprogress, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-dfmta_1.7-8-1.ca2604.1_arm64.deb Size: 121532 MD5sum: 1af3c59ed2949d811ecb2096a27a2808 SHA1: b47808b1820d9728e0bc4ffbb84fb05ee9a1e9bf SHA256: cbc9685a3e206e4b042299195099bb056a269ecef63c9d63636afd9bf726f2c8 SHA512: 4a5383102b99ef92e50dd7d4d610b7f69ced834882948c3640632dc1930608657dccebdc274ddb9e09da3871187bfa642ffa2d116bb4736c1c7fbeb42d8b21df 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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This paper illustrates the method in detail: J Cai, RJB Goudie, C Starr, BDM Tom (2023) . Package: r-cran-dhmeasures Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2286 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-tidytext Filename: pool/dists/resolute/main/r-cran-dhmeasures_1.0-1.ca2604.1_arm64.deb Size: 2144276 MD5sum: fe199c4befb14eb22554a654ff6fbed9 SHA1: 20c1c1fd8b4c39e9d4c6fad8ded4b1e5345cde08 SHA256: 44b66d301cc8bd3eb672900ae876c081e4ae2f0e05ef6f811bfc2e27a7a2bb56 SHA512: dd8abf14837418729fc5d95fd57d81d47cc89b42467825117a0ccd5f8987a314df911db648eb5d090930fce1555d6f04c16bd49991ce91b1f8af728eee5d1ab5 Homepage: https://cran.r-project.org/package=dhmeasures Description: CRAN Package 'dhmeasures' (Digital History Measures) Provides statistical functions to aid in the analysis of contemporary and historical corpora. 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Package: r-cran-dipsaus Architecture: arm64 Version: 0.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2347 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-cran-r6, r-cran-shiny, r-cran-cli, r-cran-stringr, r-cran-jsonlite, r-cran-future, r-cran-future.apply, r-cran-parallelly, r-cran-progressr, r-cran-fastmap, r-cran-base64enc, r-cran-digest, r-cran-rlang, r-cran-rstudioapi Suggests: r-cran-knitr, r-cran-promises, r-cran-later, r-cran-rmarkdown, r-cran-testthat, r-cran-microbenchmark, r-cran-yaml, r-cran-future.callr Filename: pool/dists/resolute/main/r-cran-dipsaus_0.3.5-1.ca2604.1_arm64.deb Size: 1082984 MD5sum: 57a848bbc26d03c6d11b7bf8108932dd SHA1: 0f8f0d41fed3fd6cd0ed95ed6c3453b9da1bbb42 SHA256: 8a933a58694c9d12ecddfd54441d2bacd0435071f3217490c7d15bb193e11f9b SHA512: 41bbadf4e7df0fd9f57b0ebdda22266ae010e8da76e1a27efd8948cb0d3b8cb9befe43b5808a498e29a40c85ec100f15b5d4369f4ddde010bbeb7bb3148317c0 Homepage: https://cran.r-project.org/package=dipsaus Description: CRAN Package 'dipsaus' (A Dipping Sauce for Data Analysis and Visualizations) Works as an "add-on" to packages like 'shiny', 'future', as well as 'rlang', and provides utility functions. 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Package: r-cran-dirichletreg Architecture: arm64 Version: 0.7-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-maxlik Suggests: r-cran-rgl, r-cran-knitr, r-cran-rmarkdown, r-cran-formatr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dirichletreg_0.7-2-1.ca2604.1_arm64.deb Size: 349114 MD5sum: b23ee267d0e6b516e97ae49dfe684e5e SHA1: 335f6eca4973329e5ee1badde74d79eef16716ce SHA256: 67112ba2eeef7fb4b26454c3cedfd41726923207e92bd40157406e76ef645fc0 SHA512: 9cf399db197d3a30e09eb48f60234c2f7d4db99c15fdf38eae81fe4c5ba367e5f7e3570b6d7067192c05d03c2fd5d11cd1979520106f6b4c5cac21ad762daa98 Homepage: https://cran.r-project.org/package=DirichletReg Description: CRAN Package 'DirichletReg' (Dirichlet Regression) Implements Dirichlet regression models. Package: r-cran-dirichletrf Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 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-dirichletrf_0.1.0-1.ca2604.1_arm64.deb Size: 91456 MD5sum: 3449b67ca514cbc0eb82864a1e531441 SHA1: 66596e4c598f9ab42fb9c201a4a36dba7ae07840 SHA256: 4f4fea06b830c7b2a07ce5562d525274b9363286f7e127c51448b65695b7eb94 SHA512: 647fc7cc7de8343773e55071639db0a2aa75dbe6f4f3505980f49a508fa25caacee471accacce8f67d69c7ff9540bb294350b00511e1b8f0b9e2ff7f1456e52d Homepage: https://cran.r-project.org/package=DirichletRF Description: CRAN Package 'DirichletRF' ("Dirichlet Random Forest") Implementation of the Dirichlet Random Forest algorithm for compositional response data. 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When the response data are aggregated to polygon level but the predictor variables are at a higher resolution, these models can be useful. Regression models with spatial random fields. The package is described in detail in Nandi et al. (2023) . 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Given data on age-specific mortality and either incidence or prevalence, Bayesian inference is used to estimate the posterior distributions of incidence, case fatality, and functions of these such as prevalence. The methods are described in Jackson et al. (2023) . 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This can e.g. be used for modelling the distribution of Y chromosomal haplotypes as described in [1, 2] (refer to the URL section). Package: r-cran-discretedists Architecture: arm64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 515 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_arm64.deb Size: 373496 MD5sum: 2f751cae289976485bdf80a83b1e3e37 SHA1: dd78d9fbf81384a2f71525d7a49378c338678b24 SHA256: e37ceb629e42a160dff44876a91e19341c7497f86baba9ce898f550b15653f51 SHA512: 8b7aa426bd1c6a0c63cf32276f8f3a6ebe02d0ab2e0d03732d226617845690c7fc1bb3f7c89482d187655ac2fc4659cb9fbed08339ed79c8bd46274d94f05d3e 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: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 409 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_arm64.deb Size: 278240 MD5sum: 28f2b5b2def775e26ddbbefca0c52e3f SHA1: e26333c93c09f4fd1a9a848dcdaa3fd00b7c7a57 SHA256: ef58e3b584a55fc0f88f46124e9f3848588193aea5c17f0f92a9d7f48192732f SHA512: 094b22f756d12bfc4742d919266d2ce6038cfb7f54228daf73654f38120dc1017a9e6fef2572b405d4da91b37fb7de01ce1f2994bc55225c09563265926a9404 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: arm64 Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2115 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1119838 MD5sum: 4aadfb5655ffa50a80b57c80ca0f4041 SHA1: 50d370179ab7b8502e44fa3dd3ff4f6fee2ac6df SHA256: 7fc2a6c25c4f694d5c082d549c6d6051201242ca31bac2de56039f991d87cd62 SHA512: 7eb085b2bebeadc398826f9cc366a5f8ba2afdb7a6e775dc376fc770da66a47af6963d50e8873a5f9490af51e8d54109ca32279931fed814facdb4b74e60207a Homepage: https://cran.r-project.org/package=DiscreteFDR Description: CRAN Package 'DiscreteFDR' (FDR Based Multiple Testing Procedures with Adaptation forDiscrete Tests) Implementations of the multiple testing procedures for discrete tests described in the paper Döhler, Durand and Roquain (2018) "New FDR bounds for discrete and heterogeneous tests" . The main procedures of the paper (HSU and HSD), their adaptive counterparts (AHSU and AHSD), and the HBR variant are available and are coded to take as input the results of a test procedure from package 'DiscreteTests', or a set of observed p-values and their discrete support under their nulls. A shortcut function to obtain such p-values and supports is also provided, along with a wrapper allowing to apply discrete procedures directly to data. Package: r-cran-discretefit Architecture: arm64 Version: 0.1.3-1.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-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_arm64.deb Size: 87010 MD5sum: c07c8277b1c50e5cca591af42a781c4a SHA1: 72e553c475c7d042f9c30fa102ec0d31f4ed6a0f SHA256: ea8254d253f5846e45b16d85fda7b1a13c3dc346ae2e858b25af4063e9074e5a SHA512: 67d5494bf21372d0b45067a3feee1708c67bc36c993fd2f90e229c67304f56ae1be64c82bce957a4f96ce6aa713665da718d7cc0992e160d24b2923ba66317e1 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: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 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_arm64.deb Size: 171136 MD5sum: dff933d464a38ddf34ff37470ee9d76b SHA1: 55b3f5ec926e9f6708e12869296389589c2db61f SHA256: b9679d4f3f6753c5f166feccc9db838d7c31e6752cba19f19abc0e08a7e3b00c SHA512: 37b5fa745d1e88761a0832cf4cbecdd955ce491c2b676da40b29116f570d90f5d80a0bcfce151324059597023ace91a64adaf8c031edbbf75d72a6ce138cb007 Homepage: https://cran.r-project.org/package=DiscreteFWER Description: CRAN Package 'DiscreteFWER' (FWER-Based Multiple Testing Procedures with Adaptation forDiscrete Tests) Implementations of several multiple testing procedures that control the family-wise error rate (FWER) designed specifically for discrete tests. Included are discrete adaptations of the Bonferroni, Holm, Hochberg and Šidák procedures as described in the papers Döhler (2010) "Validation of credit default probabilities using multiple-testing procedures" and Zhu & Guo (2019) "Family-Wise Error Rate Controlling Procedures for Discrete Data" . The main procedures of this package take as input the results of a test procedure from package 'DiscreteTests' or a set of observed p-values and their discrete support under their nulls. A shortcut function to apply discrete procedures directly to data is also provided. Package: r-cran-discretetests Architecture: arm64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 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_arm64.deb Size: 338626 MD5sum: ae81b7bc8dd3e050bae819caf97572f9 SHA1: 25e26d67a28ff42023c02e33339e6a4192462034 SHA256: 50ca3d6b4a223969243b8f4bbfc92b0ef336f3fde21313f7fa0dc5bb655b28d4 SHA512: 2fcd80d522a15a6d63f8a5f96280fc1d479e39c2d060823ed30457b29d108e11bb84355548feb80228a077431a9bf8591273cc7ebdfa137ffc7af70fce40dc82 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: arm64 Version: 1.3-16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2965 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1898260 MD5sum: f275bd29a34dcb59b86252dbee8db548 SHA1: 63f1d643087e163414d8e9deeae477b5517e2f79 SHA256: e3c73403fac3af650798ccd602fd37792e5ce926090b02be44799e450ef25d40 SHA512: e945412974c995aa730915dae4c50e974ef6ad6206724343d866b75051f664282e8625ad9f0e2ae66064ee54db00b9f6822eac1735b9a9a349d204e821113c97 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: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 291918 MD5sum: d0d3ad26b29bcea5dfd41492efdffb84 SHA1: bb38c4a98e59ad0a3314736e8b5521c52e7c123f SHA256: d9c98fdd838914ebc1e8550fbfa8460b7a6745d85b84dc6f94a5b46b9464360c SHA512: 85f81897d3f5b2704a0379a9961a86abbc4e10708c9291a430782ffc632f88a8f4a4f59753ae124d94b93155bab7bd898c957eb5f35d85ad2a0a77ad79296594 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: arm64 Version: 1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2972 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2779122 MD5sum: a4b1ce2915872384aa5c200f526d6b0d SHA1: a68cbd275004a054c033a3039c52927bc399275e SHA256: f2e829a94dada8becc7fa34013bc06631fa03762c23180b2deac16c0f6310363 SHA512: 688363704824f5a6e6df3b09c730c0a26a6a2917276d644a4e6fa4706409c9172398263c60606b899ba1e46b7fcd7eb7b9448f04308455fc8bee6d9e823c498e 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: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 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_arm64.deb Size: 85244 MD5sum: 9daac15a93dd49260910d032850db987 SHA1: 8668a8095826056438df4e6a20a4f6042ec49900 SHA256: e5285466ccbb1b87d37f558d45cf2c5526ad525ed1e7adae44b4b9596935b3bf SHA512: b4af36c25ad1c1dc0a60c0ea294ced4eaef7ba31d80ee534dc6db375477873c06c026e38f951b80a61b0462ea73681063069140200bc1cea5bc6e0c52eadf511 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: arm64 Version: 0.1.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-distances_0.1.13-1.ca2604.1_arm64.deb Size: 70432 MD5sum: 9fa6c416bde91d88dc5eb52af733b362 SHA1: 190c5c8980d0cc60dae79670b12ae110f51ab995 SHA256: 4a2e753587e47e3d05a3c584e4454eb3491aabcba8eb8f47dac62365a27ee499 SHA512: 3599ffc1393f5c6e38a61e80e712352b56c4488506f7edbc47248a3a4a468473bf55e9f1d76e7f7c39ac960b0856bd91a01c04df02bcb0797412500cfd2978e4 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: arm64 Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2075 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1691206 MD5sum: 4e796b54f251e14b2971e321f5663d00 SHA1: 9b6590e40acbf72fb165493559edd31f1b8d162f SHA256: e903952d5af4e16ecdf6c0e3a6f67a874b1b5b9bba1e7bad920e83a2be35b638 SHA512: b5cd1cf6a96abb5451d00aa475ea118ee020b4d38fbb4be5492073332167e90a26b71bd649e0f1fddf00fa29ebaf0c629a90ac5376456f83674d8865fabb8b08 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: arm64 Version: 1.3-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3396 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-shiny, r-cran-httr, r-cran-digest, r-cran-jsonlite, r-cran-stringr, r-cran-r6, r-cran-dplyr, r-cran-rlang, r-cran-magrittr, r-cran-homomorpher, r-cran-gmp Suggests: r-cran-opencpu, r-cran-knitr, r-cran-covr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-distcomp_1.3-4-1.ca2604.1_arm64.deb Size: 1156702 MD5sum: 3ddc4d855494945c2c4974ee8e09c968 SHA1: c1bc746a5819a3aecb02bd7824a89cf48d3e0022 SHA256: f31698b268fa97247cce56dd574e7e5353c0fe20b40397419ded86a03bae387e SHA512: 5854b409b7ef10bec5dd892c5a707ba4a96f66f812f329b44945836dfc263e449e8ea89e0c93df8640059a7cecbb14dee9dff117b1d34097ee68b3bfe1d989b2 Homepage: https://cran.r-project.org/package=distcomp Description: CRAN Package 'distcomp' (Computations over Distributed Data without Aggregation) Implementing algorithms and fitting models when sites (possibly remote) share computation summaries rather than actual data over HTTP with a master R process (using 'opencpu', for example). A stratified Cox model and a singular value decomposition are provided. The former makes direct use of code from the R 'survival' package. (That is, the underlying Cox model code is derived from that in the R 'survival' package.) Sites may provide data via several means: CSV files, Redcap API, etc. An extensible design allows for new methods to be added in the future and includes facilities for local prototyping and testing. Web applications are provided (via 'shiny') for the implemented methods to help in designing and deploying the computations. Package: r-cran-distops Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 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_arm64.deb Size: 86322 MD5sum: 30a1a142d7654927995cda22082457ff SHA1: 75c24334b48c825eeca4c55c12df8b00e5e4ea3d SHA256: dc985b52ae6b2785e2e245a5310bf17508164bc5d6ec1709deb9d47f21cd1a85 SHA512: 6407e2501bc788b4e178b102b3070a7d4ae7373b4bdcf4c31566db90a6278b3ed5295935490356ba9c0ef8db23391b2c64c3267211d9d9e72e7eaf9134de1b23 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-distributionutils Architecture: arm64 Version: 0.6-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-generalizedhyperbolic, r-cran-variancegamma, r-cran-skewhyperbolic, r-cran-runit Filename: pool/dists/resolute/main/r-cran-distributionutils_0.6-2-1.ca2604.1_arm64.deb Size: 173886 MD5sum: a345a90517e920365ff969bed00d9302 SHA1: 4d9e1066e3ae104e2e6ab572a819fbfb51388328 SHA256: 505c77374649dbc06b0328e5adb9dfb7056cbd05937020343d24f5838c7b6fcb SHA512: bbd08efe378f720eab74b7bf999a3eb34cfaa3d525b070379b934ba7195b76e6e5af6e7003948eb05a823abf4aaa7384f16072616620828cef31ae826c0241aa Homepage: https://cran.r-project.org/package=DistributionUtils Description: CRAN Package 'DistributionUtils' (Distribution Utilities) Utilities are provided which are of use in the packages I have developed for dealing with distributions. 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Package: r-cran-divdyn Architecture: arm64 Version: 0.8.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2071 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1780074 MD5sum: 514142ea3193eb39eea528aa105278b9 SHA1: f7266984798375699a5aaed154e9e0379f81ccb1 SHA256: 65745d2d30a582eda2a927fcb7684c562aa5b5c9ad062b60ac031a20eebab698 SHA512: 6e1e765d778fa0cf3f6f632beced5281bfeb5353f15e6da0783331cd009548613b9e38b20911119d9a27fe6659a656ea55a5afe868e4fa5eed6f15aa1da5d9c7 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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Package: r-cran-dmbc Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 799 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_arm64.deb Size: 515370 MD5sum: 7536d0bdecd872ae1734c4bc400c4a0a SHA1: 5e338873788d7a63a9fc4e40c435add287c10b4f SHA256: 68b43b9ae871378aca4bc8181472353a53f97ae9f874b98f8372d3b878c77950 SHA512: 28cc18dc96cf0d2a5d3818170b044aa0550307c7a5f710c66e489b00d8912f0bb1140c1328f5a4f4ec9cb3c77a24f38c12368ea5e333f586139ca98bf010da8a 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. 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Package: r-cran-dormancy Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2404 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-ggplot2, r-cran-covr Filename: pool/dists/resolute/main/r-cran-dormancy_0.1.0-1.ca2604.1_arm64.deb Size: 1927182 MD5sum: b3b29876f4a0c1edbff02953e2017eff SHA1: f19bcf766f457bea76f45f89f1703b30ab8a04e6 SHA256: 2e8f2e8679d21a7769ade263eb902c5a372e486489f56f7e2c546d6eae06e5d7 SHA512: 4dde4c018d7e461883f1ad6949d51a927a4dce09426eb0622b885c06589b6826a4fb38997b7a708c52ebac926e361d46fa1d4bad63cbe09b0d9ef721e4bdb99e Homepage: https://cran.r-project.org/package=dormancy Description: CRAN Package 'dormancy' (Detection and Analysis of Dormant Patterns in Data) A novel framework for detecting, quantifying, and analyzing dormant patterns in multivariate data. Dormant patterns are statistical relationships that exist in data but remain inactive until specific trigger conditions emerge. This concept, inspired by biological dormancy (seeds, pathogens) and geological phenomena (dormant faults), provides tools to identify latent risks, hidden correlations, and potential phase transitions in complex systems. The package introduces methods for quantifying dormancy depth, trigger sensitivity, and awakening risk - enabling analysts to discover patterns that conventional methods miss because they focus only on currently active relationships. Package: r-cran-dosearch Architecture: arm64 Version: 1.0.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1116 Depends: libc6 (>= 2.17), 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-dagitty, r-cran-diagrammer, r-cran-dot, r-cran-igraph, r-cran-knitr, r-cran-mockr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dosearch_1.0.12-1.ca2604.1_arm64.deb Size: 441022 MD5sum: 07d49a439e5b4e86e31a0f98434dfe5a SHA1: d4c28f6a471132eea717053e1148b47df3c57de0 SHA256: c45c356e1a244bdcccefb00356e10b9436a18c826cb29fde600c6c4d4c41df69 SHA512: 2d4267c4f50a756c4a515fc14d998a6497eb5b11f100d0ec4e3ff436eb0c2827ac370af10c46d519b8731c656b7c2d494899081e9e2384aa1064fe0ccfc628ec Homepage: https://cran.r-project.org/package=dosearch Description: CRAN Package 'dosearch' (Causal Effect Identification from Multiple Incomplete DataSources) Identification of causal effects from arbitrary observational and experimental probability distributions via do-calculus and standard probability manipulations using a search-based algorithm by Tikka, Hyttinen and Karvanen (2021) . Allows for the presence of mechanisms related to selection bias (Bareinboim and Tian, 2015) , transportability (Bareinboim and Pearl, 2014) , missing data (Mohan, Pearl, and Tian, 2013) ) and arbitrary combinations of these. Also supports identification in the presence of context-specific independence (CSI) relations through labeled directed acyclic graphs (LDAG). For details on CSIs see (Corander et al., 2019) . 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Package: r-cran-dotcall64 Architecture: arm64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 126 Depends: libc6 (>= 2.17), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-microbenchmark, r-cran-rhpcblasctl, r-cran-rcolorbrewer, r-cran-roxygen2, r-cran-spam, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dotcall64_1.2-1.ca2604.1_arm64.deb Size: 30842 MD5sum: 9e138c42b3fadaea2b43c621fc87718f SHA1: bd24f4748e98177a5707b3e717eed3ce898d59da SHA256: da32660d5d3ba00c59a4290d62932a4afbfba3e8bf610d442bfa9d776e5abd41 SHA512: 9ee042788b59c02f691a4b1a1c1c0fdf60b1d200e369bb749978d2c1892ab1919863e8cb2aca4afe270943138b58654521df0ea23dd41237f443cd467e485345 Homepage: https://cran.r-project.org/package=dotCall64 Description: CRAN Package 'dotCall64' (Enhanced Foreign Function Interface Supporting Long Vectors) Provides .C64(), which is an enhanced version of .C() and .Fortran() from the foreign function interface. .C64() supports long vectors, arguments of type 64-bit integer, and provides a mechanism to avoid unnecessary copies of read-only and write-only arguments. 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Lagakos (1989) ] [Jianguo Sun (1995) ]. 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The individual normal distributions from a mixture of normals can be inferred. Following the method described in Escobar (1994) we use a Dirichlet Process Prior (DPP) to describe stochastically our prior assumptions about the dimensionality of the data. 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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}. Package: r-cran-dpseg Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2052 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-markdown, r-cran-knitr, r-cran-htmltools, r-cran-rcppdynprog, r-cran-microbenchmark, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-dpseg_0.1.1-1.ca2604.1_arm64.deb Size: 1364292 MD5sum: e425b20903fa71c735d46f91095a7971 SHA1: e3f535aabace90fdb9db87fede5d766752774c9f SHA256: c4e44936e4f6ffef21ec5fc1bd58e4f2328b21977394d47603bb4de2709535fc SHA512: 6192884165111bc08246ba27d1912eb6cbbf594c04d08b90b9aae66c8baf781ecbdc90381bef4b1ded10acd78b9c138025f88015616e5aa05133d59b0f217e28 Homepage: https://cran.r-project.org/package=dpseg Description: CRAN Package 'dpseg' (Piecewise Linear Segmentation by Dynamic Programming) Piecewise linear segmentation of ordered data by a dynamic programming algorithm. The algorithm was developed for time series data, e.g. growth curves, and for genome-wide read-count data from next generation sequencing, but is broadly applicable. Generic implementations of dynamic programming routines allow to scan for optimal segmentation parameters and test custom segmentation criteria ("scoring functions"). Package: r-cran-dptm Architecture: arm64 Version: 3.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 361 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_arm64.deb Size: 249638 MD5sum: dcd095709500e61484510175c544be01 SHA1: 2282ff46105522db2975037c1fd93873ff946183 SHA256: 2216abb3ea81deefee208683119649206c6f91b3236632cfaa2a85db4474b0d5 SHA512: 46192f782a33d79bcb59f63c69f353da2afc9472516edec363660cbe00a23af4e734bfca54ca031a5f9e7248b88976200e314be949e45946cb9468da9276f54b Homepage: https://cran.r-project.org/package=DPTM Description: CRAN Package 'DPTM' (Dynamic Panel Multiple Threshold Model with Fixed Effects) Compute the fixed effects dynamic panel threshold model suggested by Ramírez-Rondán (2020) , and dynamic panel linear model suggested by Hsiao et al. (2002) , where maximum likelihood type estimators are used. Multiple thresholds estimation based on Markov Chain Monte Carlo (MCMC) is allowed, and model selection of linear model, threshold model and multiple threshold model is also allowed. Package: r-cran-dqrng Architecture: arm64 Version: 0.4.1-1.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-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_arm64.deb Size: 170780 MD5sum: a494224fa3ad4da03804f218539f8f87 SHA1: a2d69f51ad2e2f68ff1ec5583cdf6f84c76fb89b SHA256: 30320f023085bb2c98cbdef89a53bc510366a7460448d4bf4e9eaea4a45dd64c SHA512: 2125938b207c114627bae4202e1c6dd79ba9ffa0001def9fa8fbbf5fb8c785b69e5f055f849df0ef2e4220c8eec5acda051b78100c1ea236cde757453caebfb2 Homepage: https://cran.r-project.org/package=dqrng Description: CRAN Package 'dqrng' (Fast Pseudo Random Number Generators) Several fast random number generators are provided as C++ header only libraries: The PCG family by O'Neill (2014 ) as well as the Xoroshiro / Xoshiro family by Blackman and Vigna (2021 ). In addition fast functions for generating random numbers according to a uniform, normal and exponential distribution are included. The latter two use the Ziggurat algorithm originally proposed by Marsaglia and Tsang (2000, ). The fast sampling methods support unweighted sampling both with and without replacement. These functions are exported to R and as a C++ interface and are enabled for use with the default 64 bit generator from the PCG family, Xoroshiro128+/++/** and Xoshiro256+/++/** as well as the 64 bit version of the 20 rounds Threefry engine (Salmon et al., 2011, ) as provided by the package 'sitmo'. Package: r-cran-dr.sc Architecture: arm64 Version: 3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3888 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 3353268 MD5sum: 93409959a4ea0f90c665769e680db356 SHA1: e10a9835cf65a0d95f219798ad6f722e74a3e556 SHA256: 1af059a22ced882ada684f88f2a8294d5ad52a7f3d71391c6775e1e073b0e0f0 SHA512: e2790840a9183c7f6b90177d3eedd90f8bff11df6e5586865e03f7bf838c18c098430954fb61b2cf25f655f297c8a533b86a272ead283d91a939918d80f7f038 Homepage: https://cran.r-project.org/package=DR.SC Description: CRAN Package 'DR.SC' (Joint Dimension Reduction and Spatial Clustering) Joint dimension reduction and spatial clustering is conducted for Single-cell RNA sequencing and spatial transcriptomics data, and more details can be referred to Wei Liu, Xu Liao, Yi Yang, Huazhen Lin, Joe Yeong, Xiang Zhou, Xingjie Shi and Jin Liu. (2022) . It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well. Package: r-cran-dracor Architecture: arm64 Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 911 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_arm64.deb Size: 265976 MD5sum: 72202776212d15a59020f1e5287057a2 SHA1: 0aedeeb6720beedb0430961683a0ee936496c61d SHA256: 908ee55125007131495330910910093ee31bdc3f6723f9a2ed92b516b217396c SHA512: adf733cc0d791a7b8225e88683d9ad7c8b59ac6cb36d8e4eab79315c6910feca7eeadb1ae9d1cd05f06cb8d4178c40455663453cccb1970d52873a0665ef3778 Homepage: https://cran.r-project.org/package=dracor Description: CRAN Package 'dracor' (Decode Draco Format 3D Mesh Data) Decodes meshes and point cloud data encoded by the Draco mesh compression library from Google. Note that this is only designed for basic decoding and not intended as a full scale wrapping of the Draco library. Package: r-cran-drclust Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 867 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_arm64.deb Size: 318528 MD5sum: 41a8442c597fdee7f5501d5d149fcca0 SHA1: 900b065260bfb91d449db2272ce4e74f8273b467 SHA256: a97c72119c9f144608d10d9547f58766a83f63253df113d6ac523877420a18c6 SHA512: 95a1303edf59734b1b5bfc384941b1af777a1cfe487c7cea15fad3e9fee7c17fdb28050926bdacee8720e0950bcdd03baac7b61a14342d2e931f0f9f287e4ad2 Homepage: https://cran.r-project.org/package=drclust Description: CRAN Package 'drclust' (Simultaneous Clustering and (or) Dimensionality Reduction) Methods for simultaneous clustering and dimensionality reduction such as: Double k-means, Reduced k-means, Factorial k-means, Clustering with Disjoint PCA but also methods for exclusively dimensionality reduction: Disjoint PCA, Disjoint FA. The statistical methods implemented refer to the following articles: de Soete G., Carroll J. (1994) "K-means clustering in a low-dimensional Euclidean space" ; Vichi M. (2001) "Double k-means Clustering for Simultaneous Classification of Objects and Variables" ; Vichi M., Kiers H.A.L. (2001) "Factorial k-means analysis for two-way data" ; Vichi M., Saporta G. (2009) "Clustering and disjoint principal component analysis" ; Vichi M. (2017) "Disjoint factor analysis with cross-loadings" . Package: r-cran-drdid Architecture: arm64 Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 968 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 808264 MD5sum: 6d25d12d159592f80698eb07b564abee SHA1: b856bbf88106e1a7e87db8de7780992cd2b8d0c3 SHA256: 11323b4267b531d26e2929dc71ce1590e376d2900b81534106b2fd8c044dce15 SHA512: 0f2722bedcb3af162d188fd2502f50426253d7cced30a56f7d127b09b18a62d095d7c045e12517697b5334f2c4488b11ac3c213f70101b5a5f68253a03d69b65 Homepage: https://cran.r-project.org/package=DRDID Description: CRAN Package 'DRDID' (Doubly Robust Difference-in-Differences Estimators) Implements the locally efficient doubly robust difference-in-differences (DiD) estimators for the average treatment effect proposed by Sant'Anna and Zhao (2020) . The estimator combines inverse probability weighting and outcome regression estimators (also implemented in the package) to form estimators with more attractive statistical properties. Two different estimation methods can be used to estimate the nuisance functions. Package: r-cran-dream Architecture: arm64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1519 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_arm64.deb Size: 1159564 MD5sum: 462fada3b7be7898f4060a99d5780c36 SHA1: f1dad1c8ad0d3255fbfaedc8ddb4bf5af2306d73 SHA256: f3cd906cb60931042b45fe406bd8c70405faa6e64f0a6ace70aec9d35a1d704b SHA512: ef4ede28ecb721d4ca49631991b0a685c01d4b41973c92b706e9dff1f15dc23637b3c298592db04f385e10a31cf11fc8f2c0bd7831b2f06e63b325a676a083a6 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: arm64 Version: 0.8.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 165 Depends: libc6 (>= 2.17), libgomp1 (>= 4.9), r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-dress.graph_0.8.3-1.ca2604.1_arm64.deb Size: 83974 MD5sum: 15b786efc51cfa6fff08b82f2a9ad659 SHA1: 9e5033174fe1b6502b9f557db8de58ff2a07b112 SHA256: 496ae99357776498c34d6065f01841c91179367f3ea4146e36123fba272d17c3 SHA512: 78ac0c4621e98ff6fb590c6447b300291ff9407b5d681144516dd8992bdc7f7f8652d3103bf0a2a77cf3885e9649f3f7522b509dcd9d2af77e20240f4586d8cf Homepage: https://cran.r-project.org/package=dress.graph Description: CRAN Package 'dress.graph' (DRESS - A Continuous Framework for Structural Graph Refinement) DRESS is a deterministic, parameter-free framework for continuous structural graph refinement. It iterates a nonlinear dynamical system on real-valued edge similarities and produces a graph fingerprint as a sorted edge-value vector once the iteration reaches a prescribed stopping criterion. The resulting fingerprint is self-contained, isomorphism-invariant by construction, reproducible across vertex labelings under the reference implementation, numerically robust in practice, and efficient to compute with straightforward parallelization and distribution. Package: r-cran-drf Architecture: arm64 Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 576 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_arm64.deb Size: 237174 MD5sum: 5b995e35ad1109cd0255501d70740c16 SHA1: 542ab57db8bda2a4af6aa24fbfa44140f00e828f SHA256: 168049a4e8f2ff42320d3c42e008cdcf821ea899bd33d42bb22ab6a68d307dd3 SHA512: 3ba048b7996ac1543faba4d18279418e8afe15a300928ae108af78839369cf1165281b0ced67c83399e744cac7a118b652d96bfca010561bea67bf639006f1b4 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: arm64 Version: 1.1.10-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 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_arm64.deb Size: 190906 MD5sum: 88dd7e2bd564f2d9c40eaad8a951fb81 SHA1: 412817f82372e4704cd8ca800c24dd1eedc012c2 SHA256: babd41a01ad498a8cb9a3c5ab9396e5fbf08f2b0b1abe407e75376cb2c1bf8c3 SHA512: c2d00b0d1c7f3fe8f98b2bdf305c305602ec6ff713bb97b2fab68e99602e8e3400f44506e358048715c28efbc9a61181cbdff1e27b56fa07c410bc9581c0ab2f 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) . 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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: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1583 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1356960 MD5sum: 19ad6e0c3e902faaa6b2c84f91c017f5 SHA1: 77a8909220ead0ca5d1d3e77a4d8d7538f1e604e SHA256: 8dcbc022ed7e18b720922b4d3bd9012ab4d6bfa1a2fdab7592f6533664d64532 SHA512: 5bb5c07c1d272df06074a827e19434820904088677c6636e4e3cced0b2537b6af5b478437ea36210172b501cfd425ff08b630df5435f5d933698110758336af1 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: arm64 Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1558 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_arm64.deb Size: 1241142 MD5sum: 733dc6c5a4146604d5b0af3c255328d2 SHA1: c87f8ac0da290d50db44c88cb13b9f060b21bc7d SHA256: 300bb27808aaba3a76a34ec671319716e4815a41821888f37dc232bd95e23677 SHA512: 216a5546870db560eebacc48599ea8c9cb302bce88674ad099d20eaaff7126d7efab4dde306ddc941dcc3ca94f407c6f567622acf32902f213885ae86f3cd31e 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: arm64 Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2987 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_arm64.deb Size: 901840 MD5sum: 75936ed69d5d14bcdea862b8fe49a8c6 SHA1: f6011247cd488bdcaeee33c15b1671313285b896 SHA256: 6beb7ccda8c851bcce9170f1b344129d3a4a422494956c7481807fff1eaa007a SHA512: ac6add8db396faf64beac3c79a6c81ca28f7e51c524c3b5d46fc601ca6f1afa4bf77e1a0776d8d89c70305f5ca4471265f21d8cd80e7e0d11f7a547057071ddc Homepage: https://cran.r-project.org/package=drogonR Description: CRAN Package 'drogonR' (High-Performance HTTP Server for R via 'Drogon') Provides an 'R' interface to the 'Drogon' high-performance 'C++' 'HTTP' server framework (). Offers a 'plumber'-style application programming interface for building 'REST' services from 'R' with substantially higher throughput. Package: r-cran-dropout Architecture: arm64 Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 497 Depends: 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-dropout_2.2.0-1.ca2604.1_arm64.deb Size: 378896 MD5sum: a55220c10ef1384c7813009f594bdbb1 SHA1: bd60f019cdd4593a297fc03bc37a34bc9cdcd2b2 SHA256: eee8827fbdab2614ae8c9800e1446f889620bb2929d24ade4f78072250efbb0e SHA512: c601095fa30e7df3ce9b9a46948a1a4d3bd28445eb9784a62f1a010f74046896a3bfed0f8a41467494b2430a3b51c9ed6d6184f673273a25a5a62b5b6c2d972d Homepage: https://cran.r-project.org/package=dropout Description: CRAN Package 'dropout' (Handling Incomplete Responses in Survey Data Analysis) Offers robust tools to identify and manage incomplete responses in survey datasets, thereby enhancing the quality and reliability of research findings. Package: r-cran-drpt Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-biasedurn, r-cran-rootsolve, r-cran-future, r-cran-future.apply, r-cran-rdpack Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-drpt_1.1-1.ca2604.1_arm64.deb Size: 79620 MD5sum: 798b380170fb37c97f4cdc5e80bb9c35 SHA1: 4b2d702fdfd89b31d0d3ac1e23d0bef6c6786fee SHA256: 3c0dc032c2c09e8ca8bf6404f6b7e305960e20c6e8279ddde7d2e6b61b3f970a SHA512: 176b9e982618757c5faec55dc8a45c8c6cdfb72afeda46a426607c1927059bd637547aedf381e58bbe1453215af2f86112222a3c7771497473e01b218536426f Homepage: https://cran.r-project.org/package=DRPT Description: CRAN Package 'DRPT' (Density Ratio Permutation Test) Implementation of the Density Ratio Permutation Test for testing the goodness-of-fit of a hypothesised ratio of two densities, as described in Bordino and Berrett (2025) . Package: r-cran-drrglm Architecture: arm64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6749 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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-glmnet, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-drrglm_0.3.2-1.ca2604.1_arm64.deb Size: 6783576 MD5sum: f0d7ee02f1a046d42c294fe88a1a2f78 SHA1: 54028c594114ed2125a0debcb57abd88d2b1accf SHA256: eeaef2c0466e6818dd710372454297e6f9be0795958db556e10081e7ac63d426 SHA512: b25af859bdc8cb77e9c632345119f8f3c7f9ce0af990417d877c4f7bd3bd90e579775f0e23069452428325b35cfa1c07b1095959369ee6269fbed751eda292b7 Homepage: https://cran.r-project.org/package=drrglm Description: CRAN Package 'drrglm' (Doubly Regularized Matrix-Variate Regression) The doubly regularized matrix-variate regression solves a low-rank-plus-sparse structure for matrix-variate generalized linear models through a weighted combination of nuclear-norm and L1-norm. The methodology implemented by this package is described in the paper "Doubly Regularized Matrix-Variate Regression", which has been tentatively accepted for publication but does not yet have a DOI or URL. A formal citation will be added in a future update once the final publication details are available. Package: r-cran-drsurvcrt Architecture: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 386 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-frailtyem, r-cran-survival, r-cran-ggplot2, r-cran-pracma, r-cran-abind, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-drsurvcrt_0.0.1-1.ca2604.1_arm64.deb Size: 224534 MD5sum: 9fdd9bd3e217781dc9d6662f1807e0c2 SHA1: 500b3f117252acd14d4007197a79185646207c55 SHA256: ba6839da06f5721b872bedb687eed0bb34fbe661f76b621ce194088a73b85a65 SHA512: 693d681604234861b688b200e53ef80140bbd3396ae49d7d5eb53ca8f501435d664b7937efaf2a7a7454720dd05e53dadb4889ee486300b838ec6415ab2a230a Homepage: https://cran.r-project.org/package=DRsurvCRT Description: CRAN Package 'DRsurvCRT' (Doubly-Robust Estimation for Survival Outcomes inCluster-Randomized Trials) Cluster-randomized trials (CRTs) assign treatment to groups rather than individuals, so valid analyses must distinguish cluster-level and individual-level effects and define estimands within a potential-outcomes framework. This package supports right-censored survival outcomes for both single-state (binary) and multi-state settings. For single-state outcomes, it provides estimands based on stage-specific survival contrasts (SPCE) and restricted mean survival time (RMST). For multi-state outcomes, it provides SPCE as well as a generalized win-based restricted mean time-in-favor estimand (RMT-IF). The package implements doubly robust estimators that accommodate covariate-dependent censoring and remain consistent if either the outcome model or the censoring model is correctly specified. Users can choose marginal Cox or gamma-frailty Cox working models for nuisance estimation, and inference is supported via leave-one-cluster-out jackknife variance and confidence interval estimation. Methods are described in Fang et al. (2025) "Estimands and doubly robust estimation for cluster-randomized trials with survival outcomes" . Package: r-cran-drugdemand Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 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-purrr, r-cran-stringr, r-cran-plotly, r-cran-survival, r-cran-mvtnorm, r-cran-erify, r-cran-mass, r-cran-nlme, r-cran-l1pack, r-cran-eventpred, r-cran-foreach, r-cran-doparallel, r-cran-dorng Filename: pool/dists/resolute/main/r-cran-drugdemand_0.1.3-1.ca2604.1_arm64.deb Size: 282942 MD5sum: e67f2341c62fd88d997a0ed5ab0af90a SHA1: 9d996c228cc8cfe1176b9dbc92f079f974028824 SHA256: 051da7d783a3d2f818b3ca97c533a68ac9b156006abe4e3c391d9cfb228e034d SHA512: 01a9c771ee37931da83fb7d9cd522efc2d48057a79f34790e6e031a6d1c284adde9fed919f2cc4a22930097232850a02dc87dc6e501513b7a44e69d156aa401f Homepage: https://cran.r-project.org/package=drugDemand Description: CRAN Package 'drugDemand' (Drug Demand Forecasting) Performs drug demand forecasting by modeling drug dispensing data while taking into account predicted enrollment and treatment discontinuation dates. The gap time between randomization and the first drug dispensing visit is modeled using interval-censored exponential, Weibull, log-logistic, or log-normal distributions (Anderson-Bergman (2017) ). The number of skipped visits is modeled using Poisson, zero-inflated Poisson, or negative binomial distributions (Zeileis, Kleiber & Jackman (2008) ). The gap time between two consecutive drug dispensing visits given the number of skipped visits is modeled using linear regression based on least squares or least absolute deviations (Birkes & Dodge (1993, ISBN:0-471-56881-3)). The number of dispensed doses is modeled using linear or linear mixed-effects models (McCulloch & Searle (2001, ISBN:0-471-19364-X)). 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Package: r-cran-dsdp Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 602 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-rlang Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-dsdp_0.1.1-1.ca2604.1_arm64.deb Size: 453192 MD5sum: 8239543453c86537719fb95e42fb1491 SHA1: df6fa8112185ad24b8c007642166a74d5b41d5b6 SHA256: a35033edf9382f046fe035419c75ccce7f7b5f3b4c01011b52a27aa006e8c51f SHA512: 771acb40382e74df6f95c085707646d819c812aef459d67fd264cc0dcb68026bc729eacf003743530b03ebdb30c5d38af87ea7ccb290c7f50c66a92c4791c19e Homepage: https://cran.r-project.org/package=dsdp Description: CRAN Package 'dsdp' (Density Estimation with Semidefinite Programming) The models of probability density functions are Gaussian or exponential distributions with polynomial correction terms. 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Methods are described in Thorson et al. (2024) "Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms." Package: r-cran-dsl Architecture: arm64 Version: 0.1-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-hive Filename: pool/dists/resolute/main/r-cran-dsl_0.1-7-1.ca2604.1_arm64.deb Size: 292634 MD5sum: 144a93732b49734e1d4f18388118b0a4 SHA1: 33ca216f31ca5fdebc8c95bcc9db06f92a7ad8e6 SHA256: acdc2e9dfd413ef3fe762c03df9e076487859294667f228e5914dc9266913023 SHA512: 55adcefc0d2ec07604375df4cb0b2e13f884b9e06c062ca529d1aad78cd681c8da1b20e571ee3edf376d7f437327c8bf2548bf1edb77f6076a2297b301ba5c45 Homepage: https://cran.r-project.org/package=DSL Description: CRAN Package 'DSL' (Distributed Storage and List) An abstract DList class helps storing large list-type objects in a distributed manner. 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Package: r-cran-dslice Architecture: arm64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1690 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-scales Filename: pool/dists/resolute/main/r-cran-dslice_1.2.2-1.ca2604.1_arm64.deb Size: 1522624 MD5sum: 7a34a7060bdf4d6740221b434bdc4696 SHA1: a207ab05080907b2e5efefe22f946c5a1c77e63e SHA256: e02919e853c613c213f69374f669ffdddd3e2a3b1a34f5d2cdb4f053755db502 SHA512: c4673bb1be173453aa3055e0953f546af2c7e05196481c3e29b5743ebc49482fce60b7aeb5d4b6c66b7c2c71993759feedd07d2082d8acdbdfaf5bdba09df03f Homepage: https://cran.r-project.org/package=dslice Description: CRAN Package 'dslice' (Dynamic Slicing) Dynamic slicing is a method designed for dependency detection between a categorical variable and a continuous variable. 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Package: r-cran-dspline Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2707 Depends: libc6 (>= 2.17), 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-rlang, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dspline_1.0.4-1.ca2604.1_arm64.deb Size: 1875780 MD5sum: a9dad2f094ed472db4c7ff6bad4f2a60 SHA1: 6535b418ab1d2404475005aac6481416d3a6fb75 SHA256: d8856e81aaae022836aed7b8cc5b99844ec73e2c4c05e77d15b0c17657c91781 SHA512: 24eb62763333c7b49a68563b72b3147af8a29648d280d9667e8ad5cdfe4b610d70bde5d8c631ef50b64085d10b24740786faefaa8331883f022f7485bb3cd20d Homepage: https://cran.r-project.org/package=dspline Description: CRAN Package 'dspline' (Tools for Computations with Discrete Splines) Discrete splines are a class of univariate piecewise polynomial functions which are analogous to splines, but whose smoothness is defined via divided differences rather than derivatives. Tools for efficient computations relating to discrete splines are provided here. These tools include discrete differentiation and integration, various matrix computations with discrete derivative or discrete spline bases matrices, and interpolation within discrete spline spaces. These techniques are described in Tibshirani (2020) . Package: r-cran-dstarm Architecture: arm64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 493 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-deoptim, r-cran-rwiener, r-cran-rtdists, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dstarm_0.5.0-1.ca2604.1_arm64.deb Size: 280730 MD5sum: 9c8d72683c3918e9ed39f17e2a4ba173 SHA1: b74ea5b93eca4ab4f1f12c70677014ac5baf0a4e SHA256: 58cbba1e760b8558fc3a8124248427fcfead4d8b931c30b9568d1a85364218c0 SHA512: 2ed57103ad182f6b3fbf71de826de96ccfec9c4a7bc6bb196db0aca3054296bb8867b4a5c736df9d10e70737f9370f03bffec1953c3e993e917131acee5dee7b Homepage: https://cran.r-project.org/package=DstarM Description: CRAN Package 'DstarM' (Analyze Two Choice Reaction Time Data with the D*M Method) A collection of functions to estimate parameters of a diffusion model via a D*M analysis. Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function. Package: r-cran-dswe Architecture: arm64 Version: 1.8.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3437 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mixtools, r-cran-gss, r-cran-e1071, r-cran-dplyr, r-cran-xgboost, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-dswe_1.8.4-1.ca2604.1_arm64.deb Size: 3072806 MD5sum: 7e2a43744534ce29fc319f4755591017 SHA1: 71d50e8420983a116fcfdd27b6a4172540d2bcde SHA256: e666dcfb29db8fbc41403fce7ca5a449862ebed077526d0f3e2638d4307a56d1 SHA512: 5926cb4d5bf326db2c2e370c77f86733d5ac83cfbd6838341ce3e3049049fd0bd6a9c64fc92f2bf539de8c4ea053e21d828a667ec94d30944a4ee0bab6063a73 Homepage: https://cran.r-project.org/package=DSWE Description: CRAN Package 'DSWE' (Data Science for Wind Energy) Data science methods used in wind energy applications. Current functionalities include creating a multi-dimensional power curve model, performing power curve function comparison, covariate matching, and energy decomposition. Relevant works for the developed functions are: funGP() - Prakash et al. (2022) , AMK() - Lee et al. (2015) , tempGP() - Prakash et al. (2022) , ComparePCurve() - Ding et al. (2021) , deltaEnergy() - Latiffianti et al. (2022) , syncSize() - Latiffianti et al. (2022) , imptPower() - Latiffianti et al. (2022) , All other functions - Ding (2019, ISBN:9780429956508). Package: r-cran-dtda.cif Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 93712 MD5sum: 1ce78d2f93ca6560e5684d2bbab5883d SHA1: 9557fec0d7e4729863870baa6da91190f09a74c6 SHA256: 63247fc8b3d4a3b470e821c8557ef0f38c737222894c1ae68ea318df6719a978 SHA512: a19350aad25406d0e645f51e0e9eec81ae09fd0c05015a31bed2b3ea3784c5733245bea4996f8f31111624593474328b52e59b783a642f8d24e6112fa58362df 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: arm64 Version: 1.5.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1536 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-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_arm64.deb Size: 1115688 MD5sum: 756fefbe3e0658e574e14658cbe04994 SHA1: 109ce820aef3671d33d4b92e1adc02caf58dee33 SHA256: e07d6d2d17d6f70c85b6ad93a05f2023ad31e9e5c83600bdb4236e0eb3b1a6d0 SHA512: c870abbc37830636e8da96f9de43a0353baaa577685249009afa03842b9e8b10cd26b50a73fbad05d6aaea4170fbed980adbe4cc398266cac19300c9103a5b37 Homepage: https://cran.r-project.org/package=dti Description: CRAN Package 'dti' (Analysis of Diffusion Weighted Imaging (DWI) Data) Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging modality, that measures diffusion of water in tissues like the human brain. The package contains R-functions to process diffusion-weighted data. The functionality includes diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), modeling for high angular resolution diffusion weighted imaging (HARDI) using Q-ball-reconstruction and tensor mixture models, several methods for structural adaptive smoothing including POAS and msPOAS, and a streamline fiber tracking for tensor and tensor mixture models. The package provides functionality to manipulate and visualize results in 2D and 3D. Package: r-cran-dtrkernsmooth Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 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_arm64.deb Size: 100488 MD5sum: 1cdbf1bb301f5758d89e4715d5ddb3f2 SHA1: 7687dfa83a3f3836022f41aa41821d3f98909a9b SHA256: 900bd9801dd610b41dcaaae2dfe5c23cc77e24227429946e88ea9f38167bbed6 SHA512: ed088ad0ad5ae3e38442890665d6bd295962bec880908404a345980c9d36a947196f28be20c658df7e6a3c1764a6dbf2d37b607ee1995a5af6218552888137c1 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: arm64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 463 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 301628 MD5sum: ebf0af26598b051c5125d885888e50a2 SHA1: 556dc3f35d1aaa2c04cce5e4b266ca3c217ef5d3 SHA256: ebe7a15466dc0786cb0300849c003411a61cb77f28968bca8fe9e27485842792 SHA512: 13cf9e7f69a518eea105e06365fd8ea1dada2f63457732c9ee9f57f1f74f8df3a5dd9eb738b8a330bc4b4a12180328811e7222e84356518581078b37ce82ba36 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: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 114034 MD5sum: cf11789b4fa2d9bcd52cec0cdacec494 SHA1: 03552892ee34932d593b292dc9a0662b7a0b8566 SHA256: be973cb1eba42d12d5ac84b0b5d60828d57d3dba3cd876f314aa18205602459a SHA512: 92e6a1ab425031f393b4a68de28a2c5fe6a2bcafa27b99535ff9f7afe4e1c819c5678cdb6bfb004621d1e487f7c32bb0d0b7fd2e17d0d250f42e029b496efa14 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: arm64 Version: 1.23-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 761 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 601446 MD5sum: da538584e76b38b8892b1b3a11979568 SHA1: 237d385aca9da8389bba9698c5fba429a4f8b95c SHA256: 72a0df106827a1b770cb68b99f091f39a7aaeade2c17df61e2f22a6475525bf5 SHA512: ad300e404711d7cdaa1530c8fdffc89e1f665183bb1b667f626048509f14c2b8c1ce9d79fe62b39998ed8bd1e50e888857d30147b17f46972dfe207ff958a49b Homepage: https://cran.r-project.org/package=dtw Description: CRAN Package 'dtw' (Dynamic Time Warping Algorithms) A comprehensive implementation of dynamic time warping (DTW) algorithms in R. DTW computes the optimal (least cumulative distance) alignment between points of two time series. 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Package: r-cran-dtwclust Architecture: arm64 Version: 6.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3984 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-proxy, r-cran-dtw, r-cran-clue, r-cran-cluster, r-cran-dplyr, r-cran-flexclust, r-cran-foreach, r-cran-ggplot2, r-cran-ggrepel, r-cran-rlang, r-cran-matrix, r-cran-rspectra, r-cran-rcpp, r-cran-rcppparallel, r-cran-reshape2, r-cran-shiny, r-cran-shinyjs, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-doparallel, r-cran-iterators, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-dtwclust_6.0.0-1.ca2604.1_arm64.deb Size: 2792564 MD5sum: 242266ce56f836e5385cbba49fea2de5 SHA1: 3833a311da85262734946d1d41ad8f1c865d3e9d SHA256: 36234af893057b42745a947889b7e35ebfa23d1fac9c529d7bf0117bd7b61309 SHA512: 5a40f4a7c0f00adaa7b616137d132d9920824f234d01c929c432d2dd4952b46d915b2f0dbbda333b81e42514e79534cbc78bc42b4dcc39ff69ca3f1488375ce6 Homepage: https://cran.r-project.org/package=dtwclust Description: CRAN Package 'dtwclust' (Time Series Clustering Along with Optimizations for the DynamicTime Warping Distance) Time series clustering along with optimized techniques related to the Dynamic Time Warping distance and its corresponding lower bounds. 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: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 326 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-dualtrees_0.1.5-1.ca2604.1_arm64.deb Size: 227704 MD5sum: 711cee7b88c9f6249e3669e397b4e795 SHA1: cb5894cfa39ffa3829937bcaa4480ead8c3369a2 SHA256: 59093efc722e3bbd5d2c4faae4e4e55ee6f6777248ae5136664db32805ed955f SHA512: b02701023fc9f2bc35061730ea8f0c0b04a926b98cc10de41b6d7ca9171a2046f24d8d8f19957d9a2a5e3f99653967966e035b6ae84ecafb3e37b7f509f2a0e8 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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Package: r-cran-dwdradar Architecture: arm64 Version: 0.2.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1703 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_arm64.deb Size: 164670 MD5sum: 86e9f70866d358a12fe69f19c41e3fa4 SHA1: 049ee1a73b97985839537235428b9b0d8fc891ed SHA256: e84deff9efb58b0f248d41983cdb134aed929f049dbaeedf74e7e31245e99d22 SHA512: 56fefe5c91fe3fc1f8974750f05e749fc03d82c97860d3c98c7dd13995738908fe04cea5e01578f7117d9a3fc1fbc6c49281bcf5937b0037e9e609f589971802 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. Package: r-cran-dyadicarma Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 524 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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-dyadicarma_1.0.1-1.ca2604.1_arm64.deb Size: 255482 MD5sum: cf97fb1c872c90b87a1f46c255d75251 SHA1: d898d09c80084f470c158fb0eb3d8bac8224d2a3 SHA256: cc66589372321b4fd89ff70c4dc0a650e14999d00becbe42dc9afed7b01dabf9 SHA512: 08d50e2480ede8b66cfa77032cb6dfc4f84ce0def5ddedda5c16147123f40ea809e21304defb77dad69bc8de3bf3809a61b90183fc4a42646d6b0b7479577a1b Homepage: https://cran.r-project.org/package=DyadiCarma Description: CRAN Package 'DyadiCarma' (Dyadic Matrices and their Algebra using 'Rcpp' and'RcppArmadillo') Provides methods for efficient algebraic operations and factorization of dyadic matrices using 'Rcpp' and 'RcppArmadillo'. 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Package: r-cran-dyadratios Architecture: arm64 Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 558 Depends: libc6 (>= 2.43), 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-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-lubridate, r-cran-rio, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-dyadratios_2.1-1.ca2604.1_arm64.deb Size: 279788 MD5sum: d9b844659279b69d5554860a29211899 SHA1: 8f81727ddd532066a5527b176b7a0bad0e9e125f SHA256: 3524aa7ef6b847d270eb871e2b864ada596f536af1562fd13f9d5e7377a5ff5e SHA512: b653533bd20044577f7ad43c7df26f4ecb04b8daae155af60d1640191c25dc1e49c228b42fb34d34cf73bb13c99f581efa9aace394f02f8fc90dabd6fb27ff3b Homepage: https://cran.r-project.org/package=DyadRatios Description: CRAN Package 'DyadRatios' (Dyad Ratios Algorithm for Latent Variable Estimation) Implements the Dyad Ratios algorithm for estimating latent variables from time-series survey data. The algorithm estimates a latent mood dimension (or two dimensions) from a set of issue opinion series. Supports annual, quarterly, monthly, and daily aggregation intervals, optional exponential smoothing, and up to two latent dimensions. Input data can be provided as a data frame or read from delimited text files. Based on Stimson's 'MCalc' C++ program. See Stimson (2018) for more details. Package: r-cran-dynatop Architecture: arm64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1137 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_arm64.deb Size: 655712 MD5sum: d8f381ce57e2c7285c548e2381b21a8a SHA1: ecf06962a2fa5b471055a7ca942fedef8095c4d2 SHA256: 73428a369c669f3b18674e700401d1d83845a0920caff18ba6a6561749b986a9 SHA512: c62c2e4bcf72aec2defb21b3ed2ef70a7035a91ae0e21e4e93c74d1b5313be98242775cf2cae5ce95319694a3a983b286efbaafce407e4e88cf16078e9898c32 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: arm64 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_arm64.deb Size: 525884 MD5sum: a54eec46f91843d6297f04a3736286e5 SHA1: 0f6d2384fadb98f4eb6bb313424188149ab45d8e SHA256: d4219bfebf963197262504d87439913f3ff6e5b9727ec626a7b2014df1e86e55 SHA512: b692411104a012e49ded860728c31745042ee0d45bc71cffb78848e8b78439b9f3521be7abf20b97bdf4ed37ec97d29da4eb6698079983c7c5f4587ad598238e 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"). 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All models are explained in detail by Hellmann et al. (2023; Preprint available at , published version: ). Implemented models are the dynaViTE model, dynWEV model, the 2DSD model (Pleskac & Busemeyer, 2010, ), and various race models. C++ code for dynWEV and 2DSD is based on the 'rtdists' package by Henrik Singmann. Package: r-cran-dynmix Architecture: arm64 Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 197220 MD5sum: 2476e740d9a518d20d8d31e38d8b5ba8 SHA1: cecd47494afe8caef9373a86d8376222971af890 SHA256: 776b7bd96cf0f80f4aeb29aecc29b1852a5fb00a6f865e16868e0d2314e3f03f SHA512: 76f31efb8c5b1a527efa5676a4ebf6295a28a804284d499e588524378870ad0e33379bad4481450cf528a532991b228344bce8a39df2704dadfbe0e044a90281 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) . 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Many such data sets are noisy, multivariate, and multi-subject in nature. The change functions may also be continuous, or continuous but interspersed with periods of discontinuities (i.e., showing regime switches). The package 'dynr' (Dynamic Modeling in R) is an R package that implements a set of computationally efficient algorithms for handling a broad class of linear and nonlinear discrete- and continuous-time models with regime-switching properties under the constraint of linear Gaussian measurement functions. The discrete-time models can generally take on the form of a state-space or difference equation model. The continuous-time models are generally expressed as a set of ordinary or stochastic differential equations. All estimation and computations are performed in C, but users are provided with the option to specify the model of interest via a set of simple and easy-to-learn model specification functions in R. Model fitting can be performed using single-subject time series data or multiple-subject longitudinal data. Ou, Hunter, & Chow (2019) provided a detailed introduction to the interface and more information on the algorithms. 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Dynamic screening systems (DySS) are methods that aim to identify and give signals to processes with poor performance as early as possible. This package is designed to implement dynamic screening systems and the related methods. References: Qiu, P. and Xiang, D. (2014) ; Qiu, P. and Xiang, D. (2015) ; Li, J. and Qiu, P. (2016) ; Li, J. and Qiu, P. (2017) ; You, L. and Qiu, P. (2019) ; Qiu, P., Xia, Z., and You, L. (2020) ; You, L., Qiu, A., Huang, B., and Qiu, P. (2020) ; You, L. and Qiu, P. (2021) . 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'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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Package: r-cran-ecosolver Architecture: arm64 Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1648 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-matrix, r-cran-covr, r-cran-slam Filename: pool/dists/resolute/main/r-cran-ecosolver_0.6.1-1.ca2604.1_arm64.deb Size: 1044764 MD5sum: 52d9e47e8208b94783e9a69086694905 SHA1: 040e2c72db36600ce91ac0637cf4848bf6e13616 SHA256: f911076c65a1caaaced69b91174ae5fd415b08bf4a465ad68c905ac7ac58c890 SHA512: b87449d93ded7450029972f5f548b4edaf29591f4eb61860fd19d646be65c44877524ff92ca76a261ce3d4c00f680aa832f0bed9ff6df4f1e4ccb80528a7b120 Homepage: https://cran.r-project.org/package=ECOSolveR Description: CRAN Package 'ECOSolveR' (Embedded Conic Solver in R) R interface to the Embedded COnic Solver (ECOS), an efficient and robust C library for convex problems. 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: arm64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1246 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_arm64.deb Size: 1124924 MD5sum: c4b35a15434dafd5a600d165a8e2b4a6 SHA1: 275a67de0baa3ddd72df96279d78ff5db4926f73 SHA256: 08e6a42b36bd3e62425bc8366871b643c74b54680f970722404972f447f01e3f SHA512: eae33739956e7fd5c4d718daa9716783460eab1e45dbbf67698ae3a49a5b03f8ed5a14db7e18baefde4e5cb282b76bcfbf1e11643ddc2c6e2ca637d75f2e1f31 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: arm64 Version: 3.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2065 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_arm64.deb Size: 1805118 MD5sum: 6d90bfd7e7290c095de8b8fb36b64b38 SHA1: 343227e6d82f99b99fe9e786b76e56b2df7210e4 SHA256: 03f55fa8b2b04d5d06aff86964d0d3eb2b6c14cf6e15f7713f83d6cd00bb63cd SHA512: 5b05fd542e829ca395ef5c6a19498dc0e76d4fac59c0e8ccf6a9b0308dd337a1c9c2e275ce94ec0a61541a69c827561c3bb558c5e97d7c4d4912e4e8ba12944e Homepage: https://cran.r-project.org/package=ecp Description: CRAN Package 'ecp' (Non-Parametric Multiple Change-Point Analysis of MultivariateData) Implements various procedures for finding multiple change-points from Matteson D. et al (2013) , Zhang W. et al (2017) , Arlot S. et al (2019). Two methods make use of dynamic programming and pruning, with no distributional assumptions other than the existence of certain absolute moments in one method. Hierarchical and exact search methods are included. All methods return the set of estimated change- points as well as other summary information. Package: r-cran-ecr Architecture: arm64 Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2116 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_arm64.deb Size: 1831834 MD5sum: 3ec2be04686cfbbea707f4493d93752f SHA1: b1e8c0a0ff67d26f1a955023ee1a24e4a7d1a1bb SHA256: 8e073c12110496f35ed26c3d1a8336dd475f61fddc5b1c372bed012e59ea680b SHA512: aac77faff9de95e4ac6ecdebf4b75e8a586b6ae860a0a64c2c7d0a4d0dc255e531f6c7ddb3c5f4bf3c0ed9399e0ad374cb6ca057ef7104d84554cb51c616a008 Homepage: https://cran.r-project.org/package=ecr Description: CRAN Package 'ecr' (Evolutionary Computation in R) Framework for building evolutionary algorithms for both single- and multi-objective continuous or discrete optimization problems. A set of predefined evolutionary building blocks and operators is included. Moreover, the user can easily set up custom objective functions, operators, building blocks and representations sticking to few conventions. The package allows both a black-box approach for standard tasks (plug-and-play style) and a much more flexible white-box approach where the evolutionary cycle is written by hand. Package: r-cran-eddington Architecture: arm64 Version: 4.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 199730 MD5sum: 0d677588a8a6c69b3bdfe30745104420 SHA1: 1dd80492ab711106dbacfec59be244530ac83af9 SHA256: 336c1e86e02e7714f0bb56153f26bf5d20ddbdeff9df9af23689bd0aee925144 SHA512: 36c50e718392d06ef2a0984fd05ca5cd5ec87d5b52b42371a02078215391a36b556e58845a5b5fa0bcc4873fdcafe154b4d107913f43199533c5480124caab65 Homepage: https://cran.r-project.org/package=eddington Description: CRAN Package 'eddington' (Compute a Cyclist's Eddington Number) Compute a cyclist's Eddington number, including efficiently computing cumulative E over a vector. A cyclist's Eddington number is the maximum number satisfying the condition such that a cyclist has ridden E miles or greater on E distinct days. The algorithm in this package is an improvement over the conventional approach because both summary statistics and cumulative statistics can be computed in linear time, since it does not require initial sorting of the data. These functions may also be used for computing h-indices for authors, a metric described by Hirsch (2005) . Both are specific applications of computing the side length of a Durfee square . Some additional author-level metrics such as g-index and i10-index are also included in the package. Package: r-cran-edgebundle Architecture: arm64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 473 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 298760 MD5sum: 0333f6bdf8204c3992f1f46b874228c9 SHA1: 7805e1c7abf7b630dbeba5c6f15ba527e24c17ed SHA256: e8978c760821cb4670be762ca65db174407fc76df505f36743d1b8f6dbfeb5a9 SHA512: c31cf33f6a9b56ffcdb5641542aa0fc23ca182d386ed78f58f55d2dc80954092a122015bc77c27932fe68de0f3cd843c10169cf0d2632d0ff03fa9c3814bac12 Homepage: https://cran.r-project.org/package=edgebundle Description: CRAN Package 'edgebundle' (Algorithms for Bundling Edges in Networks and Visualizing Flowand Metro Maps) Implements several algorithms for bundling edges in networks and flow and metro map layouts. This includes force directed edge bundling , a flow algorithm based on Steiner trees and a multicriteria optimization method for metro map layouts . Package: r-cran-edina Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 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_arm64.deb Size: 149572 MD5sum: c1172fed245a959f28320053703e4f21 SHA1: 33ea221da880f762578961076c1f50d6b2331337 SHA256: 5ae8de98879ac009b4bab2e0e02809c878752e538c431dc424c638aea1a41fb2 SHA512: bf1fa489493aa7fb7f7d5a40ba29cf8dfa9aac1df9b0c3b0715ef71c694aa05013d84ae10bf84943a2ab5d608ef2d3fa8cba327de6aeda2bb55b39ae0bdbd908 Homepage: https://cran.r-project.org/package=edina Description: CRAN Package 'edina' (Bayesian Estimation of an Exploratory Deterministic Input, Noisyand Gate Model) Perform a Bayesian estimation of the exploratory deterministic input, noisy and gate (EDINA) cognitive diagnostic model described by Chen et al. (2018) . Package: r-cran-edith Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2366 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_arm64.deb Size: 2014354 MD5sum: 572327a25a34a7dcea0808cadeccba68 SHA1: e7d6f73bda0a1460b6000b08ed91e7d40f55ef47 SHA256: 85a4cc3a393b10d65ee3206b9dbea520d675fd399d3d3c6ebcd5cf1d95d96dbc SHA512: c91a9e69ef1ddc4e3794fda5beecaf8d2b5535582e041745a08a2db0c61a37121885b0a3e1e203eb85943836cb164bccd379f953cc561a3c2f2cd0ce2ea4f57d Homepage: https://cran.r-project.org/package=eDITH Description: CRAN Package 'eDITH' (Model Transport of Environmental DNA in River Networks) Runs the eDITH (environmental DNA Integrating Transport and Hydrology) model, which implements a mass balance of environmental DNA (eDNA) transport at a river network scale coupled with a species distribution model to obtain maps of species distribution. eDITH can work with both eDNA concentration (e.g., obtained via quantitative polymerase chain reaction) or metabarcoding (read count) data. Parameter estimation can be performed via Bayesian techniques (via the 'BayesianTools' package) or optimization algorithms. An interface to the 'DHARMa' package for posterior predictive checks is provided. See Carraro and Altermatt (2024) for a package introduction; Carraro et al. (2018) and Carraro et al. (2020) for methodological details. Package: r-cran-edlibr Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 77600 MD5sum: ad83d7263dca6b004dcce68eb710e630 SHA1: 45797415f6f645af1cfe6bf18e93fb920888da7e SHA256: d789b5ae7f222d8c5f47dcb577d7f5d678aef4d1d1c629007b0e651e2d87eacf SHA512: 1c53ab40198ca07f7a8ecc021cdc280b97b5cef9d3a819c4afd8b18773aa433e2632e1ab05ea421c4d158ea45566897b94b6436b4ee610556438aaf97c10d07e Homepage: https://cran.r-project.org/package=edlibR Description: CRAN Package 'edlibR' (R Integration for Edlib, the C/C++ Library for Exact PairwiseSequence Alignment using Edit (Levenshtein) Distance) Bindings to edlib, a lightweight performant C/C++ library for exact pairwise sequence alignment using edit distance (Levenshtein distance). The algorithm computes the optimal alignment path, but also can be used to find only the start and/or end of the alignment path for convenience. Edlib was designed to be ultrafast and require little memory, with the capability to handle very large sequences. Three alignment methods are supported: global (Needleman-Wunsch), infix (Hybrid Wunsch), and prefix (Semi-Hybrid Wunsch). The original C/C++ library is described in "Edlib: a C/C++ library for fast, exact sequence alignment using edit distance", M. Šošić, M. Šikić, . Package: r-cran-edma Architecture: arm64 Version: 1.5-4-1.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_arm64.deb Size: 365494 MD5sum: 300dca3cb405fc2f31eeb47d97f246e7 SHA1: 00bb0302ae11ba4712713636f7c406d3d84eee2b SHA256: e2539845b93b8bc354a76f31491272090ac12686243759e551e1fbfb511b7b79 SHA512: 944523f88369c2530ea5946032ed4b2ae03349c9b71b0f5b3ae40296d341dc480fa399d690b32dc196ad929dd44e1186e73b80c6a147d708ca3447eb9cf0bc9c Homepage: https://cran.r-project.org/package=eDMA Description: CRAN Package 'eDMA' (Dynamic Model Averaging with Grid Search) Perform dynamic model averaging with grid search as in Dangl and Halling (2012) using parallel computing. Package: r-cran-ednajoint Architecture: arm64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6624 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_arm64.deb Size: 3345996 MD5sum: 530cfc5782af3b302f20a7e23588f342 SHA1: 2129a36fa7ef21292efb571459df2101b1661b6f SHA256: 92a8a6b82a3c62886000f74a4b9612dc6fb390a728193240bb13a3b63d563369 SHA512: 32fd2c2db0eee497eb1e673e505aac5db8894eee65970c5393048d5e9d8a1f85f0f5be5da87c0ead1c945d7f162d0464d60a99c8adb93087e7dfa58e21181374 Homepage: https://cran.r-project.org/package=eDNAjoint Description: CRAN Package 'eDNAjoint' (Joint Modeling of Traditional and Environmental DNA Survey Datain a Bayesian Framework) Models integrate environmental DNA (eDNA) detection data and traditional survey data to jointly estimate species catch rate (see package vignette: ). Models can be used with count data via traditional survey methods (i.e., trapping, electrofishing, visual) and replicated eDNA detection/nondetection data via polymerase chain reaction (i.e., PCR or qPCR) from multiple survey locations. Estimated parameters include probability of a false positive eDNA detection, a site-level covariates that scale the sensitivity of eDNA surveys relative to traditional surveys, and gear scaling coefficients for traditional gear types. Models are implemented with a Bayesian framework (Markov chain Monte Carlo) using the 'Stan' probabilistic programming language. Package: r-cran-edotrans Architecture: arm64 Version: 0.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 118994 MD5sum: d159cccac246b6ca939b7b17603d523b SHA1: 4c82d758fd9a9bac4ad12b0ecdfd6e6e8b8eab78 SHA256: 0aa711f5463f0781fece65c8880c966ccc69df893980c427848758e7bdec7298 SHA512: 91fff3249dea669acefebccfbf5837c7f3b01a6a1cb605e24d7c6834ab62fbc98d64e30732bd2a8d215a04789e60083d91e6c0b5c5d3ea54fe76e9c4097216f7 Homepage: https://cran.r-project.org/package=EDOtrans Description: CRAN Package 'EDOtrans' (Euclidean Distance-Optimized Data Transformation) A data transformation method which takes into account the special property of scale non-invariance with a breakpoint at 1 of the Euclidean distance. Package: r-cran-ef Architecture: arm64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 853 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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_arm64.deb Size: 344336 MD5sum: 8caa7a3d4c5804dbda8aaae31134d7b8 SHA1: 308026852f2f69f9bd5d3182ed1215e38215a13f SHA256: 918c08adb2a458678bda832c3e737da78c18d0265ef30613815be2f448442949 SHA512: 3f71a3ee6e618bca6da31f0334b2e82ef12ce951f9ec18459b90e00d5a5b2701718a05c92176227e2f84675394c6427fe26ced808ef6f259c625961451a61b50 Homepage: https://cran.r-project.org/package=ef Description: CRAN Package 'ef' (Modelling Framework for the Estimation of Salmonid Abundance) A set of functions to estimate capture probabilities and densities from multipass pass removal data. Package: r-cran-efafactors Architecture: arm64 Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1904 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmisc, r-cran-checkmate, r-cran-ddpcr, r-cran-ineq, r-cran-mass, r-cran-matrix, r-cran-mlr, r-cran-proxy, r-cran-psych, r-cran-ranger, r-cran-reticulate, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-simcormultres, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-efafactors_1.2.4-1.ca2604.1_arm64.deb Size: 1731850 MD5sum: 6a171966ca42f84438d8a1431761c405 SHA1: c36fc15cdb8824041dc329fd69575710305b5434 SHA256: d4e284b16eb2afee49d1090a57be3a788ee1dd5947250e0918b40cee38673b49 SHA512: a308c7ceca15868e3fc887597e743a6376b3012bcc9e60d049d0547f1c487d23bbbde32469720d1aa807cd773aeae046f8f2ce4874c04d0543a1a8afc863cdaa Homepage: https://cran.r-project.org/package=EFAfactors Description: CRAN Package 'EFAfactors' (Determining the Number of Factors in Exploratory Factor Analysis) Provides a collection of standard factor retention methods in Exploratory Factor Analysis (EFA), making it easier to determine the number of factors. Traditional methods such as the scree plot by Cattell (1966) , Kaiser-Guttman Criterion (KGC) by Guttman (1954) and Kaiser (1960) , and flexible Parallel Analysis (PA) by Horn (1965) based on eigenvalues form PCA or EFA are readily available. This package also implements several newer methods, such as the Empirical Kaiser Criterion (EKC) by Braeken and van Assen (2017) , Comparison Data (CD) by Ruscio and Roche (2012) , and Hull method by Lorenzo-Seva et al. (2011) , as well as some AI-based methods like Comparison Data Forest (CDF) by Goretzko and Ruscio (2024) and Factor Forest (FF) by Goretzko and Buhner (2020) . Additionally, it includes a deep neural network (DNN) trained on large-scale datasets that can efficiently and reliably determine the number of factors. Package: r-cran-efatools Architecture: arm64 Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1976 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1316968 MD5sum: cc9450ed15d769f86c646048b805af46 SHA1: 610a7b87d624e3b75acabdfbbd36da9c8e358343 SHA256: cf29dd5887458955107138aecb16987dd916bd9f832bfcae2b2925a8c941081a SHA512: 5756cbf82b8dcb8f592629d753088a180dec615c11ef96e6aacd3d089e9bf4a689cdfe75f510d6f11df8afad9b0d182a70ecb0e49ba01909b272957583dd5237 Homepage: https://cran.r-project.org/package=EFAtools Description: CRAN Package 'EFAtools' (Fast and Flexible Implementations of Exploratory Factor AnalysisTools) Provides functions to perform exploratory factor analysis (EFA) procedures and compare their solutions. The goal is to provide state-of-the-art factor retention methods and a high degree of flexibility in the EFA procedures. This way, for example, implementations from R 'psych' and 'SPSS' can be compared. Moreover, functions for Schmid-Leiman transformation and the computation of omegas are provided. To speed up the analyses, some of the iterative procedures, like principal axis factoring (PAF), are implemented in C++. Package: r-cran-efcm Architecture: arm64 Version: 1.0-1.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), 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_arm64.deb Size: 1895358 MD5sum: 03ac5729464d7bbf2c7b9ff44f2af713 SHA1: e2868e3a47d6d3c236aef0eef0610ed13d632c34 SHA256: 28a233550bebb0c927cfb4fa28e84738c0aa039bd5af0d72def4f89e14fada53 SHA512: f1faf560c2a711ea52c7f1f63e2e6d3bcef971b51d3fd39656c92c5720a546fd5ae78b85d7ddecde27ca5d80ea8d8c6e5c99cf34606af1e3c809c777dbdcfd85 Homepage: https://cran.r-project.org/package=eFCM Description: CRAN Package 'eFCM' (Exponential Factor Copula Model) Implements the exponential Factor Copula Model (eFCM) of Castro-Camilo, D. and Huser, R. (2020) for spatial extremes, with tools for dependence estimation, tail inference, and visualization. The package supports likelihood-based inference, Gaussian process modeling via Matérn covariance functions, and bootstrap uncertainty quantification. See Castro-Camilo and Huser (2020) . Package: r-cran-effectplots Architecture: arm64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 682 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-collapse, r-cran-ggplot2, r-cran-labeling, r-cran-patchwork, r-cran-plotly, r-cran-rcpp, r-cran-scales Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-effectplots_0.2.2-1.ca2604.1_arm64.deb Size: 246654 MD5sum: 84668108388974f49767c64422323531 SHA1: 3e935b5e6b6ffc2a556e12215936b3eed2152dbb SHA256: f91bff70551fd67241f770f8f15d3b1435da27db27a895e5b9c4e95ab5fe6de0 SHA512: 46cc602c83f40df0e9107d9034e879e7819c576a3bde21e065b68e14304549231e1e1eb7dd22cd8eec6a14dee46aa494473faa89eb0f05608f38a34967a3c7ee Homepage: https://cran.r-project.org/package=effectplots Description: CRAN Package 'effectplots' (Effect Plots) High-performance implementation of various effect plots useful for regression and probabilistic classification tasks. The package includes partial dependence plots (Friedman, 2021, ), accumulated local effect plots and M-plots (both from Apley and Zhu, 2016, ), as well as plots that describe the statistical associations between model response and features. It supports visualizations with either 'ggplot2' or 'plotly', and is compatible with most models, including 'Tidymodels', models wrapped in 'DALEX' explainers, or models with case weights. Package: r-cran-eganet Architecture: arm64 Version: 2.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4112 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-clue, r-cran-dendextend, r-cran-future, r-cran-future.apply, r-cran-ggally, r-cran-ggplot2, r-cran-ggpubr, r-cran-glasso, r-cran-glassofast, r-cran-gparotation, r-cran-igraph, r-cran-lavaan, r-cran-matrix, r-cran-network, r-cran-progressr, r-cran-qgraph, r-cran-semplot, r-cran-sna Suggests: r-cran-fitdistrplus, r-cran-gridextra, r-cran-pbapply, r-cran-progress, r-cran-psych, r-cran-pwr, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-eganet_2.4.1-1.ca2604.1_arm64.deb Size: 3828914 MD5sum: ba8285011d918137365fc734db89362b SHA1: cad44112e9b5effad46b470de4106a3a17411402 SHA256: e816071fcd7c17bfb714e4a8e6af8ddcc744980a19900fb5eed42cfafb293f47 SHA512: d0ae861accd8a41c0d0e13b84b302233170d5d7795e1fb98c2d8af908e742987a8b3c240ad5454439ad2c8280b84c4d8219553b3ea5ffad543a23c65e82049ad Homepage: https://cran.r-project.org/package=EGAnet Description: CRAN Package 'EGAnet' (Exploratory Graph Analysis – a Framework for Estimating theNumber of Dimensions in Multivariate Data using NetworkPsychometrics) Implements the Exploratory Graph Analysis (EGA) framework for dimensionality and psychometric assessment. EGA estimates the number of dimensions in psychological data using network estimation methods and community detection algorithms. A bootstrap method is provided to assess the stability of dimensions and items. Fit is evaluated using the Entropy Fit family of indices. Unique Variable Analysis evaluates the extent to which items are locally dependent (or redundant). Network loadings provide similar information to factor loadings and can be used to compute network scores. A bootstrap and permutation approach are available to assess configural and metric invariance. Hierarchical structures can be detected using Hierarchical EGA. Time series and intensive longitudinal data can be analyzed using Dynamic EGA, supporting individual, group, and population level assessments. Package: r-cran-eggcounts Architecture: arm64 Version: 2.5-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6229 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-boot, r-cran-coda, r-cran-numbers, r-cran-lattice, r-cran-rootsolve, r-cran-bh, r-cran-stanheaders, r-cran-rcppeigen Suggests: r-cran-r.rsp, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-eggcounts_2.5-1-1.ca2604.1_arm64.deb Size: 1467888 MD5sum: 78537fc7eee421a750f6446c71b54b18 SHA1: 754873f9b360d4eafcb2c390b3ebe68db9e6d35b SHA256: fb143c9b8cdb08527ebacabb8c6fe2d4b498907f9151292fa3fc7228b5a06c7b SHA512: 0c232d480109109c0eb6285980f52df7efc19136b15459c28bc45add5d510c4ca2e398cee76675dfb8a92dada1f12a2ab2522b64ab9a4204a9dbb6f08d4ecfb6 Homepage: https://cran.r-project.org/package=eggCounts Description: CRAN Package 'eggCounts' (Hierarchical Modelling of Faecal Egg Counts) An implementation of Bayesian hierarchical models for faecal egg count data to assess anthelmintic efficacy. Bayesian inference is done via MCMC sampling using 'Stan' . Package: r-cran-eglhmm Architecture: arm64 Version: 0.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 867 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbd, r-cran-nnet Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-eglhmm_0.1-3-1.ca2604.1_arm64.deb Size: 715036 MD5sum: af401c0e216636fcc5b68dd255f05a5f SHA1: 5918ba8d5c42b880a6dffa26532fb24112e36c03 SHA256: 6f70f9c3b1af9204856f1e149764e7f48d17aa08773b18c138c2c0fc40658ab5 SHA512: 03d8acdebe58b8c546e811c4c9e30f773b85a78b8da36c7d04b7e9e1f8c9599ecfb00ba34ebe53abb621f8ee8087fab8ce1f10e89c3885c0c0248ea0b0d8598d Homepage: https://cran.r-project.org/package=eglhmm Description: CRAN Package 'eglhmm' (Extended Generalised Linear Hidden Markov Models) Fits a variety of hidden Markov models, structured in an extended generalized linear model framework. See T. Rolf Turner, Murray A. Cameron, and Peter J. Thomson (1998) , and Rolf Turner (2008) and the references cited therein. 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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. Package: r-cran-eha Architecture: arm64 Version: 2.11.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4064 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/resolute/main/r-cran-eha_2.11.5-1.ca2604.1_arm64.deb Size: 2087918 MD5sum: cc768c9daab1efcd662d2e1616826332 SHA1: 1775fcbf3a9bbb5c41bb9baad6f456ea77b06ad1 SHA256: 957b1ccef05dce2f0d103025060bcbc26f0cd6ff836372c4221b806016439cf8 SHA512: f402c5afb10b93586898283b3a4133cf2cd0fdfaa193f06a13c5dd96e32f3d7e34193f375c64e8b4acb32dc9ccfdcb5af09da288694452e8eee5eccc6007c9b8 Homepage: https://cran.r-project.org/package=eha Description: CRAN Package 'eha' (Event History Analysis) Parametric proportional hazards fitting with left truncation and right censoring for common families of distributions, piecewise constant hazards, and discrete models. Parametric accelerated failure time models for left truncated and right censored data. Proportional hazards models for tabular and register data. Sampling of risk sets in Cox regression, selections in the Lexis diagram, bootstrapping. Broström (2022) . Package: r-cran-ehrmuse Architecture: arm64 Version: 0.0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl28 (>= 2.8+dfsg), libstdc++6 (>= 4.1.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-plotrix, r-cran-dplyr, r-cran-magrittr, r-cran-mass, r-cran-nleqslv, r-cran-xgboost, r-cran-survey, r-cran-nnet Filename: pool/dists/resolute/main/r-cran-ehrmuse_0.0.2.2-1.ca2604.1_arm64.deb Size: 177900 MD5sum: 655b5fd83ac50b66a9d74724d0dec4f7 SHA1: 223ac9be7578b081acf5f3c4da5283c2b6b6402b SHA256: 8e71d9d6c20bc6055977a6fc1d143803b2613f48919f4193e862f7f6a53fd641 SHA512: eaa61545080d5043b4629a7db10bcd384b55f092eec20d2ec5d36bfd548fd69c2746d00f2366757f8666417737ec0811dfe4d2c75dac52340bfad368d2228c73 Homepage: https://cran.r-project.org/package=EHRmuse Description: CRAN Package 'EHRmuse' (Multi-Cohort Selection Bias Correction using IPW and AIPWMethods) Comprehensive toolkit for addressing selection bias in binary disease models across diverse non-probability samples, each with unique selection mechanisms. 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Package: r-cran-eicm Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1421 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ga, r-cran-snow, r-cran-dosnow, r-cran-iterators, r-cran-pso, r-cran-ucminf, r-cran-foreach, r-cran-optimparallel Suggests: r-cran-igraph, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-eicm_1.0.3-1.ca2604.1_arm64.deb Size: 642030 MD5sum: b11a337a2535585433052b6b8aa93e40 SHA1: 8d64a2cb7b002910fa0420505af9b959226cdeaa SHA256: 6ca49fe83f0f8048ec73a5bfda79342636565be958e8abd55830cf2af22986f9 SHA512: ecf6ea266236b728cc24994f90d3e7add23d79fe25e36e94efe50ef12ab94ad97964673ce1181467a82910d83e4662ef8d2b8d98efd55c9856651c9e77d830e9 Homepage: https://cran.r-project.org/package=eicm Description: CRAN Package 'eicm' (Explicit Interaction Community Models) Model fitting and species biotic interaction network topology selection for explicit interaction community models. Explicit interaction community models are an extension of binomial linear models for joint modelling of species communities, that incorporate both the effects of species biotic interactions and the effects of missing covariates. Species interactions are modelled as direct effects of each species on each of the others, and are estimated alongside the effects of missing covariates, modelled as latent factors. The package includes a penalized maximum likelihood fitting function, and a genetic algorithm for selecting the most parsimonious species interaction network topology. Package: r-cran-eimpute Architecture: arm64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1473 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-rcppeigen Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-eimpute_0.2.4-1.ca2604.1_arm64.deb Size: 562280 MD5sum: 5f02ae62919e2a59426662fb213aebe9 SHA1: 5b11baf0c268b3edf3ce602155a2f6cf5c44a3ab SHA256: e43f88846cc4e54705776aff639ea96ffb486d9f685c803075c94b328fa6b84b SHA512: 8fb12103ccbabbf14f84bcb8115c9dbf521692697db8df4d72c56d0009267f8d3518fe49cf60d602264cda7c8ebfc104cd9528b1b4c25ac384a49e569f7dc205 Homepage: https://cran.r-project.org/package=eimpute Description: CRAN Package 'eimpute' (Efficiently Impute Large Scale Incomplete Matrix) Efficiently impute large scale matrix with missing values via its unbiased low-rank matrix approximation. Our main approach is Hard-Impute algorithm proposed in , which achieves highly computational advantage by truncated singular-value decomposition. Package: r-cran-einsum Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-glue, r-cran-mathjaxr Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-einsum_0.1.2-1.ca2604.1_arm64.deb Size: 62372 MD5sum: 813eee66edf7c21755082c34bafd14ac SHA1: eb3bb61eb20738aeae0a51c418468b9dd8c607d2 SHA256: a1d843c5245ffc336926d00ad978d9b58c606abed623f1ec943d65c883faea43 SHA512: e789bbdcb765f2a6c11729bb84868a4e176a1a40e26d32400a8866e19331321b504c522a92e08a88c384bf5735fd54c8989b95090c3218b73fea666ad5881f60 Homepage: https://cran.r-project.org/package=einsum Description: CRAN Package 'einsum' (Einstein Summation) The summation notation suggested by Einstein (1916) is a concise mathematical notation that implicitly sums over repeated indices of n-dimensional arrays. 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: arm64 Version: 0.2-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 331 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 236916 MD5sum: 079ff9926cb70ad336dc4f5dcd1dd60f SHA1: 021326527417089adc66693789207377c85878fa SHA256: 1f6946736dcb4fcb514882f28da27dd9247ca19f1be16ab035f815992fc76191 SHA512: 5e48576a8092f8db0d603c046d037e8f3cbe21f5309ef1ab84d7f3b37c122b158c36544ad187966c290d6108cff1854c75760a1af5fe91f19e8babe1e76bb435 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. Also provides tools for manipulating higher-dimension data objects. Package: r-cran-eive Architecture: arm64 Version: 3.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 62952 MD5sum: 1e5cdc2c7b10e12cea661ed59f50aa8a SHA1: a5b2ea888faa041723aefb10e50f2dd0396790fe SHA256: c428398eaf25d9ee3f00d39f18685ff694c133c0609f7b92799aa9e7c40ba3ee SHA512: 2340d2493f54583685ba532f1fa137642bf1fb142923f2f62dd05b7a1ad9f2282556e9eabd7739fde152bf278975f97b137b9b984cf94aa4465218c51cb930e5 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: arm64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-nleqslv, r-cran-ggplot2 Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-el_1.4-1.ca2604.1_arm64.deb Size: 114620 MD5sum: 7bf07e0bf800786b881ed030c4527e2c SHA1: e801b9367a23e05df1544e0ac4cf5cf28e122588 SHA256: b6982c53df6e7d7e4be5ffbc5e7f458200f29593856b2848f188002ea79410ce SHA512: 929031ef106c0d16092c4fe6f2b296da45652a0ecff1bd715f2b0aa94143aad692e6ce3fa9d6560ab959baf1d497bdbee5c0eaa64ee59685b5d13ac4cd30bb9d Homepage: https://cran.r-project.org/package=EL Description: CRAN Package 'EL' (Two-Sample Empirical Likelihood) Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (fdiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves. Also includes two-sample block-wise empirical likelihood (BEL) and a frequency-domain empirical likelihood test for autocorrelation differences (FDEL). Methods for EL, P-P, Q-Q, ROC, qdiff and fdiff are based on Valeinis and Cers (2011) . Package: r-cran-elcf4r Architecture: arm64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2333 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_arm64.deb Size: 779828 MD5sum: f06d3e58e4791da5fad6decd9f691aa3 SHA1: 56739cd096d5040bef5bd1dc83785a332c6edb06 SHA256: 84e6d194652680a5f456440dfe381dbb499b840f5ec6a13f9b721bae527d7797 SHA512: 7b8e8f1faba1ed21ee29ddd63ac729c22491f3479187c00e664b21e97f7bad25c0ed2cd9ffe72b52e666e0abf1bb42ae2288597c78440859c600ecca52db9700 Homepage: https://cran.r-project.org/package=elcf4R Description: CRAN Package 'elcf4R' (Electricity Load Curves Forecasting at Individual Level) Implements forecasting methods for individual electricity load curves, including Kernel Wavelet Functional (KWF), clustered KWF, Generalized Additive Models (GAM), Multivariate Adaptive Regression Splines (MARS), and Long Short-Term Memory (LSTM) models. Provides normalized dataset adapters for iFlex, StoreNet, Low Carbon London, and REFIT; download and read support for IDEAL and GX; explicit Python backend selection for TensorFlow-based LSTM fits; helpers for daily segmentation and rolling-origin benchmarking; and compact shipped example panels and benchmark-result datasets. Package: r-cran-elfdistr Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 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_arm64.deb Size: 51818 MD5sum: c23cb15b22a7e2f66997d7f9b97d06cf SHA1: 0da894715f72ec1ab9aa49b97ef2bcf13f3d6402 SHA256: 74ec3b8c1f7b19504080ae379b42e495de0bc148bb9f18dd79e69b2320c98d91 SHA512: b055d76fd1bd564a7503a582c7fae8fc66c632e806193b7e226ffff14000ea44e946937a8722fd2b123c8f9702ce874b5add580e458fc660cdaae2f819654060 Homepage: https://cran.r-project.org/package=elfDistr Description: CRAN Package 'elfDistr' (Kumaraswamy Complementary Weibull Geometric (Kw-CWG) ProbabilityDistribution) Density, distribution function, quantile function and random generation for the Kumaraswamy Complementary Weibull Geometric (Kw-CWG) lifetime probability distribution proposed in Afify, A.Z. et al (2017) . Package: r-cran-elgbd Architecture: arm64 Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 472 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_arm64.deb Size: 193120 MD5sum: 16786bd7533935c931cd6867055f7844 SHA1: 5104ad24bb8e45529025bee40c8802db42480930 SHA256: 47014849a81a59fedcab759d43262818ac59041729417019bbea199a7d3b57a6 SHA512: 20cd970c5dbc8a19adae78f4c2e1a9ede74a5689e40f23b9baa721d5cd6f1d6b87679749720bc2ea04931f0d7dc0898718e76fb3d5b2cdef25d7faaf73e33746 Homepage: https://cran.r-project.org/package=elgbd Description: CRAN Package 'elgbd' (Empirical Likelihood for General Block Designs) Performs hypothesis testing for general block designs with empirical likelihood. The core computational routines are implemented using the 'Eigen' 'C++' library and 'RcppEigen' interface, with 'OpenMP' for parallel computation. Details of the methods are given in Kim, MacEachern, and Peruggia (2023) . This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552. Package: r-cran-ellipsis Architecture: arm64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ellipsis_0.3.2-1.ca2604.1_arm64.deb Size: 35344 MD5sum: 3b8607453699dec679468387b901d9a2 SHA1: 7aa0e5004bdc0bf1e651371703585e20f5e1234e SHA256: 550e16c6054b7fb1bafc024de2d6e8bc65039f3e9abec8bb58b266be15946177 SHA512: 7634b724f4d34b262775c360ffbf04e07f1e9bf72fbc73221dfcfb6a754ba53655309ab0b9dff35c252abc216410e719fafb52dbb9e3be0ceb9c38e99978c7f2 Homepage: https://cran.r-project.org/package=ellipsis Description: CRAN Package 'ellipsis' (Tools for Working with ...) The ellipsis is a powerful tool for extending functions. Unfortunately this power comes at a cost: misspelled arguments will be silently ignored. The ellipsis package provides a collection of functions to catch problems and alert the user. Package: r-cran-elmnnrcpp Architecture: arm64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 811 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-kernelknn, 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-elmnnrcpp_1.0.5-1.ca2604.1_arm64.deb Size: 494872 MD5sum: a8213855f81c4cdab921d4cd3e43bedc SHA1: 182ce66f414ea79619606a65d802d2939bdb2017 SHA256: b7233ccd97d12cd40f1fdc3c8cf82ad4edce18f8642b9453a2985485a8074cb9 SHA512: 94c641043d01ba92c0ff89fbd6b04c2a2373aca6c40b715c4a9e93249bd176d2ba9d08a55b43c129a129f2a2a763c296750adda9650b4969d3743287ac119a23 Homepage: https://cran.r-project.org/package=elmNNRcpp Description: CRAN Package 'elmNNRcpp' (The Extreme Learning Machine Algorithm) Training and predict functions for Single Hidden-layer Feedforward Neural Networks (SLFN) using the Extreme Learning Machine (ELM) algorithm. The ELM algorithm differs from the traditional gradient-based algorithms for very short training times (it doesn't need any iterative tuning, this makes learning time very fast) and there is no need to set any other parameters like learning rate, momentum, epochs, etc. This is a reimplementation of the 'elmNN' package using 'RcppArmadillo' after the 'elmNN' package was archived. For more information, see "Extreme learning machine: Theory and applications" by Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew (2006), Elsevier B.V, . Package: r-cran-elo Architecture: arm64 Version: 3.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 596 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_arm64.deb Size: 264354 MD5sum: 19e64eb70b7d4bb9f41927e98750d89f SHA1: ae3118cf83e6e46ac671ff5ec277b06e11346e77 SHA256: d8a1e38dad0c38598f47da8424e3f29d3edbfac0885c3686d9fcf3410eea4f99 SHA512: 06438d9beaf790b1a7c1e97d41761ec9e649b999df55fa6896a840dccf09826335bd0244708939db5e03ff7ca97d81d7fcb5a6b2493a8d088f09a461bf8b2924 Homepage: https://cran.r-project.org/package=elo Description: CRAN Package 'elo' (Ranking Teams by Elo Rating and Comparable Methods) A flexible framework for calculating Elo ratings and resulting rankings of any two-team-per-matchup system (chess, sports leagues, 'Go', etc.). 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Package: r-cran-elochoice Architecture: arm64 Version: 0.29.4-1.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-rcpp, r-cran-psychotools, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-xtable, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-elochoice_0.29.4-1.ca2604.1_arm64.deb Size: 223874 MD5sum: 91ae2736ed5e710d9ba8d6159405f241 SHA1: b7b0557bed30d58ac6402dddf294cffc2c6df530 SHA256: a5a8fd38bd99404a10d1be24549912debb5f132e06b72d4f933a6b6aa22d0ee4 SHA512: 28cfecb0f91d0f95c0edf0c88915b9ca0869e022c2a262dcbcff527a9b262dca3cd2a08caf5b8acf748de5a6c65f67997316c90a6dddd994d1273dd8ed1d5a4a Homepage: https://cran.r-project.org/package=EloChoice Description: CRAN Package 'EloChoice' (Preference Rating for Visual Stimuli Based on Elo Ratings) Allows calculating global scores for characteristics of visual stimuli as assessed by human raters. Stimuli are presented as sequence of pairwise comparisons ('contests'), during each of which a rater expresses preference for one stimulus over the other (forced choice). The algorithm for calculating global scores is based on Elo rating, which updates individual scores after each single pairwise contest. Elo rating is widely used to rank chess players according to their performance. Its core feature is that dyadic contests with expected outcomes lead to smaller changes of participants' scores than outcomes that were unexpected. As such, Elo rating is an efficient tool to rate individual stimuli when a large number of such stimuli are paired against each other in the context of experiments where the goal is to rank stimuli according to some characteristic of interest. Clark et al (2018) provide details. Package: r-cran-elorating Architecture: arm64 Version: 0.46.18-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1250 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-sna, r-cran-network, r-cran-rcpp, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-anidom, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-elorating_0.46.18-1.ca2604.1_arm64.deb Size: 914960 MD5sum: 96b4034519084c24d1168de58af0a4e7 SHA1: 675cea3f065bd8b0054698c53af0bf2de54eb511 SHA256: 6952bbebdece5d6a37df7113afd2340e46e10e37f26af49f5ddc8b68e6fdd746 SHA512: 2d4070e16d9f74164cd70ae171fe23a21ce60ed0672583415d933eac7ca9cde1b85140f12100d95bc41a6fabaa4acf7d9fb938edd1018648e55b6a79f0d3ca45 Homepage: https://cran.r-project.org/package=EloRating Description: CRAN Package 'EloRating' (Animal Dominance Hierarchies by Elo Rating) Provides functions to quantify animal dominance hierarchies. The major focus is on Elo rating and its ability to deal with temporal dynamics in dominance interaction sequences. For static data, David's score and de Vries' I&SI are also implemented. In addition, the package provides functions to assess transitivity, linearity and stability of dominance networks. See Neumann et al (2011) for an introduction. Package: r-cran-elosteepness Architecture: arm64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4468 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-elorating, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-anidom, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rmarkdown, r-cran-bookdown, r-cran-xtable, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-elosteepness_0.5.0-1.ca2604.1_arm64.deb Size: 1786190 MD5sum: 2908008ad2892b8403adc6ebc50cae51 SHA1: 953680d93b1aafba5e7ffbfaa469958f7d90729b SHA256: 89052d5badb27c678d3c17be3c467ead736f7b985737d8fbb3e0c952bf5993be SHA512: d177b585d96f8aa98781a81472bc0804e8bb5d56649b4dfb43a8f906ebdde7109a2e752bff6ecf66c1e7af3f7fb117b80692783f327830e9f43276f8f008a52f Homepage: https://cran.r-project.org/package=EloSteepness Description: CRAN Package 'EloSteepness' (Bayesian Dominance Hierarchy Steepness via Elo Rating andDavid's Scores) Obtain Bayesian posterior distributions of dominance hierarchy steepness (Neumann and Fischer (2023) ). Steepness estimation is based on Bayesian implementations of either Elo-rating or David's scores. Package: r-cran-elrm Architecture: arm64 Version: 1.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda Filename: pool/dists/resolute/main/r-cran-elrm_1.2.6-1.ca2604.1_arm64.deb Size: 313826 MD5sum: bb663a92fc98f8556b87194b1285e2d9 SHA1: d228a01f859f5a1a69b63ee306f92bc11030e3a5 SHA256: f900d75564efe2aad089c1b3ff63f2c63ca50123b232a214b8740c418a539384 SHA512: 4e882bbbe91bbd17bede540809b6477ee18eec28ce44e73a629dfcdc94f569dc0c0655a00a5b309ed3b8a2de69af3c49cd2c30c392a2e0550b553b7d764e097a Homepage: https://cran.r-project.org/package=elrm Description: CRAN Package 'elrm' (Exact Logistic Regression via MCMC) Implements a Markov Chain Monte Carlo algorithm to approximate exact conditional inference for logistic regression models. Exact conditional inference is based on the distribution of the sufficient statistics for the parameters of interest given the sufficient statistics for the remaining nuisance parameters. Using model formula notation, users specify a logistic model and model terms of interest for exact inference. See Zamar et al. (2007) for more details. 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In addition, this package offers other methods to measure local indicators of spatial associations (LISA). Furthermore, global spatial structure can be measured using a variogram-like diagram, called entrogram. For more information, please check that paper: Naimi, B., Hamm, N. A., Groen, T. A., Skidmore, A. K., Toxopeus, A. G., & Alibakhshi, S. (2019) . Package: r-cran-elyp Architecture: arm64 Version: 0.7-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 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_arm64.deb Size: 150534 MD5sum: e2b1ec5d317304cc1352aa067bf9985b SHA1: d480515ecc1cb179783281178c61eb12ad609ecb SHA256: 4cf10bc986a7075778a775cd91880f13ae61f7f78a1c84b45ba91547fdf13495 SHA512: e5d27f2a5d5231e72cb90cd0a6d9f87d33a400fd1b61ceff87b3f6d0d3002b67a5076c151f2d9b2851afdcb8bc616b657ec132a5a88642b47d10c508fda80fef 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: arm64 Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 442 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 231802 MD5sum: e2313465bed2151a487386a20ece6cf1 SHA1: 3cb85b655432994e0e29327f988bb2ddefd2923e SHA256: a9aa45b15363ac3fddfb570f7c54f9f30265a89ab04eaccd8d292e92b6564e54 SHA512: 51ae7a7c8f8230d2585b3abc9de2dc9d6b565cf72ddd7bab3e6bcea0088bd38ad602408d3b5581b9cfb4e070d8b7780ca949154c6b34d86bb80ec8fac4921175 Homepage: https://cran.r-project.org/package=emBayes Description: CRAN Package 'emBayes' (Robust Bayesian Variable Selection via Expectation-Maximization) Variable selection methods have been extensively developed for analyzing highdimensional omics data within both the frequentist and Bayesian frameworks. This package provides implementations of the spike-and-slab quantile (group) LASSO which have been developed along the line of Bayesian hierarchical models but deeply rooted in frequentist regularization methods by utilizing Expectation–Maximization (EM) algorithm. The spike-and-slab quantile LASSO can handle data irregularity in terms of skewness and outliers in response variables, compared to its non-robust alternative, the spike-and-slab LASSO, which has also been implemented in the package. In addition, procedures for fitting the spike-and-slab quantile group LASSO and its non-robust counterpart have been implemented in the form of quantile/least-square varying coefficient mixed effect models for high-dimensional longitudinal data. The core module of this package is developed in 'C++'. Package: r-cran-embc Architecture: arm64 Version: 2.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1204 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_arm64.deb Size: 920352 MD5sum: 69eae8b09de03378892a71d189354c15 SHA1: 3933dcba23151d6f442931720dedc6618fe7233b SHA256: f6bd6abdccd8c09dbd6a32a42a6c93137aa19530050f67f3e528a2029f963bee SHA512: cc1a0cbadfccdd814fdc99c27ec92aa515cb2f3466a86d77b5e19917598795ffeacea33fd0be75539c05d3042b31cbf12233a5ba5278c6f5dff7d7e6f2578463 Homepage: https://cran.r-project.org/package=EMbC Description: CRAN Package 'EMbC' (Expectation-Maximization Binary Clustering) Unsupervised, multivariate, binary clustering for meaningful annotation of data, taking into account the uncertainty in the data. A specific constructor for trajectory analysis in movement ecology yields behavioural annotation of trajectories based on estimated local measures of velocity and turning angle, eventually with solar position covariate as a daytime indicator, ("Expectation-Maximization Binary Clustering for Behavioural Annotation"). Package: r-cran-embedsom Architecture: arm64 Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 686 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_arm64.deb Size: 355732 MD5sum: 934f3647cdbac3726976b2993f924416 SHA1: 22111489f14079894aaa5482ff4e5324ae5f30d2 SHA256: a1aa01de6b20e83b28899896d921fbbdee2b31cd691b75829b0493d910e16808 SHA512: cbdeb3a50453dcd2e203ab14684cf513021ebbd8b5e6c6ec697748aa2a6068e935229e1e886e1303ed7ab545e3a9f162f71dd124d547c1527548014b822be92f Homepage: https://cran.r-project.org/package=EmbedSOM Description: CRAN Package 'EmbedSOM' (Fast Embedding Guided by Self-Organizing Map) Provides a smooth mapping of multidimensional points into low-dimensional space defined by a self-organizing map. Designed to work with 'FlowSOM' and flow-cytometry use-cases. See Kratochvil et al. (2019) . Package: r-cran-emc2 Architecture: arm64 Version: 3.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6350 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-cran-coda, r-cran-magic, r-cran-mass, r-cran-matrixcalc, r-cran-msm, r-cran-mvtnorm, r-cran-matrix, r-cran-rcpp, r-cran-brobdingnag, r-cran-corrplot, r-cran-colorspace, r-cran-psych, r-cran-lpsolve, r-cran-wienr, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-diagrammer Filename: pool/dists/resolute/main/r-cran-emc2_3.4.1-1.ca2604.1_arm64.deb Size: 3891766 MD5sum: 8b4dc33b56b8199dce14175962613081 SHA1: 882a5e5d6b5f17e68fd1641a23ba50ee1fc62fd6 SHA256: fb58779fa33005d89bd079d533b260cb201ab5572018099e0f7df6e4c1a5c19e SHA512: c2a4d515067b3c185639f8318ecb554b6cda25a88fea0d3098cdaa7ee5bffb5b2b6af91d4a9cb449cf0f4591d06305b934a113ace6427edef2a91bc58403adef Homepage: https://cran.r-project.org/package=EMC2 Description: CRAN Package 'EMC2' (Bayesian Hierarchical Analysis of Cognitive Models of Choice) Fit Bayesian (hierarchical) cognitive models using a linear modeling language interface using particle Metropolis Markov chain Monte Carlo sampling with Gibbs steps. The diffusion decision model (DDM), linear ballistic accumulator model (LBA), racing diffusion model (RDM), and the lognormal race model (LNR) are supported. Additionally, users can specify their own likelihood function and/or choose for non-hierarchical estimation, as well as for a diagonal, blocked or full multivariate normal group-level distribution to test individual differences. Prior specification is facilitated through methods that visualize the (implied) prior. A wide range of plotting functions assist in assessing model convergence and posterior inference. Models can be easily evaluated using functions that plot posterior predictions or using relative model comparison metrics such as information criteria or Bayes factors. References: Stevenson et al. (2024) . Package: r-cran-emcadr Architecture: arm64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1160 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ggplot2, r-cran-dplyr, r-cran-umap, r-cran-dbscan, r-cran-logistf, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-emcadr_1.3-1.ca2604.1_arm64.deb Size: 779228 MD5sum: 99d7e8a501a2b476c10564cf54621f75 SHA1: eb311dc9c4bc4a565e4895a18a11ff2645c63ccc SHA256: 01be1f732768be47a252ee6482e78e3e9095d4bfe03b308dfcec2d9092c7d768 SHA512: 0c8a05e72e22ba3d55acd01cf781e495abae6a9283d7c1507295164b735bbbae2c621b195b2c011bd6e97982938c48117b341672d8ea75d8aa55d8785b2d0ffc Homepage: https://cran.r-project.org/package=emcAdr Description: CRAN Package 'emcAdr' (Evolutionary Version of the Metropolis-Hastings Algorithm) Provides computational methods for detecting adverse high-order drug interactions from individual case safety reports using statistical techniques, allowing the exploration of higher-order interactions among drug cocktails. Package: r-cran-emcluster Architecture: arm64 Version: 0.2-17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1064 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_arm64.deb Size: 828958 MD5sum: bf216b0440e914fe98101b20cce3646b SHA1: a70a546f107ffffad961b3a3f6266f26320afb99 SHA256: 74b3bf30fa7550f0f6c74535ef871da008d1d8e4fe4776f1f75f5ef7379ca17c SHA512: ef85dc144f80d9dbe26e254744a9928fa337eb23d68b4138de3d8561ea6945b57d8d1984129bf47f895220a0b6251f00ca216659601d6923ebc9ebf65a5937a8 Homepage: https://cran.r-project.org/package=EMCluster Description: CRAN Package 'EMCluster' (EM Algorithm for Model-Based Clustering of Finite MixtureGaussian Distribution) EM algorithms and several efficient initialization methods for model-based clustering of finite mixture Gaussian distribution with unstructured dispersion in both of unsupervised and semi-supervised learning. Package: r-cran-emd Architecture: arm64 Version: 1.5.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 388274 MD5sum: c2237ca44687f2b25611c9ed2ead48ef SHA1: a8bdd3278fbfa9b7401a6e703e4beaa353aade0a SHA256: 2da23b5ef8493f089fcbe3fed73fc9ab0b8899334578bd2d12946114627836af SHA512: 6466526c3ec1754a9a0bd9160fa0731018590f7740cbe219f753e323b657e012eadc65361573a2374e41aa450c3ea773aa68419b386204e9285d787622212fe3 Homepage: https://cran.r-project.org/package=EMD Description: CRAN Package 'EMD' (Empirical Mode Decomposition and Hilbert Spectral Analysis) For multiscale analysis, this package carries out empirical mode decomposition and Hilbert spectral analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46). Package: r-cran-emdist Architecture: arm64 Version: 0.3-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-emdist_0.3-3-1.ca2604.1_arm64.deb Size: 25108 MD5sum: 3ce9ba3a829671365a17cb2c83918f44 SHA1: 1d12cb7efea0625702e89b72d4422650582f2146 SHA256: eeb0e89c1ff4f86674dc84f9dc5beafa484833c0299b110a65ef67047fd18fd0 SHA512: 650b9fc6050f5b0b70dadd68ea238cba1281c1d7f62c1f33d2bf72efd7c2337610d2b575a8b65a4c92001e0453bee4d4b02d018b85e67dc0a41e1cf1a4ad1510 Homepage: https://cran.r-project.org/package=emdist Description: CRAN Package 'emdist' (Earth Mover's Distance) Package providing calculation of Earth Mover's Distance (EMD). Package: r-cran-emgaussian Architecture: arm64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrixcalc, r-cran-matrix, r-cran-lavaan, r-cran-glasso, r-cran-glassofast, r-cran-caret, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-psych, r-cran-bootnet, r-cran-qgraph, r-cran-cglasso Filename: pool/dists/resolute/main/r-cran-emgaussian_0.2.2-1.ca2604.1_arm64.deb Size: 114460 MD5sum: ce2a70a41446229e5ed193f7bacff544 SHA1: 9b53f36cf8081f250dfe198e829cf7812213c7a0 SHA256: 0cda34e9d9135ecc92a7ea5b93dd1d29bce98c0bb0eed2c9015b7f50a0f9183a SHA512: 6f017a5f58259d9a531d515715b4fede34497d203d5129138b9501fec07e1e39d9626ce197635a9dba7b11fca13f31fe7d40abb436fb00084b01c19b95d0233e Homepage: https://cran.r-project.org/package=EMgaussian Description: CRAN Package 'EMgaussian' (Expectation-Maximization Algorithm for Multivariate Normal(Gaussian) with Missing Data) Initially designed to distribute code for estimating the Gaussian graphical model with Lasso regularization, also known as the graphical lasso (glasso), using an Expectation-Maximization (EM) algorithm based on work by Städler and Bühlmann (2012) . As a byproduct, code for estimating means and covariances (or the precision matrix) under a multivariate normal (Gaussian) distribution is also available. Package: r-cran-emir Architecture: arm64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1609 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_arm64.deb Size: 652504 MD5sum: b0ebbf27f678efd3b18efbdfb167a709 SHA1: a66a41bdb49263f5ddaaf8963ea813cf71098a86 SHA256: a4cbf4ba232de0d5cdd976e17620c1f65f52265a4ad327e12e0a3a52917369f8 SHA512: 9960df92d26c63bd2c6ffe233212e0308fd00ae4f544e04e466f0d0b51333e20944e0aa44cafe44f849c9b54d75b9763dde0c3adcb2fab9cde2531a9b10a8c13 Homepage: https://cran.r-project.org/package=EmiR Description: CRAN Package 'EmiR' (Evolutionary Minimizer for R) A C++ implementation of the following evolutionary algorithms: Bat Algorithm (Yang, 2010 ), Cuckoo Search (Yang, 2009 ), Genetic Algorithms (Holland, 1992, ISBN:978-0262581110), Gravitational Search Algorithm (Rashedi et al., 2009 ), Grey Wolf Optimization (Mirjalili et al., 2014 ), Harmony Search (Geem et al., 2001 ), Improved Harmony Search (Mahdavi et al., 2007 ), Moth-flame Optimization (Mirjalili, 2015 ), Particle Swarm Optimization (Kennedy et al., 2001 ISBN:1558605959), Simulated Annealing (Kirkpatrick et al., 1983 ), Whale Optimization Algorithm (Mirjalili and Lewis, 2016 ). 'EmiR' can be used not only for unconstrained optimization problems, but also in presence of inequality constrains, and variables restricted to be integers. Package: r-cran-emirt Architecture: arm64 Version: 0.0.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2867 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 2439606 MD5sum: 7a638590a4107cc87ba0f02acbe0b18d SHA1: 416f91d8997650a8c6eca838dc6c1863082a28fa SHA256: c66cad5b2c305f163512cd872a245391f19f1717b9d0faf9ad53359d95b051ab SHA512: 807d81db54e5b6445bb995e4b167b72365b1cb0db40302afcf753247b30345ac816e082bcb198094a937660e13b8839cf044d671a499d0f61020c6d7ac2ba0ca Homepage: https://cran.r-project.org/package=emIRT Description: CRAN Package 'emIRT' (EM Algorithms for Estimating Item Response Theory Models) Various Expectation-Maximization (EM) algorithms are implemented for item response theory (IRT) models. The package includes IRT models for binary and ordinal responses, along with dynamic and hierarchical IRT models with binary responses. The latter two models are fitted using variational EM. The package also includes variational network and text scaling models. The algorithms are described in Imai, Lo, and Olmsted (2016) . Package: r-cran-emmixgene Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2486 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_arm64.deb Size: 2280420 MD5sum: 284ec86799bbebd54cff744a190b0287 SHA1: 61e936876136b86011c826a9ea2772015e039f46 SHA256: 172e0d7fd35b2c7d5b10fff3618d5e197d88c51ca525b09d486cb2396a4d4f2c SHA512: 60cf6aed0d1699c7803fe54d994b4f6b22f104217e1f47fef30fe31460249fa6ad369244056a76482aefbc300ebdb312c1a3c52d2e183ab8248ac7a634d86791 Homepage: https://cran.r-project.org/package=EMMIXgene Description: CRAN Package 'EMMIXgene' (A Mixture Model-Based Approach to the Clustering of MicroarrayExpression Data) Provides unsupervised selection and clustering of microarray data using mixture models. Following the methods described in McLachlan, Bean and Peel (2002) a subset of genes are selected based one the likelihood ratio statistic for the test of one versus two components when fitting mixtures of t-distributions to the expression data for each gene. The dimensionality of this gene subset is further reduced through the use of mixtures of factor analyzers, allowing the tissue samples to be clustered by fitting mixtures of normal distributions. Package: r-cran-emmixmfa Architecture: arm64 Version: 2.0.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 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_arm64.deb Size: 217852 MD5sum: 37a0318064c9bf8be3a6e53399f24183 SHA1: c16a0e2ef6d7ae43077f215cebc41ea480d2c0a7 SHA256: 076c2a9ed3eda206caa5f22f3e94df1dc6640b13a5b55736ce85b3f25614b0fa SHA512: 5679a7473eeb8469cf3087518a81af6b433385c7c12e2fa71b5dc476031a147498c76bcbe94b14fc90e0346804b918a183ce93c745715638a9b8b4d75f47128e Homepage: https://cran.r-project.org/package=EMMIXmfa Description: CRAN Package 'EMMIXmfa' (Mixture Models with Component-Wise Factor Analyzers) We provide functions to fit finite mixtures of multivariate normal or t-distributions to data with various factor analytic structures adopted for the covariance/scale matrices. The factor analytic structures available include mixtures of factor analyzers and mixtures of common factor analyzers. The latter approach is so termed because the matrix of factor loadings is common to components before the component-specific rotation of the component factors to make them white noise. Note that the component-factor loadings are not common after this rotation. Maximum likelihood estimators of model parameters are obtained via the Expectation-Maximization algorithm. See descriptions of the algorithms used in McLachlan GJ, Peel D (2000) McLachlan GJ, Peel D (2000) McLachlan GJ, Peel D, Bean RW (2003) McLachlan GJ, Bean RW, Ben-Tovim Jones L (2007) Baek J, McLachlan GJ, Flack LK (2010) Baek J, McLachlan GJ (2011) McLachlan GJ, Baek J, Rathnayake SI (2011) . Package: r-cran-emoa Architecture: arm64 Version: 0.5-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 280 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-emoa_0.5-3-1.ca2604.1_arm64.deb Size: 138696 MD5sum: 06acd07becf99ea801cfb3ec05bbf24e SHA1: dc5af2ce422c5cb69347f780354f1ecb786b081d SHA256: 8a406ceef0ee681fcf31846dbbb75687dc0c258ab52edc33ac3abb2582331386 SHA512: 864d777fc88a4269d8fe15d9f0c046e5d18a5949469ceb98015f8728ed13194b48f3a573c99c859f25f49d975e1cc3411e7919918f4f90ac221e41594fc59302 Homepage: https://cran.r-project.org/package=emoa Description: CRAN Package 'emoa' (Evolutionary Multiobjective Optimization Algorithms) Collection of building blocks for the design and analysis of evolutionary multiobjective optimization algorithms. Package: r-cran-empichar Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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-spelling, r-cran-covr Filename: pool/dists/resolute/main/r-cran-empichar_1.0.1-1.ca2604.1_arm64.deb Size: 51984 MD5sum: 4bb6a1f62fb9c82d2798551ecb282907 SHA1: d76799d0ea3471979239644cccdbff7dacbfef7c SHA256: 0c36ea0d9ee22627754970068a8712acd200e3a0f9594bc8254db13ec8d99e12 SHA512: 9d4e6501588740b25eef2b8cb62de540db9b94d41b4580429599a87778c2e5a460f07fbfa1e143e72826cea9ed4d9bf32a983bc2c058c81f1afaa978fe36432c Homepage: https://cran.r-project.org/package=empichar Description: CRAN Package 'empichar' (Evaluates the Empirical Characteristic Function for MultivariateSamples) Evaluates the empirical characteristic function of univariate and multivariate samples. This package uses 'RcppArmadillo' for fast evaluation. It is also possible to export the code to be used in other packages at 'C++' level. 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By using a set of negative control hypotheses we can estimate the empirical null distribution of a particular observational study setup. This empirical null distribution can be used to compute a calibrated p-value, which reflects the probability of observing an estimated effect size when the null hypothesis is true taking both random and systematic error into account. A similar approach can be used to calibrate confidence intervals, using both negative and positive controls. For more details, see Schuemie et al. (2013) and Schuemie et al. (2018) . 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In particular, the empirical likelihood for the Kaplan-Meier/Nelson-Aalen estimator. Now does AFT regression too. 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Package: r-cran-enderecobr Architecture: arm64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2417 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-rlang, r-cran-stringr, r-cran-tibble Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-enderecobr_0.5.0-1.ca2604.1_arm64.deb Size: 849204 MD5sum: e36037dc749fb643866b0193bc27a760 SHA1: a98ba70feb252a1ebf03f9ae987fe36631343dc1 SHA256: cb1b1f1652cd9fa763f5169e1d788b883be96caa44b524ff3127e07604f83158 SHA512: 458a4b69ef4de0250e900f1a40adee289efc391ffa97afd920064c2927ae913f3ebe29eb49f70173417672129182dddb3dd8dd89d665b4113b798b66a1b43651 Homepage: https://cran.r-project.org/package=enderecobr Description: CRAN Package 'enderecobr' (Padronizador de Endereços Brasileiros (Brazilian AddressesStandardizer)) Padroniza endereços brasileiros a partir de diferentes critérios. Os métodos de padronização incluem apenas manipulações básicas de strings, não oferecendo suporte a correspondências probabilísticas entre strings. (Standardizes brazilian addresses using different criteria. Standardization methods include only basic string manipulation, not supporting probabilistic matches between strings.) Package: r-cran-endogeneity Architecture: arm64 Version: 2.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 419 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-pbivnorm, r-cran-maxlik, r-cran-statmod, r-cran-mass, r-cran-data.table, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-endogeneity_2.1.5-1.ca2604.1_arm64.deb Size: 253918 MD5sum: 409ee63d8045d372d39798fccd9d97f4 SHA1: 58c91f885ffce9f1fa3d17ef473b260953608e5e SHA256: 33a7c8f4703b175e3ef3b7c030da7b89b690024b06a4194846e9e9f130664f13 SHA512: db47bcc0344ce6edcda24c8eefb070c1b270f8b5e8d086616c52795bfe21deb8380c76adee726ceaf2f76f2e78844a84c0b4387004db4d1420a9a2af4babdea0 Homepage: https://cran.r-project.org/package=endogeneity Description: CRAN Package 'endogeneity' (Recursive Two-Stage Models to Address Endogeneity) Various recursive two-stage models to address the endogeneity issue of treatment variables in observational study or mediators in experiments. The details of the models are discussed in Peng (2023) . Package: r-cran-endorse Architecture: arm64 Version: 1.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda Filename: pool/dists/resolute/main/r-cran-endorse_1.6.2-1.ca2604.1_arm64.deb Size: 172912 MD5sum: 5605f9826c559863d9fafcca8fb0e60e SHA1: 680c86eb8c7c408079a40e009e44a932677fb3c6 SHA256: 5a5e5b922643f06d3eac7f732a265344253cc16c565761ad86091a8a1e858446 SHA512: e67a97121fdf17f000d8e3a149e1fe8f017bfcc0a4d5484e389b9c2b7551fe711ceb2fb0ac051100272bd32684faebb5ee09daaa67106a5823719a09a533c0cc Homepage: https://cran.r-project.org/package=endorse Description: CRAN Package 'endorse' (Bayesian Measurement Models for Analyzing EndorsementExperiments) Fit the hierarchical and non-hierarchical Bayesian measurement models proposed by Bullock, Imai, and Shapiro (2011) to analyze endorsement experiments. Endorsement experiments are a survey methodology for eliciting truthful responses to sensitive questions. This methodology is helpful when measuring support for socially sensitive political actors such as militant groups. The model is fitted with a Markov chain Monte Carlo algorithm and produces the output containing draws from the posterior distribution. Package: r-cran-energy Architecture: arm64 Version: 1.7-12-1.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-rcpp, r-cran-boot, r-cran-gsl Suggests: r-cran-mass, r-cran-compquadform, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-energy_1.7-12-1.ca2604.1_arm64.deb Size: 297234 MD5sum: 481c7046e29be537316b552771c723d9 SHA1: defe0d23f2670623c2100082de5d60528f8c2396 SHA256: aa2a3c960ab5d74aa51baf1a126e3b36f32adcea1528c294c43a6912e9327fe9 SHA512: 988406acc852bb7e1be28600423cd04e91d6dd4bf5b0a2f1b0f2232864bf0c7a54ad79b20f67e97480cf5572d481774b3ff585454f8d56d036d7525a8283304d Homepage: https://cran.r-project.org/package=energy Description: CRAN Package 'energy' (E-Statistics: Multivariate Inference via the Energy of Data) E-statistics (energy) tests and statistics for multivariate and univariate inference, including distance correlation, one-sample, two-sample, and multi-sample tests for comparing multivariate distributions, are implemented. Measuring and testing multivariate independence based on distance correlation, partial distance correlation, multivariate goodness-of-fit tests, k-groups and hierarchical clustering based on energy distance, testing for multivariate normality, distance components (disco) for non-parametric analysis of structured data, and other energy statistics/methods are implemented. Package: r-cran-energymethod Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-energymethod_1.1-1.ca2604.1_arm64.deb Size: 73568 MD5sum: 0b19f7a0418dfd3c4f267de99e4a3e8a SHA1: c769c8e2bb5d6b1de6529c4366220a88afa330b2 SHA256: abd51c2e47ec1005d1e1911ceb1f89984accde7c5d809c4d1313591b1ed45da5 SHA512: ec56a78b477550e1a07111595b04ff36c116e78f4b483e2710276daa0484fae2b94b5603c5a646c52a4af509f5d139efca03a2e358e37cc85b36177d1d590138 Homepage: https://cran.r-project.org/package=energymethod Description: CRAN Package 'energymethod' (Two-Sample Test of many Functional Means using the Energy Method) Given two samples of size n_1 and n_2 from a data set where each sample consists of K functional observations (channels), each recorded on T grid points, the function energy method implements a hypothesis test of equality of channel-wise mean at each channel using the bootstrapped distribution of maximum energy to control family wise error. The function energy_method_complex accomodates complex valued functional observations. Package: r-cran-enerscape Architecture: arm64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1460 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-terra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-enerscape_1.2.0-1.ca2604.1_arm64.deb Size: 1204112 MD5sum: 504f34e6ea936e482dca6b2cf274ee64 SHA1: f6a78422bbe61b221417e49371c30eb7c0055a46 SHA256: ade4e947d82e62aaeb3e744f7386d5e843bf59d61f46a0b237a72ad26c37d16d SHA512: 62e9e259b8882c6ecb2fe455a481a67eb1f5add95bf42fca165aee21622608be3e9dca0080b66c078db9d2e0e30c46d80e9fe06250fffec826ce1679022d7959 Homepage: https://cran.r-project.org/package=enerscape Description: CRAN Package 'enerscape' (Compute Energy Landscapes) Compute energy landscapes using a digital elevation model and body mass of animals. Package: r-cran-enmpa Architecture: arm64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3174 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dosnow, r-cran-ellipse, r-cran-foreach, r-cran-mgcv, r-cran-rcpp, r-cran-snow, r-cran-terra, r-cran-vegan Filename: pool/dists/resolute/main/r-cran-enmpa_0.2.3-1.ca2604.1_arm64.deb Size: 2999750 MD5sum: cfd3efaf6ed53210c6db136838c56565 SHA1: ddce0bca26dbe6a1676198024aa40c44fd54a909 SHA256: 51ee96542a03fb70e7e984b34b43e9456912f545f994ee96b748c7b12ecc2922 SHA512: 71bc825b1fc4b92d93a21c666b5d8b4df385ca7697118fef2929785d15d393c066028884b9d3840d41bb7fe5d83b0af95a6e46c880c7af17f363eb2c35ff9022 Homepage: https://cran.r-project.org/package=enmpa Description: CRAN Package 'enmpa' (Ecological Niche Modeling using Presence-Absence Data) A set of tools to perform Ecological Niche Modeling with presence-absence data. It includes algorithms for data partitioning, model fitting, calibration, evaluation, selection, and prediction. Other functions help to explore signals of ecological niche using univariate and multivariate analyses, and model features such as variable response curves and variable importance. Unique characteristics of this package are the ability to exclude models with concave quadratic responses, and the option to clamp model predictions to specific variables. These tools are implemented following principles proposed in Cobos et al., (2022) , Cobos et al., (2019) , and Peterson et al., (2008) . Package: r-cran-enrichit Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 570 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-yulab.utils Suggests: r-bioc-annotationdbi, r-bioc-clusterprofiler, r-bioc-dose, r-cran-gson, r-bioc-qvalue, r-cran-quarto, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-enrichit_0.1.4-1.ca2604.1_arm64.deb Size: 315430 MD5sum: a73ae4982df54d127b9b2e316f683f85 SHA1: f5858eb03f4732e318dd6eec5b3798942ea5d122 SHA256: 8b551e080dcce5e5eac8118700cf1ba3edd3739e5556f2750393376b5cfa2e1b SHA512: 80aa99a94f649e6bebb85cc138d5342184404da814da7a8b66403d2e73b4afe327059d6070a7bc87841fde79492f5df2f2cca24eb3fd2d02e3f625852125e53e Homepage: https://cran.r-project.org/package=enrichit Description: CRAN Package 'enrichit' ('C++' Implementations of Functional Enrichment Analysis) Fast implementations of functional enrichment analysis methods using 'C++' via 'Rcpp'. Currently provides Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA). The multilevel GSEA algorithm is derived from the 'fgsea' package. Methods are described in Subramanian et al. (2005) and Korotkevich et al. (2021) . 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The estimators used have a bias that decays exponentially fast. Package: r-cran-envcpt Architecture: arm64 Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-changepoint, r-cran-mass, r-cran-zoo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-envcpt_1.1.5-1.ca2604.1_arm64.deb Size: 83904 MD5sum: 75dec2efa80ab65733a70c67d521978f SHA1: 24735fc75b45e040b1551b131fc8bf038b3c8fd5 SHA256: 6a132dd458c4cb6a11bad2cf3ceef33ca9db9b3ea8a9eedbefca13ba826b4cf0 SHA512: 9b497e396a57246adb8a24db0c7df408f555fe29b66509f5bef1c886c17c67274bc26a7ecda3101c6d764f6d409da8432064c578628815221c7223aaa737f7a6 Homepage: https://cran.r-project.org/package=EnvCpt Description: CRAN Package 'EnvCpt' (Detection of Structural Changes in Climate and Environment TimeSeries) Tools for automatic model selection and diagnostics for Climate and Environmental data. In particular the envcpt() function does automatic model selection between a variety of trend, changepoint and autocorrelation models. The envcpt() function should be your first port of call. 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Batch processing, resolution interpolation, wrapper, adduct calculations and molecular formula parsing. Loos, M., Gerber, C., Corona, F., Hollender, J., Singer, H. (2015) . 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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. Package: r-cran-epicontacttrace Architecture: arm64 Version: 0.18.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 793 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-epicontacttrace_0.18.0-1.ca2604.1_arm64.deb Size: 429408 MD5sum: 01061f3ab637e33d90b3b8cc0aeb6f2a SHA1: 4e4b444b12cb77a0e5c1960d95626cf99a847c64 SHA256: a5b0ed4e8b052f8be402ed23864da70797650d13c3f6882c65e28903b200dc99 SHA512: 001b6f0b51b9f350a02695e7a2aaf86796654b166d563f824b4699b9628a5e6290935ae2e81559a0947fd3407dc3c54f7f6017286728dda79bd9a29a133effe6 Homepage: https://cran.r-project.org/package=EpiContactTrace Description: CRAN Package 'EpiContactTrace' (Epidemiological Tool for Contact Tracing) Routines for epidemiological contact tracing and visualisation of network of contacts. 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(2019) . Package: r-cran-epigrowthfit Architecture: arm64 Version: 0.15.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2090 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-tmb, r-cran-nlme, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-epigrowthfit_0.15.4-1.ca2604.1_arm64.deb Size: 1004592 MD5sum: 3a5496fa63db1948b67214b5f548078c SHA1: d66316c118c2c5d80701a614d63639197a634725 SHA256: 488b7654ee33a0d1ebe8c79a779f3ec6294530a321a937e259c73689ceb7c77f SHA512: 04b39d6ff4cdc453da56a017bde11612bb32017783562371726ec3ce11c876e9fdaf69165a05eb4bd09731cfd40c14d867ddac0f7403cea77fb5f624b7a528be Homepage: https://cran.r-project.org/package=epigrowthfit Description: CRAN Package 'epigrowthfit' (Nonlinear Mixed Effects Models of Epidemic Growth) Maximum likelihood estimation of nonlinear mixed effects models of epidemic growth using Template Model Builder ('TMB'). Enables joint estimation for collections of disease incidence time series, including time series that describe multiple epidemic waves. Supports a set of widely used phenomenological models: exponential, logistic, Richards (generalized logistic), subexponential, and Gompertz. Provides methods for interrogating model objects and several auxiliary functions, including one for computing basic reproduction numbers from fitted values of the initial exponential growth rate. Preliminary versions of this software were applied in Ma et al. (2014) and in Earn et al. (2020) . Package: r-cran-epiilm Architecture: arm64 Version: 1.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 862 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_arm64.deb Size: 671178 MD5sum: 3740dc84fc9fd9e3a9a90d147136d788 SHA1: 6508b07ed1246c96144b2bd836bf42dd5cd448bf SHA256: a3ff217fa998ee497d79abc1d0ff217a564c9434048f6f11638311336fbbaa98 SHA512: 3f99e318c96ecb2715f1450fb50e1298808f961d128af48a90a1cd51e61cc58159570c3a08e5e54555900be9d198cff9faf7dba70816c5f5d9aa917e439f533e 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: arm64 Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3650 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_arm64.deb Size: 3446654 MD5sum: 6df5b5d2fcc8a505f1e52da7187ea45d SHA1: 96a4311d6bdbd12641b67ca75e6afe19bab0e214 SHA256: 42bc7c295eb305743194343fc563d4b1f6af7f48a91b4e9d62a770b7fa0d4608 SHA512: 88839ad8db287db9ab8206763f58fd0fefe1ba163101958db8d4a6a1d034c43fd928ce0b413845d6fec6ff67b9303a24c92d37a08b1a323b2ef310acb7f9ed42 Homepage: https://cran.r-project.org/package=EpiInvert Description: CRAN Package 'EpiInvert' (Variational Techniques in Epidemiology) Using variational techniques we address some epidemiological problems as the incidence curve decomposition by inverting the renewal equation as described in Alvarez et al. (2021) and Alvarez et al. (2022) or the estimation of the functional relationship between epidemiological indicators. We also propose a learning method for the short time forecast of the trend incidence curve as described in Morel et al. (2022) . Package: r-cran-epilps Architecture: arm64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1174 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-rcpp, r-cran-coda, r-cran-epiestim, r-cran-ggplot2, r-cran-gridextra, r-cran-rcpparmadillo Suggests: r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-epilps_1.3.0-1.ca2604.1_arm64.deb Size: 734736 MD5sum: eed2adedb89f01ca69fd0849334bd05e SHA1: 4a09032419c926ae6067ef18edf2448bb4c91155 SHA256: edeeafd9c20c22fc5c99f9febb2af99f891b91040756791d2087a63ecfa75e4e SHA512: b07ea4db9db941893527f71cd7c8d93c07318ef69620a98ce4f2906fb93eaf8f874477ccfdbaf7df0e6ff8ee2e71fe41bde228e59ae7515ea6ff553ded456a98 Homepage: https://cran.r-project.org/package=EpiLPS Description: CRAN Package 'EpiLPS' (A Fast and Flexible Bayesian Tool for Estimating EpidemiologicalParameters) Estimation of epidemiological parameters with Laplacian-P-splines following the methodology of Gressani et al. 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Package: r-cran-epimodel Architecture: arm64 Version: 2.6.1-1.ca2604.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5432 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-desolve, r-cran-networkdynamic, r-cran-tergm, r-cran-statnet.common, r-cran-future, r-cran-future.apply, r-cran-collections, r-cran-ergm, r-cran-network, r-cran-rcolorbrewer, r-cran-ape, r-cran-lazyeval, r-cran-ggplot2, r-cran-tibble, r-cran-rlang, r-cran-dplyr, r-cran-coda, r-cran-networklite, r-cran-rcpp Suggests: r-cran-bslib, r-cran-dt, r-cran-ergm.ego, r-cran-egor, r-cran-knitr, r-cran-ndtv, r-cran-plotly, r-cran-rmarkdown, r-cran-shiny, r-cran-testthat, r-cran-progressr, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-epimodel_2.6.1-1.ca2604.2_arm64.deb Size: 2200066 MD5sum: 4bde7f31bfb565f40ee3e7c950c8be31 SHA1: 1b18b124ef180c91080fabcdb6507b81be431673 SHA256: 8976eb86d826de923d97a497be205562e99400af4439d7db944098220a7f4908 SHA512: 7404ee3969cb174508291ed182208c26093363cc37cafe5d66ebd060bc0a2b70c6b8f0b8eb6020e592b84ccde7a7b3d91790f1d5066bd37fa4733f355ec61d0e Homepage: https://cran.r-project.org/package=EpiModel Description: CRAN Package 'EpiModel' (Mathematical Modeling of Infectious Disease Dynamics) Tools for simulating mathematical models of infectious disease dynamics. Epidemic model classes include deterministic compartmental models, stochastic individual-contact models, and stochastic network models. Network models use the robust statistical methods of exponential-family random graph models (ERGMs) from the Statnet suite of software packages in R. Standard templates for epidemic modeling include SI, SIR, and SIS disease types. EpiModel features an API for extending these templates to address novel scientific research aims. Full methods for EpiModel are detailed in Jenness et al. (2018, ). Package: r-cran-epinet Architecture: arm64 Version: 2.1.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-network Filename: pool/dists/resolute/main/r-cran-epinet_2.1.11-1.ca2604.1_arm64.deb Size: 182898 MD5sum: 405a784b62ad4ccbb8cc88c0038c34bc SHA1: aaba01a29c88ff31ee6a621b29c6810f6a78473a SHA256: 0e2276124218c56a6e26f66c425daeaf180c168c47be6b65e16e4953b2715dfc SHA512: 7492889e8dc374e67dc11eba1797a93489729eef4914a3f5f2883c2b2185fab11acf8da856e6be75080f77e375553605f11ee935a927ae7f4b43c823bff80779 Homepage: https://cran.r-project.org/package=epinet Description: CRAN Package 'epinet' (Epidemic/Network-Related Tools) A collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data. Package: r-cran-epinetr Architecture: arm64 Version: 0.96-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1136 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ga, r-cran-ggplot2, r-cran-igraph, r-cran-rcpp, r-cran-rcppalgos, r-cran-reshape2, r-cran-vcfr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-epinetr_0.96-1.ca2604.1_arm64.deb Size: 712414 MD5sum: e694049beb317392761041279e741ec7 SHA1: adcf9394e891de02ec7acf65d1b292636826a1e5 SHA256: 0c0cccfda346df3e51d7fd342b4f933f081350aaa32485e20a2a2c5da1b5a212 SHA512: bee8e249f78a2c85afcb17d1d5692beb6d11340542b828230f29c7b352e9f3404063785a60cc167da8691d6c5f738a5460379bd8d4516e7217b5cc5186699227 Homepage: https://cran.r-project.org/package=epinetr Description: CRAN Package 'epinetr' (Epistatic Network Modelling with Forward-Time Simulation) Allows for forward-in-time simulation of epistatic networks with associated phenotypic output. Package: r-cran-epinow2 Architecture: arm64 Version: 1.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 12893 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 6682278 MD5sum: b954837a545f95599fae6ec6da3ba7cc SHA1: 124363291e4d39751560225b7b57a84eb41b2bad SHA256: aa322fde6831069a2e8707613df55b241bc9377cb5184f660228894151235025 SHA512: ba87040acc5aa7ef45d0c9242181beaa260c5166ede42d45d0974681551b508b2166b22198b9001f2d6580a901e92ba58f74a4d03817e81625812c9490a89f0f Homepage: https://cran.r-project.org/package=EpiNow2 Description: CRAN Package 'EpiNow2' (Estimate and Forecast Real-Time Infection Dynamics) Estimates the time-varying reproduction number, rate of spread, and doubling time using a renewal equation approach combined with Bayesian inference via Stan. Supports Gaussian process and random walk priors for modelling changes in transmission over time. Accounts for delays between infection and observation (incubation period, reporting delays), right-truncation in recent data, day-of-week effects, and observation overdispersion. Can estimate relationships between primary and secondary outcomes (e.g., cases to hospitalisations or deaths) and forecast both. Runs across multiple regions in parallel. Based on Abbott et al. (2020) and Gostic et al. (2020) . Package: r-cran-epiphy Architecture: arm64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1031 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_arm64.deb Size: 677902 MD5sum: 39a0f4d3dea9c7b7ea00c29e95acd355 SHA1: d4841bd3d9c70e7abb2cd7958bb5a23465c9ce6f SHA256: 113893890d9312388bb49938ecb6e846839143650229b3f6a9c83049ceefdaac SHA512: b7d4384cbbaf12c08460bde67a3c9cae9930da03ae04c1a4734929e0a74fc561bc58d33433b420504264c51b4208795b18b54c2cd75c8194d13ce1d5e76fe88b Homepage: https://cran.r-project.org/package=epiphy Description: CRAN Package 'epiphy' (Analysis of Plant Disease Epidemics) A toolbox to make it easy to analyze plant disease epidemics. It provides a common framework for plant disease intensity data recorded over time and/or space. Implemented statistical methods are currently mainly focused on spatial pattern analysis (e.g., aggregation indices, Taylor and binary power laws, distribution fitting, SADIE and 'mapcomp' methods). See Laurence V. Madden, Gareth Hughes, Franck van den Bosch (2007) for further information on these methods. Several data sets that were mainly published in plant disease epidemiology literature are also included in this package. Package: r-cran-epipvr Architecture: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3608 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_arm64.deb Size: 1644954 MD5sum: 778b20a601e5d124938365259c1d9451 SHA1: 8019a7ff60b490e84f2d9b71649755de97d3fbc4 SHA256: 3f7508c40a7765ae89d28d9e2c4d1738b89e5eb8a5935814a40c4bd2e0a0c944 SHA512: 7ef39636a93a6d33452ea89b637fd1e8192cd7edc19d0cb89d9728c28d7392d556eac9273be930e25f0049b80d8dd6aa6be7b12ea12d09add3cdb790b0676554 Homepage: https://cran.r-project.org/package=EpiPvr Description: CRAN Package 'EpiPvr' (Estimating Plant Pathogen Epidemiology Parameters fromLaboratory Assays) Provides functions for estimating plant pathogen parameters from access period (AP) experiments. Separate functions are implemented for semi-persistently transmitted (SPT) and persistently transmitted (PT) pathogens. The common AP experiment exposes insect cohorts to infected source plants, healthy test plants, and intermediate plants (for PT pathogens). The package allows estimation of acquisition and inoculation rates during feeding, recovery rates, and latent progression rates (for PT pathogens). Additional functions support inference of epidemic risk from pathogen and local parameters, and also simulate AP experiment data. The functions implement probability models for epidemiological analysis, as derived in Donnelly et al. (2025), . These models were originally implemented in the 'EpiPv' 'GitHub' package. Package: r-cran-epiworldr Architecture: arm64 Version: 0.14.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6704 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_arm64.deb Size: 3484432 MD5sum: 475941dda114651af05a18c945d4faab SHA1: 8df92e7f6f3c9e04e095109d44f1d28a8d970fa3 SHA256: 3fd67368ba6bf1606e7f3b038ca622d378f12dba6a9acc9ea571e4e5afc6c630 SHA512: 61d7a14b2e64038073587ca64b0fc4afc03774e5bf440e62e20cccf410cb262f651b9e7e6e759dad48687963402afedd1b5ad6326a807e2f90e7b0e4c27ccd05 Homepage: https://cran.r-project.org/package=epiworldR Description: CRAN Package 'epiworldR' (Fast Agent-Based Epi Models) A flexible framework for Agent-Based Models (ABM), the 'epiworldR' package provides methods for prototyping disease outbreaks and transmission models using a 'C++' backend, making it very fast. It supports multiple epidemiological models, including the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Removed (SIR), Susceptible-Exposed-Infected-Removed (SEIR), and others, involving arbitrary mitigation policies and multiple-disease models. Users can specify infectiousness/susceptibility rates as a function of agents' features, providing great complexity for the model dynamics. Furthermore, 'epiworldR' is ideal for simulation studies featuring large populations. Package: r-cran-epizootic Architecture: arm64 Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1328 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 854612 MD5sum: 13c6675985ee478380a4d5cd6f0c9b87 SHA1: 671f2890043058578dddeac8ad2f6b34dfb19c1c SHA256: 409f7ade70349e5c98b924bfa871b4ea1a2980b6c4408b81bcfbe25dc22313c3 SHA512: f72ddb7bf27be1a20a1f22cc906edc13c9801db362f7cb5f4c4984edb3f8336da861521ad0a23d91a253f95d8dc622ea3e266fef89385a46e0756446b7d081a6 Homepage: https://cran.r-project.org/package=epizootic Description: CRAN Package 'epizootic' (Spatially Explicit Population Models of Disease Transmission inWildlife) This extension of the pattern-oriented modeling framework of the 'poems' package provides a collection of modules and functions customized for modeling disease transmission on a population scale in a spatiotemporally explicit manner. This includes seasonal time steps, dispersal functions that track disease state of dispersers, results objects that store disease states, and a population simulator that includes disease dynamics. Package: r-cran-eplusr Architecture: arm64 Version: 0.16.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4279 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-callr, r-cran-checkmate, r-cran-cli, r-cran-data.table, r-cran-lubridate, r-cran-processx, r-cran-r6, r-cran-rsqlite, r-cran-stringi, r-cran-units Suggests: r-cran-hms, r-cran-decido, r-cran-rgl, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-eplusr_0.16.3-1.ca2604.1_arm64.deb Size: 4096694 MD5sum: 39631b7b7f637916df8f76f0ecf7b9c1 SHA1: 0848f3ef90698e04bc5805f9635e2ec2a645b383 SHA256: 43c59f312f7c45faba00147a47380b697d838076633c798d2a11ab7da03c6906 SHA512: eb29525e103d183f3d929436a83f501029c5a66751dbbe8e73d75960819a51e75dd8cf547462aaf23bafdf9e8af5b44b69bd48793cb85a853b030cd87df5750b Homepage: https://cran.r-project.org/package=eplusr Description: CRAN Package 'eplusr' (A Toolkit for Using Whole Building Simulation Program'EnergyPlus') A rich toolkit of using the whole building simulation program 'EnergyPlus'(), which enables programmatic navigation, modification of 'EnergyPlus' models and makes it less painful to do parametric simulations and analysis. Package: r-cran-epm Architecture: arm64 Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1434 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1267280 MD5sum: 911ee4a86e5e7ca5316c0966989d5d35 SHA1: 06ce1d70c2292419228499a735640c088d64440c SHA256: 98abc311cb287e09eee30c80cefa1632e636a9af9c47f71b0c60333e26227870 SHA512: 88fd0fa541a56d5d835525d50d03162d3ba8b4ce9c526a881daf8238484177973c88fa5a87b3c2661ad62f40f491b9e977081ec23c717b09f9afa79d23593137 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) . Package: r-cran-epx Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1209 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-foreach, r-cran-randomforest, r-cran-dorng, r-cran-nnet, r-cran-doparallel, r-cran-rngtools Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-epx_1.0.4-1.ca2604.1_arm64.deb Size: 1076158 MD5sum: 4237648eb5def33e5176a4962acf78f2 SHA1: c9dd1d3ab00cc2fc22230961f0cdccbe3657db45 SHA256: 1f4f7efc4476c57cdfd84e284adbc1204c27c8b60505ac81c0547210309a8289 SHA512: 7643e3594ca70c31c6da25f70ab38ebd956131de5e117a761ee0b972649f2cb6bc1f2b2f192f1e5eba2b92fdded566d20276d3d4cfdcca387d65bb2bfa81b79a Homepage: https://cran.r-project.org/package=EPX Description: CRAN Package 'EPX' (Ensemble of Phalanxes) An ensemble method for the statistical detection of a rare class in two-class classification problems. 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Package: r-cran-equalcovs Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-equalcovs_1.0-1.ca2604.1_arm64.deb Size: 16118 MD5sum: 980c7e461c6738c43b73e3a974b9702a SHA1: 3546dfb2be59eb4edbf59234e900be11d86fe48f SHA256: 593c87d885c96ee8b7b9ecfef95212c9320631fcfc8bf759abae4c95783ba71f SHA512: 400e1ac7df6f1edc34d1055038ace64723626177fa4b99ceb3450700ef6fcbe15b8238084becddc25d481984c8f09924ebb8f123b9eef5d7f967427e4732e580 Homepage: https://cran.r-project.org/package=equalCovs Description: CRAN Package 'equalCovs' (Testing the Equality of Two Covariance Matrices) Tests the equality of two covariance matrices, used in paper "Two sample tests for high dimensional covariance matrices." Li and Chen (2012) . Package: r-cran-equalden.hd Architecture: arm64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 449 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-equalden.hd_1.2.1-1.ca2604.1_arm64.deb Size: 355898 MD5sum: 1e97262ee79e36bee598840c0daf6102 SHA1: 90a728025e06daa3bfb358fbac74898e4511c469 SHA256: b1f00eb7920247f8458686cdd32ec225494f744700cffb352b848d24cbc2178b SHA512: 9515b282e8ba9f213ff411d702cc9aac5a3267fba006c68d866ba63016ede9b10cb9c4c1a90484193ed7abc89ab75382032515f719aeef8c2f7cd99d21831af0 Homepage: https://cran.r-project.org/package=Equalden.HD Description: CRAN Package 'Equalden.HD' (Testing the Equality of a High Dimensional Set of Densities) The equality of a large number k of densities is tested by measuring the L2 distance between the corresponding kernel density estimators and the one based on the pooled sample. The test even works for sample sizes as small as 2. 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See Battauz (2025) for a detailed description of the package. See Battauz M. (2017) , Battauz and 'Leoncio' (2023) and Haberman S. J. (2009) ) for the methods to link multiple test forms. Package: r-cran-equitrends Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-clubsandwich, r-cran-nloptr, r-cran-dplyr, r-cran-rlang, r-cran-plm, r-cran-rcpp, r-cran-rcppparallel, r-cran-vgam, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-equitrends_1.0.0-1.ca2604.1_arm64.deb Size: 225116 MD5sum: 31ac9977b5bf8ce56bba6cc22362a833 SHA1: 1908ac8d4c2de9fee3951f4c7976234011c3ba9a SHA256: 5b35f3715d0fd2a6c06f4b98e4807fe9e37d4ef9cec07ef5ca61ada6912458d9 SHA512: 12d57ec92f73f8afc5b724867fd42b5625aa2360b9e5ace6c9667dc64e330391a4af351cd7b630694122dbdfcd2860982a4c0741acf090c20e458ac30ea7d276 Homepage: https://cran.r-project.org/package=EquiTrends Description: CRAN Package 'EquiTrends' (Equivalence Testing for Pre-Trends in Difference-in-DifferencesDesigns) Testing for parallel trends is crucial in the Difference-in-Differences framework. To this end, this package performs equivalence testing in the context of Difference-in-Differences estimation. It allows users to test if pre-treatment trends in the treated group are “equivalent” to those in the control group. Here, “equivalence” means that rejection of the null hypothesis implies that a function of the pre-treatment placebo effects (maximum absolute, average or root mean squared value) does not exceed a pre-specified threshold below which trend differences are considered negligible. The package is based on the theory developed in Dette & Schumann (2024) . 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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) . Package: r-cran-eratosthenes Architecture: arm64 Version: 0.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 497 Depends: libc6 (>= 2.17), 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-paletteer Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-eratosthenes_0.0.9-1.ca2604.1_arm64.deb Size: 246416 MD5sum: 61eed6a77c7e3a5dc08e12bfad7182e8 SHA1: 840926e8cd0d3761f556b9e2e663b39290f88724 SHA256: 5e60924316ab7345616850d5395eb447a0b1674269b21e6eb16d6bb830686cb6 SHA512: e0dc98a49fc900f595ef0086fe62bfeee26d2270bd2fb12adbfa9c5d5c6dc68b93ba541c1335f4d51b76ff7af3bac16aa9f50a721669fa61ba4548475b6123c9 Homepage: https://cran.r-project.org/package=eratosthenes Description: CRAN Package 'eratosthenes' (Archaeological Synchronism) Estimation of unknown historical or archaeological dates subject to relationships with other relative dates and absolute constraints, derived as marginal densities from the full joint conditional, using a two-stage Gibbs sampler with consistent batch means to assess convergence. Features reporting on Monte Carlo standard errors, as well as tools for rule-based estimation of dates of production and use of artifact types, aligning and checking relative sequences, and evaluating the impact of the omission of relative/absolute events upon one another. 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This package implements a regression tree based gradient boosting estimator for nonparametric multiple expectile regression, proposed by Yang, Y., Qian, W. and Zou, H. (2018) . The code is based on the 'gbm' package originally developed by Greg Ridgeway. 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See Krivitsky (2012) and Krivitsky, Hunter, Morris, and Klumb (2023) . 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See Krivitsky and Morris (2017) . 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'ergm' is a part of the Statnet suite of packages for network analysis. See Hunter, Handcock, Butts, Goodreau, and Morris (2008) and Krivitsky, Hunter, Morris, and Klumb (2023) . Package: r-cran-ergmclust Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 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-lda, r-cran-quadprog, r-cran-igraph, r-cran-viridis, r-cran-locfit, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-ergmclust_1.0.1-1.ca2604.1_arm64.deb Size: 230648 MD5sum: e00780afcdb2c67fed9a0503868a5761 SHA1: 19291319a3a71ab259498a54e816ff6fcca1bd79 SHA256: 39009154f1cbbd1a6626b3b36a1f186b830b991cacc96e0721acd06346a7abfd SHA512: 032ed58c9ed9b28831a53880ce665625c02143086bc3ca3f6a81c9823107d41a1c862df8e6c62109d74555d21215e111dd8d18b9652dad227b365037fa5dde6c Homepage: https://cran.r-project.org/package=ergmclust Description: CRAN Package 'ergmclust' (Exponential-Family Random Graph Models for Network Clustering) Implements clustering and estimates parameters in Exponential-Family Random Graph Models for static undirected and directed networks, developed in Vu et al. (2013) . Package: r-cran-ergmgp Architecture: arm64 Version: 0.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 91990 MD5sum: ddc909b894539d572e66b5305ec6ddf8 SHA1: 238c6d0a60b9ac28dba0ebfb318e3c555e4ceaeb SHA256: 371dcdb654b68621abf113b8a27eb71c0e8e2792ab1194d9de51309300e0fb59 SHA512: 78d82c77b631f52e2cef72dfe8ab0eebbe326367370dc0d3cb2dd4bf6dcaf2e0fd191f8bb4fc7b0674162cf456ec6029c2d0764560c83b513ed81c75334b0d35 Homepage: https://cran.r-project.org/package=ergmgp Description: CRAN Package 'ergmgp' (Tools for Modeling ERGM Generating Processes) Provides tools for simulating draws from continuous time processes with well-defined exponential family random graph (ERGM) equilibria, i.e. ERGM generating processes (EGPs). 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Package: r-cran-ergmito Architecture: arm64 Version: 0.3-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1668 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-ergm, r-cran-network, r-cran-mass, r-cran-rcpp, r-cran-texreg, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-sna, r-cran-lmtest, r-cran-fmcmc, r-cran-coda, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-ergmito_0.3-2-1.ca2604.1_arm64.deb Size: 957080 MD5sum: 11164bfa0d8c0cd46eb5794d0f68c140 SHA1: 6b3298c3ffd74fde3eba0aaa18f75b74f7105260 SHA256: 5a800003ccef1d43ee2790d41052c40692177413aab6a7824754533e59cc7ff7 SHA512: f541eb0d73f3cb2d0ed9707038b28677f75580e2c42524bfcd16ddde5794de0ae553bf791c85cd738f1f72622a6f8380a2d081a13f82d627a7dfd333eeada074 Homepage: https://cran.r-project.org/package=ergmito Description: CRAN Package 'ergmito' (Exponential Random Graph Models for Small Networks) Simulation and estimation of Exponential Random Graph Models (ERGMs) for small networks using exact statistics as shown in Vega Yon et al. (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. Package: r-cran-erm Architecture: arm64 Version: 1.0-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1398 Depends: libc6 (>= 2.17), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-lattice, r-cran-colorspace, r-cran-psych Filename: pool/dists/resolute/main/r-cran-erm_1.0-10-1.ca2604.1_arm64.deb Size: 1233846 MD5sum: 01e91bd9d7677f5886fb6223c0d0d0bc SHA1: 82f85dff20bc23995ee1367ba1b24afa4a8b50ed SHA256: 8569ac77b286376b05c750da9c7331b013ca12c0cc57c29f25bcf98598a6ad45 SHA512: cdcd4373f8ca04a92b6508a01901bf79e9a46f3eb39d649948d639dea974f64a1bae914c2ab64c991a43efdda4998a567f1e15b2e2714d387f1d104fe51ae60e Homepage: https://cran.r-project.org/package=eRm Description: CRAN Package 'eRm' (Extended Rasch Modeling) Fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM). 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Package: r-cran-esemifar Architecture: arm64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fracdiff, r-cran-smoots, r-cran-rcpp, r-cran-future, r-cran-furrr, r-cran-ggplot2, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-esemifar_2.0.1-1.ca2604.1_arm64.deb Size: 253844 MD5sum: a8580be7926388665b28a4e1272f0ad6 SHA1: 8638277824edb32e2cedf3887febafa4d6702256 SHA256: cc8030673c5a15e108d194f57b879c285ac813c73485988492ab64f48fdf4f2b SHA512: f6425e8d18dc043cd4d38be0a9bda4e57823f7a8a2769fd1727d3f1dd87140b7080080c754d32eb01a957fdd62750ffeed2ab7790224d04281e26cb1e9bf85d5 Homepage: https://cran.r-project.org/package=esemifar Description: CRAN Package 'esemifar' (Smoothing Long-Memory Time Series) The nonparametric trend and its derivatives in equidistant time series (TS) with long-memory errors can be estimated. The estimation is conducted via local polynomial regression using an automatically selected bandwidth obtained by a built-in iterative plug-in algorithm or a bandwidth fixed by the user. The smoothing methods of the package are described in Letmathe, S., Beran, J. and Feng, Y., (2023) . Package: r-cran-esmprep Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-lubridate Filename: pool/dists/resolute/main/r-cran-esmprep_0.2.0-1.ca2604.1_arm64.deb Size: 370832 MD5sum: 65221ad45c360653898fc30093c88bfa SHA1: 65dfa602c1a2bd5a30c957774bd929e1137e279f SHA256: 9020913f04ae79d16c5c4abac6c789eb88c523d60229ca75b4d052a867464c8a SHA512: c00e6d8abf6387290d86e8f4f475e5bf5d3107cfe33bd397ac7bad0a90302c75044ccfc9472cb2d5b3aaa9c562ed85f7b2e5c6532953909b6addcfc96ddd8f4b Homepage: https://cran.r-project.org/package=esmprep Description: CRAN Package 'esmprep' (Data Preparation During and After the Use of the ExperienceSampling Methodology (ESM)) Support in preparing a raw ESM dataset for statistical analysis. Preparation includes the handling of errors (mostly due to technological reasons) and the generating of new variables that are necessary and/or helpful in meeting the conditions when statistically analyzing ESM data. The functions in 'esmprep' are meant to hierarchically lead from bottom, i.e. the raw (separated) ESM dataset(s), to top, i.e. a single ESM dataset ready for statistical analysis. This hierarchy evolved out of my personal experience in working with ESM data. Package: r-cran-espadon Architecture: arm64 Version: 1.11.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2402 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-colorspace, r-cran-dt, r-cran-igraph, r-cran-js, r-cran-matrix, r-cran-misc3d, r-cran-openxlsx, r-cran-progress, r-cran-qs2, r-cran-rcpp, r-cran-rdpack, r-cran-rgl, r-cran-rvcg, r-cran-shiny, r-cran-shinywidgets, r-cran-sodium Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sf Filename: pool/dists/resolute/main/r-cran-espadon_1.11.5-1.ca2604.1_arm64.deb Size: 1997334 MD5sum: 1cbf49a7c7703f568cc887e22ec46a9f SHA1: 288383b84bf225858a02fd00e9f0bc2ecd14c971 SHA256: 643b183b5d29da5bde48b59863116bb47e0aaa7406689f42f0c3dd161fdc5b92 SHA512: 35483331c3014cdc68e76e4c29e0412f45bcc71209f64686fd9ec21e3f50ecfc2883a4c561a3f367a6fd80bf7fc92b5a404380deb472f61350eee46ceb3ccfc5 Homepage: https://cran.r-project.org/package=espadon Description: CRAN Package 'espadon' (Easy Study of Patient DICOM Data in Oncology) Read, process, and export DICOM and DICOM-RT files (structures, dosimetry, imagery) for medical physics and clinical research, with patient-oriented 2D-3D visualization. Package: r-cran-esreg Architecture: arm64 Version: 0.6.2-1.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), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-quantreg, r-cran-rcpp, r-cran-formula, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-esreg_0.6.2-1.ca2604.1_arm64.deb Size: 152046 MD5sum: e5ae002b6cb9cc16536438deac9d2acb SHA1: c823f17c42c480d8ed941419113f334714fdbc8c SHA256: a7c6f4fc20db62f123cb9de25029b0904e90bae343b1bf4a373614bca3f8a24c SHA512: b6d19a634c093254623531a0bd61ca51a60c8fbe5f7780b71b8b801336683412dc2df2211a0ef399f566ad618607a85b3906c345c9a4f03a1bee6cec6976c92d Homepage: https://cran.r-project.org/package=esreg Description: CRAN Package 'esreg' (Joint Quantile and Expected Shortfall Regression) Simultaneous modeling of the quantile and the expected shortfall of a response variable given a set of covariates, see Dimitriadis and Bayer (2019) . Package: r-cran-ess Architecture: arm64 Version: 1.1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-igraph Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-ess_1.1.2.1-1.ca2604.1_arm64.deb Size: 246650 MD5sum: 8e82f0adc1c99d0b72e78ebf0f9c28fd SHA1: bc58ebeebb7b3ee4a21fa09e9ccfb601bd920564 SHA256: 337bf67ebf4f012bbd662221c325a0b3f097bb10db2d9c10852d089add6f382b SHA512: 101bc3072367980b3448293b86bb14103d0e805491c1e41c248632e6edc05ff565e23aa4726d826ff054f7fbb90e47b3d64dc6249ce0289d84553ec636cd116e Homepage: https://cran.r-project.org/package=ess Description: CRAN Package 'ess' (Efficient Stepwise Selection in Decomposable Models) An implementation of the ESS algorithm following Amol Deshpande, Minos Garofalakis, Michael I Jordan (2013) . The ESS algorithm is used for model selection in decomposable graphical models. Package: r-cran-esshist Architecture: arm64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1711 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-esshist_1.2.2-1.ca2604.1_arm64.deb Size: 1622822 MD5sum: f4ad324a089163a2e4de9088d378a2bc SHA1: c51bd556ab6194ce95d851c23d7c7739962744f5 SHA256: 2b5d61dad2c120200b72c235a397a84d14df3a5421625bd23a0dcde4e1417f58 SHA512: 957889fdc7d428c331a9dbec30510ee8f1039d52d67958461ae3662e1dd7cbd6f1a2670fe3140f46adf7ef8d19e277932cb3be44cf1d4749255eb1ef3e259ef8 Homepage: https://cran.r-project.org/package=essHist Description: CRAN Package 'essHist' (The Essential Histogram) Provide an optimal histogram, in the sense of probability density estimation and features detection, by means of multiscale variational inference. In other words, the resulting histogram servers as an optimal density estimator, and meanwhile recovers the features, such as increases or modes, with both false positive and false negative controls. Moreover, it provides a parsimonious representation in terms of the number of blocks, which simplifies data interpretation. The only assumption for the method is that data points are independent and identically distributed, so it applies to fairly general situations, including continuous distributions, discrete distributions, and mixtures of both. For details see Li, Munk, Sieling and Walther (2016) . Package: r-cran-estimatr Architecture: arm64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 782 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-formula, r-cran-generics, r-cran-rcpp, r-cran-rlang, r-cran-rcppeigen Suggests: r-cran-fabricatr, r-cran-randomizr, r-cran-aer, r-cran-clubsandwich, r-cran-emmeans, r-cran-estimability, r-cran-margins, r-cran-modelsummary, r-cran-prediction, r-cran-sandwich, r-cran-stargazer, r-cran-testthat, r-cran-car Filename: pool/dists/resolute/main/r-cran-estimatr_1.0.6-1.ca2604.1_arm64.deb Size: 446860 MD5sum: 1c2f4c0935d717429d67cccf03f2511e SHA1: d6f3ea2dabcb53c273799f7d56c2312ca715c4fa SHA256: b9a832d9caf85d95872e03b22a0214cd76520842785cdfc890954a1d62cee417 SHA512: 36a68fd45c098293a215dcb7efe655a7e55888496cef556ae9deeafce8d3557a67f908b77a5549af8c6ddace277cf8397f17fd61dd9862928882b44830a4a7eb Homepage: https://cran.r-project.org/package=estimatr Description: CRAN Package 'estimatr' (Fast Estimators for Design-Based Inference) Fast procedures for small set of commonly-used, design-appropriate estimators with robust standard errors and confidence intervals. Includes estimators for linear regression, instrumental variables regression, difference-in-means, Horvitz-Thompson estimation, and regression improving precision of experimental estimates by interacting treatment with centered pre-treatment covariates introduced by Lin (2013) . Package: r-cran-etas Architecture: arm64 Version: 0.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2330 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-maps, r-cran-lattice, r-cran-goftest, r-cran-spatstat.geom, r-cran-spatstat.explore, r-cran-spatstat.random, r-cran-rcpp, r-cran-fields Filename: pool/dists/resolute/main/r-cran-etas_0.7.2-1.ca2604.1_arm64.deb Size: 2031746 MD5sum: b7f3265fe5903c089ec7e3044a997ba0 SHA1: d18bda1143e6ac9f56225d1fef60bba25baa7936 SHA256: 6d033a1323890fded5cd1f9d72dcc810e75696fc89072b6e8138299fe0aef1d2 SHA512: 9ce5d98fdf16168a531445ee8a1581229646b55fe2183f35e8b66dd434d7dcf016c3d77d4fc236638d349375bbda1ce1148852d3e5e26b6b4df2e6315383c93e Homepage: https://cran.r-project.org/package=ETAS Description: CRAN Package 'ETAS' (Modeling Earthquake Data Using 'ETAS' Model) Fits the space-time Epidemic Type Aftershock Sequence ('ETAS') model to earthquake catalogs using a stochastic 'declustering' approach. The 'ETAS' model is a 'spatio-temporal' marked point process model and a special case of the 'Hawkes' process. The package is based on a Fortran program by 'Jiancang Zhuang' (available at ), which is modified and translated into C++ and C such that it can be called from R. Parallel computing with 'OpenMP' is possible on supported platforms. Package: r-cran-etasflp Architecture: arm64 Version: 2.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 770 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mapdata, r-cran-fields, r-cran-maps Filename: pool/dists/resolute/main/r-cran-etasflp_2.3.0-1.ca2604.1_arm64.deb Size: 657422 MD5sum: 6fcec47d0b08d679ea7d8f43162b826a SHA1: a84c7ec1f735b0c7fee9fbecfae7b91efa8ce60d SHA256: c99c0021d2ced72fa2fc7e3a72b977a8ab04c7e47197f93453d3615a75ff609e SHA512: 3394cfa53a997ae7cb0bddfa61939ba2b93f65a1d08d40395e25b7b005948919be0fed936b750e708583ec0eadfabc5023857853e8902897a3c8e7c8187aa4bf Homepage: https://cran.r-project.org/package=etasFLP Description: CRAN Package 'etasFLP' (Mixed FLP and ML Estimation of ETAS Space-Time Point Processesfor Earthquake Description) Estimation of the components of an ETAS (Epidemic Type Aftershock Sequence) model for earthquake description. Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive). New version 2.0.0: covariates have been introduced to explain the effects of external factors on the induced seismicity; the parametrization has been changed; in version 2.3.0 improved update method. Chiodi, Adelfio (2017). Package: r-cran-ethiodate Architecture: arm64 Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 458 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rcpp, r-cran-stringr, r-cran-vctrs Suggests: r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ethiodate_0.3.1-1.ca2604.1_arm64.deb Size: 207316 MD5sum: 645d7bbdcf4ce66aecd3a5d8a04c2fb7 SHA1: 9c76ddae498bb6fbace37b130da0e476bf54e919 SHA256: a8cc2e7852465bf3e907202c77d7806164157fc06533fcefd8753feb0389b7f2 SHA512: 3813631a6b49e9125563c348d498577bc7b1f61068e91d6c047059e8b5ab7aa12d69eb1f59a8f5a643cb4f295d1139691fffa939c71aec1f1f32b42fc34e0bb9 Homepage: https://cran.r-project.org/package=ethiodate Description: CRAN Package 'ethiodate' (Working with Ethiopian Dates) A robust and efficient solution for working with Ethiopian dates. It can seamlessly convert to and from Gregorian dates. It is designed to be compatible with the 'tidyverse' data workflow, including plotting with 'ggplot2'. It ensures lightning-fast computations by integrating high-performance 'C++' code through 'Rcpp' package. Package: r-cran-ethseq Architecture: arm64 Version: 3.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4341 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-geometry, r-cran-data.table, r-bioc-snprelate, r-bioc-gdsfmt, r-cran-plot3d, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ethseq_3.0.2-1.ca2604.1_arm64.deb Size: 1710244 MD5sum: 120ab6fa407f37905d2d4a64e096b3bb SHA1: e635d16a975f71a418f7b85870c1e8e058ea9653 SHA256: 91a32756d73233ef46d99f33218f50e0f2a18e67d49a98cbe592688a90355b58 SHA512: 7973865a6e152e95d233a2220171415db5e58a6677cef686b09a921f93d56ab53720b9ecdc8b789c9f2d68bbfacd6f74718a2ccdf62c56f1ba650595c6c17a4c Homepage: https://cran.r-project.org/package=EthSEQ Description: CRAN Package 'EthSEQ' (Ethnicity Annotation from Whole-Exome and Targeted SequencingData) Reliable and rapid ethnicity annotation from whole exome and targeted sequencing data. Package: r-cran-etm Architecture: arm64 Version: 1.1.2-1.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), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-lattice, r-cran-data.table, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-kmi, r-cran-geepack Filename: pool/dists/resolute/main/r-cran-etm_1.1.2-1.ca2604.1_arm64.deb Size: 556824 MD5sum: f45071d5821661ef5f58bc7e3d89dc64 SHA1: 7a34ca50363813f4e66a89adce8b02390749ea61 SHA256: abcc9ade40bb530c8aeb6ef7569858a8a75e7f83523dcb2eb3d7fe6e3ae6e1a3 SHA512: e4db17ee979c98612e252f4d94b99c1d194aabad5efec9bfca9b5026fd1ef8ca751f8c109ccfc03a658fe55a240201e947cd6e5dc47da9389a3e2d697aebeae1 Homepage: https://cran.r-project.org/package=etm Description: CRAN Package 'etm' (Empirical Transition Matrix) The etm (empirical transition matrix) package permits to estimate the matrix of transition probabilities for any time-inhomogeneous multi-state model with finite state space using the Aalen-Johansen estimator. Functions for data preparation and for displaying are also included (Allignol et al., 2011 ). Functionals of the Aalen-Johansen estimator, e.g., excess length-of-stay in an intermediate state, can also be computed (Allignol et al. 2011 ). Package: r-cran-euclimatch Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 203 Depends: libc6 (>= 2.17), 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-rcppparallel, r-cran-terra, r-cran-rcolorbrewer Filename: pool/dists/resolute/main/r-cran-euclimatch_1.0.2-1.ca2604.1_arm64.deb Size: 66354 MD5sum: 0f4e6add40a65c0c76160bb4a6e014f7 SHA1: 11b197a55c1bd01e69200c161512798db821ccfa SHA256: 7e48490c18674aca56f00ec69cfb52ad1b96035d993ca56b8129b9b65dced22c SHA512: 2017abbddec54e3f89e5b0591054a8f7b7a3b3dd26b8f3c2011f2cf359e278406f1abeb693448bdcd143f8c42cdae6e25863273c9071817272ea770cfb730aed Homepage: https://cran.r-project.org/package=Euclimatch Description: CRAN Package 'Euclimatch' (Euclidean Climatch Algorithm) An interface for performing climate matching using the Euclidean "Climatch" algorithm. Functions provide a vector of climatch scores (0-10) for each location (i.e., grid cell) within the recipient region, the percent of climatch scores >= a threshold value, and mean climatch score. Tools for parallelization and visualizations are also provided. Note that the floor function that rounds the climatch score down to the nearest integer has been removed in this implementation and the “Climatch” algorithm, also referred to as the “Climate” algorithm, is described in: Crombie, J., Brown, L., Lizzio, J., & Hood, G. (2008). “Climatch user manual”. The method for the percent score is described in: Howeth, J.G., Gantz, C.A., Angermeier, P.L., Frimpong, E.A., Hoff, M.H., Keller, R.P., Mandrak, N.E., Marchetti, M.P., Olden, J.D., Romagosa, C.M., and Lodge, D.M. (2016). . Package: r-cran-eulerr Architecture: arm64 Version: 7.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2234 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gensa, r-cran-polyclip, r-cran-polylabelr, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-knitr, r-cran-lattice, r-cran-pbrackets, r-cran-rconics, r-cran-rmarkdown, r-cran-testthat, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-eulerr_7.1.0-1.ca2604.1_arm64.deb Size: 1606812 MD5sum: c384d712bd8fcfbb219d8a02f569f244 SHA1: ab7c1f86dd6b87aad39b619fa7f1e6da50610ecc SHA256: c1d256a87fad08ec2ed68465f2932f4a024acbd0da4ee46b0cd96b2e6bb733a9 SHA512: 249193ab9aad689365d309190ce798776b6d8df7b159ff188ab922d4838dbc7faaeaaa3ee4f9fc8faf431eccaeee52b6acb2494d1f6ca1ed1f42767d7c927361 Homepage: https://cran.r-project.org/package=eulerr Description: CRAN Package 'eulerr' (Area-Proportional Euler and Venn Diagrams with Ellipses) Generate area-proportional Euler diagrams using numerical optimization. An Euler diagram is a generalization of a Venn diagram, relaxing the criterion that all interactions need to be represented. Diagrams may be fit with ellipses and circles via a wide range of inputs and can be visualized in numerous ways. Package: r-cran-eummd Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 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 Filename: pool/dists/resolute/main/r-cran-eummd_0.2.0-1.ca2604.1_arm64.deb Size: 122696 MD5sum: 13587a7d3a4de40b8ebf1d217e12c83f SHA1: 67c23d16f2b41c9849de65518ffc996c2a49694c SHA256: c51bd2c974b27364e02135144ca26e48c95baed7811172ec63695895f77f5557 SHA512: 30cf29c6511dcf37fae06a442c7ed1fa0d843431f7d860a6be80b902d16bf43e90495744cdc5f2c75da9f770e2cd0d7742b800897e2626490222311eae692b2c Homepage: https://cran.r-project.org/package=eummd Description: CRAN Package 'eummd' (Efficient Univariate Maximum Mean Discrepancy) Computes maximum mean discrepancy two-sample test for univariate data using the Laplacian kernel, as described in Bodenham and Kawahara (2023) . The p-value is computed using permutations. Also includes implementation for computing the robust median difference statistic 'Q_n' from Croux and Rousseeuw (1992) based on Johnson and Mizoguchi (1978) . Package: r-cran-eurodata Architecture: arm64 Version: 1.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 206412 MD5sum: f2eb231a8d6bb9d931311481b8b4a2b1 SHA1: 3ac0bacf455cc330d0ed920139b26510ba34cab3 SHA256: 3be7d3f3c25762f83f05fd6fee5b4e7b45915a3b1ce4d38c35a99fbe0ad9cf38 SHA512: f67206ebe9a0ba7e883c275b4c18aa7e9c028419f4d28aadb63fd3d65604c85d2ca77ba5596893f84077e8064b3dc522474356210d3fc50f1971f940fb2c9954 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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Package: r-cran-evalr Architecture: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 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-testthat, r-cran-rmarkdown, r-cran-knitr, r-cran-tufte, r-cran-microbenchmark Filename: pool/dists/resolute/main/r-cran-evalr_0.0.1-1.ca2604.1_arm64.deb Size: 187596 MD5sum: 62ab9929481c39f14dd6dff98b56299e SHA1: 36894d63a92e738e569302a25100bcdc6e917efb SHA256: 2bb1e47311c1672cc734b12bdc4e598add1b50e9cd232d754f28f3fd49119026 SHA512: 6ebb41f22e24550797224aa616689d7763c69ddc25128b52b45009a7eac0807367a452bd7df268f3f293fba25569b5594b5553c3c75de2441940f7d67cac9af8 Homepage: https://cran.r-project.org/package=evalR Description: CRAN Package 'evalR' (Evaluation of Unverified Code) The purpose of this package is to generate trees and validate unverified code. 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Package: r-cran-evdbayes Architecture: arm64 Version: 1.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 832 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-evdbayes_1.1-3-1.ca2604.1_arm64.deb Size: 731294 MD5sum: 00135411c03fa4df958edde2110ca71f SHA1: 2371af93655f8000070db58c21e36262cbc0cc98 SHA256: a2c755ee40c0f5ea3e4611648fd8fc78f43a2fbd02bcaea08c0a4b6869d674b8 SHA512: 65e76dc67d54f6ae9f921fdd9797b34b1f81b28f3738784744364f07fabcb0f0f044a57abfbe46f9425a4c7e8cff1882671a92b249c742d9f36d68d30128d77a Homepage: https://cran.r-project.org/package=evdbayes Description: CRAN Package 'evdbayes' (Bayesian Analysis in Extreme Value Theory) Provides functions for the Bayesian analysis of extreme value models, using Markov chain Monte Carlo methods. Allows the construction of both uninformative and informed prior distributions for common statistical models applied to extreme event data, including the generalized extreme value distribution. Package: r-cran-eventglm Architecture: arm64 Version: 1.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1115 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-sandwich, r-cran-geepack Suggests: r-cran-testthat, r-cran-prodlim, r-cran-knitr, r-cran-rmarkdown, r-cran-rio, r-cran-data.table Filename: pool/dists/resolute/main/r-cran-eventglm_1.4.5-1.ca2604.1_arm64.deb Size: 457100 MD5sum: 55638744fb6badbee79f948a6cb7cd97 SHA1: 4889ba0eb9559680de766c43f6029912a24803a4 SHA256: d1a4043fde3549cc898013948f3edfebaa3062b013fc5e8bc67467b7fbe1df3e SHA512: 12ad64c8ff57c08111940d7b2e7b4ab97422930af12cb812a1368738d362984b1459b00f9a258f607540ac06579cc48e23ecb278b3f1715114695c77c9f8948d Homepage: https://cran.r-project.org/package=eventglm Description: CRAN Package 'eventglm' (Regression Models for Event History Outcomes) A user friendly, easy to understand way of doing event history regression for marginal estimands of interest, including the cumulative incidence and the restricted mean survival, using the pseudo observation framework for estimation. For a review of the methodology, see Andersen and Pohar Perme (2010) or Sachs and Gabriel (2022) . The interface uses the well known formulation of a generalized linear model and allows for features including plotting of residuals, the use of sampling weights, and corrected variance estimation. Package: r-cran-evesim Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 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-evesim_1.0.0-1.ca2604.1_arm64.deb Size: 119198 MD5sum: b4427b08011f0f0b5e473f110792216a SHA1: ec00468bb74e83a888f0d0e208304085b34adc8e SHA256: 7f9437cd79dc2bb908bbe83a92d53b9ebb26522a3aa3eebc054e7394a5af752b SHA512: 5da30a91e0182ab4f0649dbfea8fb3533802128894159c6e423db21debaa72fc0c5c96537b9085a62fc5c76a6ccceba00f8309a98a82255ecb8000334e823386 Homepage: https://cran.r-project.org/package=evesim Description: CRAN Package 'evesim' (Evolution Emulator: Species Diversification under anEvolutionary Relatedness Dependent Scenario) Evolutionary relatedness dependent diversification simulation powered by the 'Rcpp' back-end 'SimTable'. Package: r-cran-evgam Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1406 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-mgcv, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-evgam_1.0.1-1.ca2604.1_arm64.deb Size: 977700 MD5sum: 3ca3ce1bd0f65e68fd6440c48ae3abe6 SHA1: 721af10e058e34452027cb09f78b59a29ae5a888 SHA256: cb53b00bc5c0c813034b85a07244ee007464727793b64b2f06449a536fa5ca55 SHA512: af64c7af89b173a1bdb86dae164f48277a05b65bbe147b87f45fc6ca41badca0d0a1d65492271f7315cb825958bc4be4e5f528cab4a1521af2b757ddd9d312d8 Homepage: https://cran.r-project.org/package=evgam Description: CRAN Package 'evgam' (Generalised Additive Extreme Value Models) Methods for fitting various extreme value distributions with parameters of generalised additive model (GAM) form are provided. 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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Package: r-cran-evitaicossa Architecture: arm64 Version: 0.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1358 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-disordr, r-cran-rdpack 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-evitaicossa_0.0-1-1.ca2604.1_arm64.deb Size: 828506 MD5sum: d75acd337844ea096c672ca0f97ec52d SHA1: de028cb3853987bbf9872434dc33f1b425b20b35 SHA256: 39cde91a4cef93e5ab0cffc7451242a1f1b0826eca4851a6f1df7f07bc5ff562 SHA512: 5d3a205159f65cdc7226d66b44fced97c18cf64547dce63802b40f4c85511393d0a84d531023bb0157433d4738c0bf0c173b15404dfa09ca34d6c75ff8d25f47 Homepage: https://cran.r-project.org/package=evitaicossa Description: CRAN Package 'evitaicossa' (Antiassociative Algebra) Methods to deal with the free antiassociative algebra over the reals with an arbitrary number of indeterminates. Antiassociativity means that (xy)z = -x(yz). Antiassociative algebras are nilpotent with nilindex four (Remm, 2022, ) and this drives the design and philosophy of the package. Methods are defined to create and manipulate arbitrary elements of the antiassociative algebra, and to extract and replace coefficients. A vignette is provided. 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Melo D, Garcia G, Hubbe A, Assis A P, Marroig G. (2016) . 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Package: r-cran-exactmultinom Architecture: arm64 Version: 0.1.3-1.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-exactmultinom_0.1.3-1.ca2604.1_arm64.deb Size: 53744 MD5sum: 304241c2f5bfeb1c178b87307be49f5a SHA1: 01c81a9acf72be321a0f7bc4bb8684afc49a7606 SHA256: 87c59174c97c2c70031fbf97f7a08996aab8bd30f24caef2d5bac8091680cc6c SHA512: e7ae4ab93f43c1d19f35b17ea4bd467dd26ea5e4d944044c755ac54d45e50d26799743900e1e002abd312f67ab90a3de88958f254c5ead6585f38b963b6e601f Homepage: https://cran.r-project.org/package=ExactMultinom Description: CRAN Package 'ExactMultinom' (Multinomial Goodness-of-Fit Tests) Computes exact p-values for multinomial goodness-of-fit tests based on multiple test statistics, namely, Pearson's chi-square, the log-likelihood ratio and the probability mass statistic. Implements the algorithm detailed in Resin (2023) . Estimates based on the classical asymptotic chi-square approximation or Monte-Carlo simulation can also be computed. Package: r-cran-exactranktests Architecture: arm64 Version: 0.8-37-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-survival Filename: pool/dists/resolute/main/r-cran-exactranktests_0.8-37-1.ca2604.1_arm64.deb Size: 149774 MD5sum: d1b902d8c0953d1c04403bd0ebc91ddc SHA1: 713a58078f210ff4cb63b493ed7a9c2d55d763ae SHA256: 1c78b98a9b8dd34c01f20dcf363585ddab665f9ce14c2dca0a56691c1a224782 SHA512: c8a8ae553402d9b211557041b9587e3a2b800bdd1983a37bc6e478c9c25c927a4d935467267ebef979baffd2bce975c3f60e5fd55aa6808440518854b54fd6ff Homepage: https://cran.r-project.org/package=exactRankTests Description: CRAN Package 'exactRankTests' (Exact Distributions for Rank and Permutation Tests) Computes exact conditional p-values and quantiles using an implementation of the Shift-Algorithm by Streitberg & Roehmel. Package: r-cran-exactvartest Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 328 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-bench, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-ggplot2, r-cran-xts, r-cran-quantmod, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-exactvartest_0.1.3-1.ca2604.1_arm64.deb Size: 131592 MD5sum: be1ec707708a62abc26222d47ce6bf42 SHA1: 4d17f03f47eaba8e0fda0b17d7d9d536fa5dd9b0 SHA256: a0d1b006db8d86a876339fb1c3979dd4c8f028943fb3fba13c41ed9dde1d419a SHA512: 7efc2fde8d86c1937faf930e429277430dc2119425cd73aa2fd339c6a4d9cf7e337ddb69c4eee6adc73833c92d76aa702de061774a5c6cca2c5ff8634b299e28 Homepage: https://cran.r-project.org/package=ExactVaRTest Description: CRAN Package 'ExactVaRTest' (Exact Finite-Sample Value-at-Risk Back-Testing) Provides fast dynamic-programming algorithms in 'C++'/'Rcpp' (with pure 'R' fallbacks) for the exact finite-sample distributions and p-values of Christoffersen (1998) independence (IND) and conditional-coverage (CC) VaR backtests. For completeness, it also provides the exact unconditional-coverage (UC) test following Kupiec (1995) via a closed-form binomial enumeration. See Christoffersen (1998) and Kupiec (1995) . Package: r-cran-exametrika Architecture: arm64 Version: 1.13.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3927 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_arm64.deb Size: 2734820 MD5sum: f9f553ef89555fa3ed1939ccd208beba SHA1: 0ad2a9c68deb412ccf5f9526ebe7990e6188fe96 SHA256: d877967d5819f38db3d3b297ae52d988dd215adc667d9786517fd5fc6e034004 SHA512: a7c74d4f50e028dab0fdd1a50384611f83029387689e45262077299aae12c140a6ea5356b82b7bfdbd0b5cc3267e5ccc6048e0ad74ae0a5aab0b6f77413ec89d Homepage: https://cran.r-project.org/package=exametrika Description: CRAN Package 'exametrika' (Test Theory Analysis and Biclustering) Implements comprehensive test data engineering methods as described in Shojima (2022, ISBN:978-9811699856). Provides statistical techniques for engineering and processing test data: Classical Test Theory (CTT) with reliability coefficients for continuous ability assessment; Item Response Theory (IRT) including Rasch, 2PL, and 3PL models with item/test information functions; Latent Class Analysis (LCA) for nominal clustering; Latent Rank Analysis (LRA) for ordinal clustering with automatic determination of cluster numbers; Biclustering methods including infinite relational models for simultaneous clustering of examinees and items without predefined cluster numbers; and Bayesian Network Models (BNM) for visualizing inter-item dependencies. Features local dependence analysis through LRA and biclustering, parameter estimation, dimensionality assessment, and network structure visualization for educational, psychological, and social science research. Package: r-cran-excursions Architecture: arm64 Version: 2.5.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 829 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 565998 MD5sum: e9d5f7a6949591e95628ada597abbf6a SHA1: d4f76a0906dafafc23f691e17642a98aea781abb SHA256: 1200c9e3d03a6b706a4bc32b12e346c30bed2ba34f6b97af525f2ec9effad03b SHA512: f45789261d79d03843846de26d12b1ecd9b08766248dedeb5127c78011849eb14870c4f973277cee46b0702ed7000faeff9ca7ec2f0fb6c22c8d36bc6597aaee Homepage: https://cran.r-project.org/package=excursions Description: CRAN Package 'excursions' (Excursion Sets and Contour Credibility Regions for Random Fields) Functions that compute probabilistic excursion sets, contour credibility regions, contour avoiding regions, and simultaneous confidence bands for latent Gaussian random processes and fields. The package also contains functions that calculate these quantities for models estimated with the INLA package. The main references for excursions are Bolin and Lindgren (2015) , Bolin and Lindgren (2017) , and Bolin and Lindgren (2018) . These can be generated by the citation function in R. Package: r-cran-exdex Architecture: arm64 Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1037 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_arm64.deb Size: 688358 MD5sum: 39f1cb01ec0da6fcb1e3466436c492d9 SHA1: 6eaaa1a4e0d0ad61c3bc2235b2a695fa63eafecb SHA256: 21110a124ff507caf66a8cbf94c488e5714ee96e63c34ce38f5af84ab2776b60 SHA512: f45df425ebedad06b5b92b61c5fef7a98cdbb8930bf77a5050127407d06db7726a9f0f51109c5293890395096cc2ec7d835b7ea43ebe702106e43cbe996b5bc8 Homepage: https://cran.r-project.org/package=exdex Description: CRAN Package 'exdex' (Estimation of the Extremal Index) Performs frequentist inference for the extremal index of a stationary time series. Two types of methodology are used. One type is based on a model that relates the distribution of block maxima to the marginal distribution of series and leads to the semiparametric maxima estimators described in Northrop (2015) and Berghaus and Bucher (2018) . Sliding block maxima are used to increase precision of estimation. A graphical block size diagnostic is provided. The other type of methodology uses a model for the distribution of threshold inter-exceedance times (Ferro and Segers (2003) ). Three versions of this type of approach are provided: the iterated weight least squares approach of Suveges (2007) , the K-gaps model of Suveges and Davison (2010) and a similar approach of Holesovsky and Fusek (2020) that we refer to as D-gaps. For the K-gaps and D-gaps models this package allows missing values in the data, can accommodate independent subsets of data, such as monthly or seasonal time series from different years, and can incorporate information from right-censored inter-exceedance times. Graphical diagnostics for the threshold level and the respective tuning parameters K and D are provided. Package: r-cran-exdqlm Architecture: arm64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1929 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1504866 MD5sum: 11f3b580196bbc80801a571b6d35813d SHA1: 5931b071e3c0e9ba9c90c057c8ae682813b6536c SHA256: a4be9059651552f920136e03498bc59db5c0c876c9c7a04b948acb6c7b79884d SHA512: 4d9499086a78cd651ceb7ff5973cbfa302fba4b0bc0d6f09104570d92504b38e0fa0267bc260a49f34bc61dab722725eea71594d476ff3fad9ec93452e389672 Homepage: https://cran.r-project.org/package=exdqlm Description: CRAN Package 'exdqlm' (Extended Dynamic Quantile Linear Models) Bayesian quantile-regression routines for dynamic state-space models and static regression under the extended asymmetric Laplace (exAL) error distribution. The dynamic state-space models are extended dynamic quantile linear models (exDQLMs). The package combines dynamic exDQLM inference via LDVB, MCMC, and legacy ISVB with static exAL regression via LDVB and MCMC, reduced AL/DQLM paths through fixed skewness, component builders for trend/seasonality/regression blocks, static shrinkage priors including ridge, regularized horseshoe, and 'rhs_ns', evidence lower bound diagnostics, optional C++ accelerators, and posterior predictive synthesis across separately fitted quantiles through 'quantileSynthesis()'. Dynamic exDQLM methods are described in Barata et al. (2020) . Package: r-cran-exhaustivesearch Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 287 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-mlbench Filename: pool/dists/resolute/main/r-cran-exhaustivesearch_1.0.2-1.ca2604.1_arm64.deb Size: 105836 MD5sum: 9aba17c6d897d93464b65798dd11f265 SHA1: 9bbce0c5ee5e418e8742fea9d05c307bf9e94d4f SHA256: 73bd0939e5716df481ad895646a90eabf7474ca5099337cd95ebd12c04c752f4 SHA512: 853e565380542a1d824bd3b8b7e0f1e797a746c824239b56a614605713ac8a43b7c5e34690ad4b38aacf6ff4fc141c8ee1ae666b1666fdbd30e29760b59d82a7 Homepage: https://cran.r-project.org/package=ExhaustiveSearch Description: CRAN Package 'ExhaustiveSearch' (A Fast and Scalable Exhaustive Feature Selection Framework) The goal of this package is to provide an easy to use, fast and scalable exhaustive search framework. Exhaustive feature selections typically require a very large number of models to be fitted and evaluated. Execution speed and memory management are crucial factors here. This package provides solutions for both. Execution speed is optimized by using a multi-threaded C++ backend, and memory issues are solved by by only storing the best results during execution and thus keeping memory usage constant. Package: r-cran-exif Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2840 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2716984 MD5sum: 13c34c2a8e981e0aded54ee8ed0738f1 SHA1: 3676480e1bfcd39f2ed708e909a761defbfb0c1c SHA256: e8686542059167be651fdbce9f0bbe5c1c04b5dff2ea4621bab6e37f6780e4e8 SHA512: 9517102c601afe4b2292552315672ee034e4da4cac0345228d43b04ea91cfc93210da202156cd16b7b7c967ef50357a2e77027f004b0c90dc4a1ef9580ab92fd 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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Package: r-cran-experiment Architecture: arm64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 503 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-mass Filename: pool/dists/resolute/main/r-cran-experiment_1.2.1-1.ca2604.1_arm64.deb Size: 358720 MD5sum: 8afea97d67d0dd692d34a03c2f327577 SHA1: cf89e6ee8cf9a9c22d4a68ba5cfde235f2f3bbe5 SHA256: b1e15f1f677bac72081c9295a2b32fdbdbddfc47d67ed93647a3cb29a13995b6 SHA512: db243ba26bca0f7fe70c20fd6652d2ad5bb0d811005d5d3fc8fa95e2ba2b45617191c2b74431dce17ea02fc8aad42c13e04064f308a622577acd87377e4e12fa Homepage: https://cran.r-project.org/package=experiment Description: CRAN Package 'experiment' (R Package for Designing and Analyzing Randomized Experiments) Provides various statistical methods for designing and analyzing randomized experiments. 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Expert Opinion can be provided on the survival probabilities at certain time-point(s) or for the difference in mean survival between two treatment arms. Please reference it's use as Cooney, P., White, A. (2023) . 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The package also gives easy access to the underlying C routines through an API; see the package vignette for details. A test package included in sub-directory example_API provides an implementation. C routines derived from the GNU Scientific Library . 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Package: r-cran-expperm Architecture: arm64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 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-expperm_1.6-1.ca2604.1_arm64.deb Size: 76302 MD5sum: f3cac7d7926a74bb082226926349aade SHA1: 3e5fe76205dc6a720af44d2eabfb1f27ad97bd50 SHA256: c681fa4ce80a38afb65d7756f7e5ec9534f485d14638c0a0d8931ab39f74d268 SHA512: 5af8f665fa531d3de00fd0a8291fe14a84aae7c522b299a1bd8d3d99034be9ee1fce80f33371656a88a623f9467a513c60867ce33c5b35053f5a50bff463b478 Homepage: https://cran.r-project.org/package=expperm Description: CRAN Package 'expperm' (Computing Expectations and Marginal Likelihoods for Permutations) A set of functions for computing expected permutation matrices given a matrix of likelihoods for each individual assignment. 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This package implements the methodology described by R. Rastelli and M. Fop (2020) . Package: r-cran-extbatchmarking Architecture: arm64 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_arm64.deb Size: 138206 MD5sum: 6c64a00353b724f15dc3b827ab31747f SHA1: 2a32ce1b0055c92edfc4ea648fb4a025676014f3 SHA256: 9a4e429ff46d53b05313b22484a6633229de864d4bf04eb23e0b9ae7b628be62 SHA512: 91f394f0d436666c461bbc77588ff09da267e58adfd5de5bb08cd6ab1bb3fc7b13a723b2396e4ffa7ce10acb89729ba249134509d14f844f4f8fc73c06e16802 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. 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Package: r-cran-extradistr Architecture: arm64 Version: 1.10.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1858 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_arm64.deb Size: 594882 MD5sum: a276c340966451b3871ee179a680bdfe SHA1: 6ed86470136e0b508497282675e0814ee66d5fb4 SHA256: f37539a11f2d601ed0e2c8f03bd52b65676b00fe91de86c77b24c4d95d544770 SHA512: 887769d73a08a117c227f9770475be976a0fd993b6501eed324e2d64dd723415125dd6567dfe182ce2f1894c857f384b872147991fab07c30225944d8cd84210 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: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1405 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_arm64.deb Size: 1288628 MD5sum: 8e79aaf5ca1a6d65a3fa63433204bcd2 SHA1: aaadce7ffde3e1ab951d122db5b52958ec0a7e1c SHA256: 72312d5dd4002baf402f93cdd364c9861370e99fb05f80cbd6670311edf7032d SHA512: c996278c6484abd0ebdc8183ac525bfe02048e38c2f3482f09de93da90b897f4e0157454e57f7b1a4460acc2a6ad2047fb34eb5d41ff30e6672fd28b99b2273b 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. The statistical inference is performed with non-parametric estimators, likelihood-based estimators and Bayesian techniques. It adapts the methodologies of Beranger and Padoan (2015) , Marcon et al. (2016) , Marcon et al. (2017) , Marcon et al. (2017) and Beranger et al. (2021) . This package also allows for the modelling of spatial extremes using flexible max-stable processes. It provides simulation algorithms and fitting procedures relying on the Stephenson-Tawn likelihood as per Beranger at al. (2021) . Package: r-cran-extrememix Architecture: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1188 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-ggplot2, r-cran-gridextra, r-cran-mixtools, r-cran-rcpp, r-cran-rcppprogress, r-cran-threshr Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-extrememix_0.0.1-1.ca2604.1_arm64.deb Size: 827678 MD5sum: 513f8480bcaea16b2ead49253f2d8ab3 SHA1: 540797b5fc5a17fc7ad755673e5b37aa44428504 SHA256: 0a4f1daee238dcd616d500e9b580686b92eee6731861d05f9af2dbacfe6d8d7a SHA512: a3e773969c84c0fd6465f9c125559a16b62c6c99c491fdc2bf3166dd2f387a41d7f4eccb3c119c243b01ab5021b30327c11acffde5b1223c708ce81565595091 Homepage: https://cran.r-project.org/package=extrememix Description: CRAN Package 'extrememix' (Bayesian Estimation of Extreme Value Mixture Models) Fits extreme value mixture models, which are models for tails not requiring selection of a threshold, for continuous data. It includes functions for model comparison, estimation of quantity of interest in extreme value analysis and plotting. Reference: CN Behrens, HF Lopes, D Gamerman (2004) . FF do Nascimento, D. Gamerman, HF Lopes . Package: r-cran-extremerisks Architecture: arm64 Version: 0.0.6-1.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_arm64.deb Size: 594876 MD5sum: df177b7012400d0bc69161bc3ffe6173 SHA1: fa61dc6cda18d21561c9a468a61ace952ab89b3d SHA256: f94142c0d5f0ae8e5be4c2f43a05538226b62673070a25175a35f6ac1785960a SHA512: 70a672c041fa08dc9ce85bdbdb42c9b1c688e2455d8b8c10afe56e79ccbfa69b1957713cf1b8a2b46e3e42211b4a3a87f818a8d35646c6971b94d2ab3b587556 Homepage: https://cran.r-project.org/package=ExtremeRisks Description: CRAN Package 'ExtremeRisks' (Extreme Risk Measures) A set of procedures for estimating risks related to extreme events via risk measures such as Expectile, Value-at-Risk, etc. is provided. Estimation methods for univariate independent observations and temporal dependent observations are available. The methodology is extended to the case of independent multidimensional observations. The statistical inference is performed through parametric and non-parametric estimators. Inferential procedures such as confidence intervals, confidence regions and hypothesis testing are obtained by exploiting the asymptotic theory. Adapts the methodologies derived in Padoan and Stupfler (2022) , Davison et al. (2023) , Daouia et al. (2018) , Drees (2000) , Drees (2003) , de Haan and Ferreira (2006) , de Haan et al. (2016) , Padoan and Rizzelli (2024) , Daouia et al. (2024) . Package: r-cran-extremis Architecture: arm64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 553 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 444840 MD5sum: f27bae3fbe06056b325000c162f29478 SHA1: 4b4e39aa1a4feef34ce2c8b6a2c872e0b9950be4 SHA256: fd63cd77068de8156fbcc98a31fb4b2298c30a20515e47c350d1828c1560e944 SHA512: 68fe0f4e74f681cd91b48082bd4430ec589c01b1ee9ca80ac9d5c252c8064b581715df6f177fb80a4cff38c9900e4164ddd8b7c8e61b933b7dcfff6268472286 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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Package: r-cran-fabmix Architecture: arm64 Version: 5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 909 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_arm64.deb Size: 739596 MD5sum: 0457c7f8538ffd89c6864a1e9414082c SHA1: 0644df93a726f69acf750f211ef097bc5a0712cf SHA256: 90d53e5f4bc5e84895124c2f9a88b20e86c5550c3f205971f0424ebea2a125cc SHA512: 5b87ac227b3134be01c8402e12958b73b6e8076db9f47e5b583a979a124334f067e64b7baa0e01e45f401e4254b581c618588273044dac281142bdb5adc630c5 Homepage: https://cran.r-project.org/package=fabMix Description: CRAN Package 'fabMix' (Overfitting Bayesian Mixtures of Factor Analyzers withParsimonious Covariance and Unknown Number of Components) Model-based clustering of multivariate continuous data using Bayesian mixtures of factor analyzers (Papastamoulis (2019) (2018) ). The number of clusters is estimated using overfitting mixture models (Rousseau and Mengersen (2011) ): suitable prior assumptions ensure that asymptotically the extra components will have zero posterior weight, therefore, the inference is based on the ``alive'' components. A Gibbs sampler is implemented in order to (approximately) sample from the posterior distribution of the overfitting mixture. A prior parallel tempering scheme is also available, which allows to run multiple parallel chains with different prior distributions on the mixture weights. These chains run in parallel and can swap states using a Metropolis-Hastings move. Eight different parameterizations give rise to parsimonious representations of the covariance per cluster (following Mc Nicholas and Murphy (2008) ). The model parameterization and number of factors is selected according to the Bayesian Information Criterion. Identifiability issues related to label switching are dealt by post-processing the simulated output with the Equivalence Classes Representatives algorithm (Papastamoulis and Iliopoulos (2010) , Papastamoulis (2016) ). 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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). 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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. 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The method is described in Dahl, Johnson, and Andros (2023) "Comparison and Bayesian Estimation of Feature Allocations" . 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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'. 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Package: r-cran-fastbandchol Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 62794 MD5sum: fae9153651b56b4bab7bece57b6617a9 SHA1: ba133eeb593fbfa5bed78d9c3fb0bb5a88a140af SHA256: b303bfed918b0eb42c22a15fc033d6424af316178c4fc10481bdd363fe4653e1 SHA512: 70abdbef2d60415f5cc83deee229833689b7536a5c5946c7157e078cdf8079821db2cb0dc0e8d47f6bc52670d84a1ed2a23b6a328a0801430b2a0c29f973c052 Homepage: https://cran.r-project.org/package=FastBandChol Description: CRAN Package 'FastBandChol' (Fast Estimation of a Covariance Matrix by Banding the CholeskyFactor) Fast and numerically stable estimation of a covariance matrix by banding the Cholesky factor using a modified Gram-Schmidt algorithm implemented in RcppArmadilo. See for details on the algorithm. Package: r-cran-fastbeta Architecture: arm64 Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 353 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 235598 MD5sum: 4ff1af43cffd6d89ba80a022c9290a0b SHA1: d186aa1f2ab5ccb16b2dbee3e6ad9266f7abf18d SHA256: 5c6a22c5a9ece2c295f7c1e80e94659b2648c25f4563d9e42e22e9b42cfe2698 SHA512: 38d0752019983aaf5b47421e9885ca9cb2d4c883b3d0d13a5816112ef241a0c9715c02c0324257fce26b29f473ed640599785782ed7c2f778300da673d765646 Homepage: https://cran.r-project.org/package=fastbeta Description: CRAN Package 'fastbeta' (Fast Approximation of Time-Varying Infectious DiseaseTransmission Rates) A fast method for approximating time-varying infectious disease transmission rates from disease incidence time series and other data, based on a discrete time approximation of an SEIR model, as analyzed in Jagan et al. (2020) . Package: r-cran-fastcluster Architecture: arm64 Version: 1.3.0-1.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_arm64.deb Size: 180810 MD5sum: 291034536d739320c95419645a5a79ae SHA1: b5d8d068a01c8e09da9398950d8ce1439bd3bcfa SHA256: 8e9da80c98a2d5a33618b742c5b53c612815c23adacb95e802cc9146f2dd2b44 SHA512: a9252de6d8ccbe20f7bd5cf51f7a09259a153f8efed0ee3b1dfaa4eb08c4c85337d6cabb891072fafc91d874ca246b75082d50d5112c7ba6529c39b3e8edc9bf 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-fastcox Architecture: arm64 Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 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_arm64.deb Size: 128110 MD5sum: abdb1b93e3ff89732c10035fb759c746 SHA1: 1a252a761e0b201715cf2262d125794974a0ee07 SHA256: a97bed6efbb95535d87cc7697313ffd2cb0cd06303483e654a1842781d54e2d0 SHA512: a0dc39fdf5c71ce63e55239e0c6295335b104d0990ed195191790ac212a7a742096b632c526199e370590e38dc752e8b1a08410dd1460050faefa347112ba79b Homepage: https://cran.r-project.org/package=fastcox Description: CRAN Package 'fastcox' (Lasso and Elastic-Net Penalized Cox's Regression in HighDimensions Models using the Cocktail Algorithm) We implement a cocktail algorithm, a good mixture of coordinate decent, the majorization-minimization principle and the strong rule, for computing the solution paths of the elastic net penalized Cox's proportional hazards model. The package is an implementation of Yang, Y. and Zou, H. (2013) . Package: r-cran-fastcpd Architecture: arm64 Version: 0.16.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6936 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_arm64.deb Size: 4395840 MD5sum: 13052b289f54ed96f026c578eb3d55f6 SHA1: e95b74b32278ebd3348278568290a6460ccaec1b SHA256: 5763ee07bddeb7718d9e79314c808499dde78967f769c2b974f61e711436bdfb SHA512: b19b4d1da48aee5aad1c475dc59a23efa24ec575e09d1b017be65c52a29f05ef5d2440cc235ec6a68d09f35d74be33442f1cd46b440b787f356c9d3dbd8062c9 Homepage: https://cran.r-project.org/package=fastcpd Description: CRAN Package 'fastcpd' (Fast Change Point Detection via Sequential Gradient Descent) Implements fast change point detection algorithm based on the paper "Sequential Gradient Descent and Quasi-Newton's Method for Change-Point Analysis" by Xianyang Zhang, Trisha Dawn . The algorithm is based on dynamic programming with pruning and sequential gradient descent. It is able to detect change points a magnitude faster than the vanilla Pruned Exact Linear Time(PELT). The package includes examples of linear regression, logistic regression, Poisson regression, penalized linear regression data, and whole lot more examples with custom cost function in case the user wants to use their own cost function. Package: r-cran-fastei Architecture: arm64 Version: 0.0.19-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1949 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1532138 MD5sum: 3d59d893edb667014cea2fdf22d506ff SHA1: 2889df50d73424e7704d6a05245f8052bc3114ac SHA256: e67795363f58f2785a825e90ac80d84cc6b06040df404023b258493d888fccdf SHA512: a218705dcc20fe60772b4c64bb11d2b0fe1a78e1ab9ed8980713659e64af846791bef37ebc547ff1be8554bac66f52543e02257e714d14d54ecc0ad4620f347f 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: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 743 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 431592 MD5sum: 73cc05ea7e5b9a9adc7365a8a8b6e25c SHA1: af4267183a705c913ea765012674c0c8766682de SHA256: f42e7034a5ca542d0b748307a5396af03e04ff3cc92875e01176b48979c298c6 SHA512: 50ae0b05985edaec8f71d1c7e4c6c325f1c847e63323ab9cb6fc5b3144b4457474497825aedc9e41a0b1725d1792cefce591fe6ebc4df385383bff42e22a7cec Homepage: https://cran.r-project.org/package=fasterize Description: CRAN Package 'fasterize' (Fast Polygon to Raster Conversion) Provides a drop-in replacement for rasterize() from the 'raster' package that takes polygon vector or data frame objects, and is much faster. There is support for the main options provided by the rasterize() function, including setting the field used and background value, and options for aggregating multi-layer rasters. Uses the scan line algorithm attributed to Wylie et al. (1967) . Package: r-cran-fastgasp Architecture: arm64 Version: 0.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1113 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_arm64.deb Size: 658928 MD5sum: eb1f18cbb9da531c7ed339260d2917be SHA1: c16e363a3629c37b04db3277947a787090619917 SHA256: 9bc7906f699b1b052bc2c85acea69987e0ab454fe800b3b275d4f4625b07843c SHA512: 057765ba53208a80993fc4bfcdbcb7724927570c5605293a88a3bd3988f84d1990445dccb7eec3a0013276347666338581a4fe26d6b705ae27cf519f9d9feefb Homepage: https://cran.r-project.org/package=FastGaSP Description: CRAN Package 'FastGaSP' (Fast and Exact Computation of Gaussian Stochastic Process) Implements fast and exact computation of Gaussian stochastic process with the Matern kernel using forward filtering and backward smoothing algorithm. It includes efficient implementations of the inverse Kalman filter, with applications such as estimating particle interaction functions. These tools support models with or without noise. Additionally, the package offers algorithms for fast parameter estimation in latent factor models, where the factor loading matrix is orthogonal, and latent processes are modeled by Gaussian processes. See the references: 1) Mengyang Gu and Yanxun Xu (2020), Journal of Computational and Graphical Statistics; 2) Xinyi Fang and Mengyang Gu (2024), ; 3) Mengyang Gu and Weining Shen (2020), Journal of Machine Learning Research; 4) Yizi Lin, Xubo Liu, Paul Segall and Mengyang Gu (2025), . Package: r-cran-fastgeojson Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1194 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-jsonlite, r-cran-sf, r-cran-leaflet Filename: pool/dists/resolute/main/r-cran-fastgeojson_0.1.3-1.ca2604.1_arm64.deb Size: 830672 MD5sum: 317117c236e1b8b92f6aaa674b2b434d SHA1: 9ddefcf71ce7355a9f78b7f33df854080e042d85 SHA256: 4cc8193cfe15ddd5045a4daffb07b56a6887fce8222405d7f49bdbf2fdfdcf5e SHA512: dbb5e68a5ed2263fa3f72f034b2d20c4f3244b38f7b460374a609e79ca407f98acb1d6e0c5af15d95c873249a28492af7da4f6aba9b06c92b0dce17c399e90a6 Homepage: https://cran.r-project.org/package=fastgeojson Description: CRAN Package 'fastgeojson' (High-Performance 'GeoJSON' and 'JSON' Serialization) Converts R data frames and 'sf' spatial objects into 'JSON' and 'GeoJSON' strings. The core encoders are implemented in 'Rust' using the 'extendr' framework and are designed to efficiently serialize large tabular and spatial datasets. Returns serialized 'JSON' text, allowing applications such as 'shiny' or web APIs to transfer data to client-side 'JavaScript' libraries without additional encoding overhead. Package: r-cran-fastghquad Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 49892 MD5sum: 5ae268d9dae5eed684710a4d7e67b877 SHA1: 9b04da5c84e3308fa5ab5c526673aac912314bbf SHA256: dae6b4d1d1472dba4c31d6852a64ab81c1d5db5daccce28b85514a6f31c9cbcd SHA512: 0af1d5334e3d60000ee56f4a058659caa2d7e86e3cc5c9cd2728cf25625657ff059b2d44514815aaf1f9d1da39c2e44129c6f209b9b17d134aa9feebdd0c5fde Homepage: https://cran.r-project.org/package=fastGHQuad Description: CRAN Package 'fastGHQuad' (Fast 'Rcpp' Implementation of Gauss-Hermite Quadrature) Fast, numerically-stable Gauss-Hermite quadrature rules and utility functions for adaptive GH quadrature. See Liu, Q. and Pierce, D. A. 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Package: r-cran-fastglcm Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4609 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 4256822 MD5sum: 08eee533c253d3d5c7364e5ac90fd297 SHA1: 34db9bceae03b6cba6b20120234f97dbbf69a1ff SHA256: 0f3aa1754c80b50976fd637313377cc07ef7434bf216391b8aab228b6012846a SHA512: dd329bffe8bed25c765bbcb30538c1aea2133a93e38c7b9b6497a66dac4aa4de18ee5c3ec7255f3a69a76baf1293960bd63e87ccd1f4d76e2f76186c11400238 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, . Package: r-cran-fastglm Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2236 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-cran-bigmemory, r-cran-matrix, r-cran-formula, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-glm2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-sandwich, r-cran-microbenchmark, r-cran-mass, r-cran-statmod, r-cran-logistf, r-cran-pscl, r-cran-speedglm, r-cran-tweedie Filename: pool/dists/resolute/main/r-cran-fastglm_0.1.0-1.ca2604.1_arm64.deb Size: 895700 MD5sum: 4ab5ee1fff8aad409374ffbeebfb045a SHA1: bba77d8a7061a5aec56a66133d6dc63fe1fad0bc SHA256: 66b23493cad6049d970a1d9e8e691c0d9a83b3196d89f4f3b3e1eba6587a3bb2 SHA512: b2ad8a4615731d052f41680c8c6b94478dec97ee4c5eac4a98b062f76dec21371b221d7da221135d468490438fa9ed68ddc57883e2391b1293dfb978a5adbbe1 Homepage: https://cran.r-project.org/package=fastglm Description: CRAN Package 'fastglm' (Fast and Stable Fitting of Generalized Linear Models using'RcppEigen') Fits generalized linear models efficiently using 'RcppEigen'. 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Package: r-cran-fastgp Architecture: arm64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 586 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 401700 MD5sum: a8268553fe6bbfa43b5d34a592ec88ec SHA1: 84a424145675404e0be5aa13a9e2fb7a4f84b5b0 SHA256: 28c9b6c6323bf0219537ee21fc2530c81bd76e4505919e1c184b8e82df761797 SHA512: e35891615bbd2513dc725daedd43dac316aa60680258b30f2d05cfc0ae6fc4b273093964d060714ba40ae909dc4d843ff8dc267800ff2a3dafbcd74fbd7c9b3c Homepage: https://cran.r-project.org/package=FastGP Description: CRAN Package 'FastGP' (Efficiently Using Gaussian Processes with Rcpp and RcppEigen) Contains Rcpp and RcppEigen implementations of matrix operations useful for Gaussian process models, such as the inversion of a symmetric Toeplitz matrix, sampling from multivariate normal distributions, evaluation of the log-density of a multivariate normal vector, and Bayesian inference for latent variable Gaussian process models with elliptical slice sampling (Murray, Adams, and MacKay 2010). Package: r-cran-fasthamming Architecture: arm64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 14474 MD5sum: 5597e5434e72da8eff5b321b987c9253 SHA1: e2c6f618ae2ebc45523b47d451d3c9fbd5c13997 SHA256: c1d802517307129213ab645f73800e97fffdad632144c2497fc8da6237ff28fd SHA512: 27d664cc0f6984d38a23e1c95be5c816e2b2a9ba14b2f74e587fe30a8f01dfda08dbddbde9d46cc562329e391964212ad7e147868bc8d3eed0569bb2c58aea6f Homepage: https://cran.r-project.org/package=FastHamming Description: CRAN Package 'FastHamming' (Fast Computation of Pairwise Hamming Distances) Pairwise Hamming distances are computed between the rows of a binary (0/1) matrix using highly optimized 'C' code. 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Package: r-cran-fastica Architecture: arm64 Version: 1.2-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-fastica_1.2-7-1.ca2604.1_arm64.deb Size: 42280 MD5sum: d16edad6f9a661ab503a400ffac07bb2 SHA1: 865ae24a5277580ef7a9cad7825b951f8248c14f SHA256: a0187a13866592e9b5eac3f64362d6aa52bc81eb0a84a2767eea2906e51152c8 SHA512: fe0443db5de5ae76d03265213d2f768b8537a6eeeb251fb7311dbd6daf4baf8840e537c5bde909f3cddef8c18f2cd44b1b244f1dfbed40eb0a6e3046b0efea63 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: arm64 Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3137 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_arm64.deb Size: 1407638 MD5sum: f31829de06ae6a0a6582e5121c680639 SHA1: 8099e91ef9359ba8a5f5ac6d4b6a0532057f83bb SHA256: 0e410578b9fed21b044f980ba3c2339c1dec34fc17594e535af1c416ffa5fa32 SHA512: a16ac6f87a63598bd9ff3c3449d7cea116f611753c1896fceba75566276cf3f5ac95dede8913d7d8b6df8a07a631fb0131b945ddbda48987b978a98c16946e33 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: arm64 Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 372772 MD5sum: f42e562db6c626699c515084737c9fc4 SHA1: 23b95f4894c74606f7e99ef385de19b7d6fd09b2 SHA256: 1fdf95864a38313f4d423701c3969f3cc10ef330e98948e981f9dcd269242f1c SHA512: f7b83a8dce5c3610d4376a6152e6e543625ce81151a6c35bc30196b3b6496c2099adf543c43deb01719e89477d373e13ca764f94c87c0b7b7e0a98aca30d8d81 Homepage: https://cran.r-project.org/package=fastJT Description: CRAN Package 'fastJT' (Efficient Jonckheere-Terpstra Test Statistics for Robust MachineLearning and Genome-Wide Association Studies) This 'Rcpp'-based package implements highly efficient functions for the calculation of the Jonckheere-Terpstra statistic. 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Package: r-cran-fastkmedoids Architecture: arm64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 253 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 80676 MD5sum: 3170771941ff404d606260deb94858b0 SHA1: 1b70e789078e2ddd7a6b34b9237a2a9c3041beab SHA256: 861c7d1f5cd6c6a3f08b7552c2ce04e2d8281b2d96ffe552ef85ae0dda133476 SHA512: 442dddbed7009f523425d6933f32b04b8bfa5d39c39aee78ee5df9989b55ea838ea70c758d3abc5525b9d3c454df84c43411643d2740543a6682d2ba07d8c3a0 Homepage: https://cran.r-project.org/package=fastkmedoids Description: CRAN Package 'fastkmedoids' (Faster K-Medoids Clustering Algorithms: FastPAM, FastCLARA,FastCLARANS) R wrappers of C++ implementation of Faster K-Medoids clustering algorithms (FastPAM, FastCLARA and FastCLARANS) proposed in Erich Schubert, Peter J. Rousseeuw 2019 . Package: r-cran-fastkqr Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), 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_arm64.deb Size: 94170 MD5sum: 46a3ccb4a07a614112423aeebf1e57e2 SHA1: 943941c9fec103efa487657bf4e5b3afbe2ecad4 SHA256: 7ea5cbcf99009e89ce37611f9386ef8b2b6880e8527533e371335fac5084565b SHA512: 78a8d4c778792cf570efb720b41e174fefe47bda380264e32679e0366a3da58a14fd033929838b6be7264553e41b8f6d00d50068c49ba8c96ae6479ff8e3064e 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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Package: r-cran-fastkrr Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 628 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cvst, r-cran-generics, r-cran-parsnip, r-cran-rcpp, r-cran-rlang, r-cran-tibble, r-cran-ggplot2, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dials, r-cran-tidymodels, r-cran-modeldata, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-fastkrr_0.1.2-1.ca2604.1_arm64.deb Size: 381230 MD5sum: 547fb556c9624750a85944cdf72588f1 SHA1: 7101bba3e1761e84d1b7ddbdbfbdcabaef1403d5 SHA256: 34ae64c85c37de91e5492e61d42e8763d8cefb78768e3965f66da4ac638aad7b SHA512: a9ab977f8b57bac79c49ce4b49483854faa3e6b2dce65f1cbf7121adc6f02b413e5261144b7c7d9f6575fb5b717ac6ad562ee5b9fdd220faea3f495c53741e5b Homepage: https://cran.r-project.org/package=FastKRR Description: CRAN Package 'FastKRR' (Kernel Ridge Regression using 'RcppArmadillo') Provides core computational operations in C++ via 'RcppArmadillo', enabling faster performance than pure R, improved numerical stability, and parallel execution with OpenMP where available. On systems without OpenMP support, the package automatically falls back to single-threaded execution with no user configuration required. For efficient model selection, it integrates with 'CVST' to provide sequential-testing cross-validation that identifies competitive hyperparameters without exhaustive grid search. The package offers a unified interface for exact kernel ridge regression and three scalable approximations—Nyström, Pivoted Cholesky, and Random Fourier Features—allowing analyses with substantially larger sample sizes than are feasible with exact KRR. It also integrates with the 'tidymodels' ecosystem via the 'parsnip' model specification 'krr_reg', and the S3 method tunable.krr_reg(). To understand the theoretical background, one can refer to Wainwright (2019) . Package: r-cran-fastlink Architecture: arm64 Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5441 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 5344722 MD5sum: 1ba955dc14e78c830bcd02c382ca994e SHA1: 9d5bd1289e4c3b77e8da19b02328071faa52daa1 SHA256: 4ace29d0cd34784a0f17eaa73311c9c5cf1deccc2294d50cf5bae14ef54a4f25 SHA512: d53c4cbbe0b665010b3fc73f769b143ff3321a270a74ee12714f9cea5329bfc400079126af1f0eb1fb6d2b499e273b7c17923374cb2548306ccc98cc2345b501 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: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 521 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_arm64.deb Size: 220294 MD5sum: 8d9733385e57514e9181249040a3736a SHA1: f3f5c0fe550956709a67081f5b5baebee19b7cea SHA256: 53adcf037874ef6396683f4b6e91fc6bc9823b97d2e105726aee85ca88f44746 SHA512: 0e42cdc7347a29ad5346c4939e383fff00da1899559dd09e309772be0eebf9b4aab78e18b8d85896151562a09da59f0b00be10e6bed8a55b0db6579bcc0bcac2 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-fastlogisticregressionwrap Architecture: arm64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 242 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcppnumerical, r-cran-rcpp, r-cran-checkmate, r-cran-mass, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-fastlogisticregressionwrap_1.2.0-1.ca2604.1_arm64.deb Size: 133508 MD5sum: 0c0f820579aab11a240f7581df8bc041 SHA1: fcc23b232e281448cc3a955b2406af22f61e9c1a SHA256: f07e6b15f31f7be9c8edf8b0891dade2181c4981b9837126694fda3e4a9514b9 SHA512: d8ed866af3cdb9a8a3682db4ae2654652e3bb755a973a44960adc588d8811d5d65e50ff76ce33c8f5b3328039dd9c261e586bf97b2e791f044f35b2a7234118b Homepage: https://cran.r-project.org/package=fastLogisticRegressionWrap Description: CRAN Package 'fastLogisticRegressionWrap' (Fast Logistic Regression Wrapper) Provides very fast logistic regression with coefficient inferences plus other useful methods such as a forward stepwise model generator (see the benchmarks by visiting the github page at the URL below). 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Package: r-cran-fastlpr Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 418 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_arm64.deb Size: 203984 MD5sum: 7ec0041b035157db34f6c2eb9c8beec0 SHA1: dda18df623df1238c58ebbd6ca8d2b43004cb2dc SHA256: c1c8ed702dfa5b746d1bb89581738bc7ad5630488d44c92cf8f08c4b0f5aeb56 SHA512: 6340e8d76da597ce734675ed31cf0c3a20796ec909e95f91814218f714a2af72f6db945d07ae6d53a335fb47b9b67aa3a236a3eeac60bbba895232ad84c052b1 Homepage: https://cran.r-project.org/package=fastlpr Description: CRAN Package 'fastlpr' (Fast Local Polynomial Regression and Kernel Density Estimation) Non-Uniform Fast Fourier Transform ('NUFFT')-accelerated local polynomial regression and kernel density estimation for large, scattered, or complex-valued datasets. 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Package: r-cran-fastm Architecture: arm64 Version: 0.0-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-fastm_0.0-5-1.ca2604.1_arm64.deb Size: 144360 MD5sum: 69808bcd0721f50f15eb7f5bb48ade40 SHA1: 1d569b53eb933bade3d018268659ec83823bdc45 SHA256: 12f87cd3572a354b1027f7435b620af50758058e7c002b31c457dae6efa102bf SHA512: d61cc2b1d44f2c8408518c56aa98b99e64db1cacd4d8d2498724331bef9a94f27d2606c7d4918bcd82633c66bff8585c173079020a6605f8e1497ccaa4884a3e Homepage: https://cran.r-project.org/package=fastM Description: CRAN Package 'fastM' (Fast Computation of Multivariate M-Estimators) Implements the new algorithm for fast computation of M-scatter matrices using a partial Newton-Raphson procedure for several estimators. 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Package: r-cran-fastmatch Architecture: arm64 Version: 1.1-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fastmatch_1.1-8-1.ca2604.1_arm64.deb Size: 37492 MD5sum: 8347e5d43c6c1160a0d727766914b337 SHA1: 1c2e641d21c836f29576eecd28e6989cf5de761a SHA256: 3fe7e257f6b01d209962a00efd0bd46e614f4fd2a95434f1aa7360ee04f0dc43 SHA512: 675dfba725c94d5b0c0b42b13407617740a85e42a3c7a9bafae6931c9e0db6d86e2dfd402c08307f662d21799d95b9c82bca4b360fb9ec15045f74f920cf6f9c Homepage: https://cran.r-project.org/package=fastmatch Description: CRAN Package 'fastmatch' (Fast 'match()' Function) Package providing a fast match() replacement for cases that require repeated look-ups. It is slightly faster that R's built-in match() function on first match against a table, but extremely fast on any subsequent lookup as it keeps the hash table in memory. Package: r-cran-fastmatmr Architecture: arm64 Version: 1.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1439 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-matrix, r-cran-microbenchmark, r-cran-rmarkdown, r-cran-spam, r-cran-sparsem, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fastmatmr_1.2.8-1.ca2604.1_arm64.deb Size: 542448 MD5sum: c86aaaa7893c1114f151327b61fe4971 SHA1: 1db19c793215d8ee84f56deb3562d6d768e929f9 SHA256: c12a53cfdbc8b77555770ea2fc2cc42507bfad0ca450b9dc6a82fadd3d1ba7c2 SHA512: 135dfc61336a08c5c3f657befe69619b2d1b74cfd84a51b3a2668382f2bcd0517abd49b7f47055a140b8ba9455a90bc470f460a22cf446bda3bfdb1d7e829c8d Homepage: https://cran.r-project.org/package=fastMatMR Description: CRAN Package 'fastMatMR' (High-Performance Matrix Market File Operations) An interface to the 'fast_matrix_market' 'C++' library, this package offers efficient read and write operations for Matrix Market files in R. 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Package: r-cran-fastmit Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.17), 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-fastmit_0.1.1-1.ca2604.1_arm64.deb Size: 49996 MD5sum: 8070d69a5333d660b0062ad70eb4333a SHA1: 98e09b2d52b0eae00c5960670b844a8f06b7350e SHA256: afef5d565ca6a56f683f56d5dac3a6ccc1f105876539f2517fe9346c0e597e68 SHA512: 0291b61793bb458ace1aa106ca808f557290b7cd3048cdedb49baf8abbc5dc37d3d97c6090fd0b9fa4aa8df3d8a6a90bc04b588792bdd9b34c7910dfd23a3094 Homepage: https://cran.r-project.org/package=fastmit Description: CRAN Package 'fastmit' (Fast Mutual Information Based Independence Test) A mutual information estimator based on k-nearest neighbor method proposed by A. Kraskov, et al. 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Package: r-cran-faulttree Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 468 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 246514 MD5sum: 285f0d879c963824c197ae02062a402a SHA1: b6df813009ea347e71fbf621076e53627a22fd75 SHA256: 488ccc27537562d2c4f71d4e29fd8810193db9962896529285358724e37032d4 SHA512: 3f968c3142eff82f6230ffa8a788a118cab423aa679a02c72a1afecb09a479be8983b60f83698653a77f4829750014de02a9602dcc54c579bb4421a9251a5a8b 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: arm64 Version: 4052.98-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2881 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-timedate, r-cran-timeseries, r-cran-mass, r-cran-spatial, r-cran-gss, r-cran-stabledist Suggests: r-cran-interp, r-cran-runit Filename: pool/dists/resolute/main/r-cran-fbasics_4052.98-1.ca2604.1_arm64.deb Size: 2474012 MD5sum: 1c3ad76ef8c18f11373df42f3ae9673b SHA1: 23da010e44034e1af4c9bc88e1827c63e09b4adc SHA256: 0482047ff7d84996f4f8fbfe860e4c614981aca9578caddd637f844c3d2185a8 SHA512: 3658b9eef34cb893a61018e26a0cd09bb6c2486047b5f1e905971b219aff0ffd53be467c953c6911e31798be4345e8380004ff56a7689037f8018fef49b963c7 Homepage: https://cran.r-project.org/package=fBasics Description: CRAN Package 'fBasics' (Rmetrics - Markets and Basic Statistics) Provides a collection of functions to explore and to investigate basic properties of financial returns and related quantities. 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Package: r-cran-fbati Architecture: arm64 Version: 1.0-11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 641 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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_arm64.deb Size: 460456 MD5sum: 97521e0db5f720205b1b8d344ca702e0 SHA1: 0a3f48834531d1534595ec2469a91da0e6155402 SHA256: 5c64a65c36e1773e64f5f3695294b77872a749eb6a8cdd1d39b0bf1363a5e410 SHA512: 51fa54a3f21ef049a8f34a6499057140301141c0d55c615241fd5cf7adde06b54c5c3bd60fd5beed6f7589787269cb025a83e83fd54ef8ee34aba461609eb6fe 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. 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Package: r-cran-fbfsearch Architecture: arm64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3845 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_arm64.deb Size: 3757100 MD5sum: 1cf315b9d9a5ea01ac8bd43700a23958 SHA1: d72721ae095dab82e2a738585e15657fbd622183 SHA256: 399fdda7ba09d2466c0ff234ac5c6cdd6522085f14be44e25a2ad810a194eeb2 SHA512: cbb2d590239bcb7375105684aa3e0222a61c1b3087403a547b0a10d55fbefa80cbf5b3a06a7595ffa9ec2be1162febddab1e44c08e57c0966275d179a1af072a Homepage: https://cran.r-project.org/package=FBFsearch Description: CRAN Package 'FBFsearch' (Algorithm for Searching the Space of Gaussian Directed AcyclicGraph Models Through Moment Fractional Bayes Factors) We propose an objective Bayesian algorithm for searching the space of Gaussian directed acyclic graph (DAG) models. 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Package: r-cran-fcar Architecture: arm64 Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3226 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_arm64.deb Size: 1785062 MD5sum: fc955c8095a274bebcd80ab17895e79b SHA1: 4ad42be92449b7c5169a5ece4780f361d77fba02 SHA256: 2b448b2fe5bc49f0e4349a35bc14b227640194bba69cd7c8dfbf2e5778ecd699 SHA512: 26771891aa4d79825918a6986efaf9f9b38e094ddfc6771227825fef5ef169c941e145640a78d22a9c405b0bd01c7e54a7e85177cc101bb7e7a2644157b76931 Homepage: https://cran.r-project.org/package=fcaR Description: CRAN Package 'fcaR' (Formal Concept Analysis) Provides tools to perform fuzzy formal concept analysis, presented in Wille (1982) and in Ganter and Obiedkov (2016) . 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Package: r-cran-fclust Architecture: arm64 Version: 2.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1281 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 782298 MD5sum: c0bcb3386db4dd73bc09c3725a29668a SHA1: c489d0e7049d25b6959af5384df68365c4d74dcb SHA256: 7c296b8d47c533d9c51ea46b5b39855610efbc85576c8793beb55d38b6cba9c7 SHA512: f566fa3a2d82df4fc64254676b164ebe2db552935e0b672c36a5c507bb51a84d67947fc2e64857419f6edbfb95526d40ade48a9f3c4437c1a741dac22b607067 Homepage: https://cran.r-project.org/package=fclust Description: CRAN Package 'fclust' (Fuzzy Clustering) Algorithms for fuzzy clustering, cluster validity indices and plots for cluster validity and visualizing fuzzy clustering results. Package: r-cran-fcros Architecture: arm64 Version: 1.6-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3128 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fcros_1.6-6-1.ca2604.1_arm64.deb Size: 2908592 MD5sum: fe7beff57c9c456f5d91f4063bf8247c SHA1: 55924e4a6e16336cd7d411b4bc2d0c3a8c5ae027 SHA256: a82cffef2295892b70d2a4fa4adab0686f3a12613ffdc89b578891321eb5b567 SHA512: e808fdf59d168f4a3d630ed6f02bbea611d02c7ae9c04e4c786517c1af8985ff5ee58231c171dbeb0bb7a2a98fbea6bab2da39fcd16c4e8902ab3974744b4d15 Homepage: https://cran.r-project.org/package=fcros Description: CRAN Package 'fcros' (A Method to Search for Differentially Expressed Genes and toDetect Recurrent Chromosomal Copy Number Aberrations) A fold change rank based method is presented to search for genes with changing expression and to detect recurrent chromosomal copy number aberrations. This method may be useful for high-throughput biological data (micro-array, sequencing, ...). Probabilities are associated with genes or probes in the data set and there is no problem of multiple tests when using this method. For array-based comparative genomic hybridization data, segmentation results are obtained by merging the significant probes detected. Package: r-cran-fd Architecture: arm64 Version: 1.0-12.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: libc6 (>= 2.29), r-base-core (>= 4.6.0), r-api-4.0, r-cran-ade4, r-cran-ape, r-cran-geometry, r-cran-vegan Filename: pool/dists/resolute/main/r-cran-fd_1.0-12.5-1.ca2604.1_arm64.deb Size: 178182 MD5sum: a7f47bf8cfd8a8beb3d5066fb2caad3a SHA1: d5c9c0bb211e2891e0967437abd7f4181d8d73e3 SHA256: 40e0ca41d7db21a114dca0e6b05291285f537a18e03c744c782ddcd3c07f2470 SHA512: ce25961a5a034a3dd930adc4868ce85960b416ecc8d35452d0b92ba6617ee73fd2fe05bcd84e887f5263aa727ff776efa521827a3298d4e9759a911e5323dc72 Homepage: https://cran.r-project.org/package=FD Description: CRAN Package 'FD' (Measuring Functional Diversity (FD) from Multiple Traits, andOther Tools for Functional Ecology) Computes different multidimensional FD indices. Implements a distance-based framework to measure FD that allows any number and type of functional traits, and can also consider species relative abundances. Also contains other useful tools for functional ecology. Package: r-cran-fda.usc Architecture: arm64 Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3262 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2971744 MD5sum: 0737c8c3aad2c39f3b18cf5489c25471 SHA1: f5d34e3fa88b2231e7eedada27ada7e7f2d6f1e7 SHA256: 3b360b87279fe956208df86a2858bbed1815623c8c7cb05bf8eeb02929fdad15 SHA512: 3dd029197bb844efad5006eaf4115e7bdfab8c49eeb0f52087630f1d1e1ceb01171018140f073fac7d377582526ced6c8b630656496b810ef48110132e12868c 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: arm64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6566 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_arm64.deb Size: 5351426 MD5sum: 4993878fe3b6b43c0cf190adf30ab90b SHA1: c4b33cea0a84c8cfa5e030e93441e7981f87f09b SHA256: 31c0b93750c2dede00e489e888bf5eb08ecf922dc20c584dacffb312b93824c6 SHA512: 9620cafda55884be25a1b9a7d4c698dcc749c21636c6621d8695a6aec415a0f1a304d54fc4724854fe7ff13c7d50e60dfc5242d70e07be97e6e0066bc2aac3e2 Homepage: https://cran.r-project.org/package=fdacluster Description: CRAN Package 'fdacluster' (Joint Clustering and Alignment of Functional Data) Implementations of the k-means, hierarchical agglomerative and DBSCAN clustering methods for functional data which allows for jointly aligning and clustering curves. It supports functional data defined on one-dimensional domains but possibly evaluating in multivariate codomains. It supports functional data defined in arrays but also via the 'fd' and 'funData' classes for functional data defined in the 'fda' and 'funData' packages respectively. It currently supports shift, dilation and affine warping functions for functional data defined on the real line and uses the SRVF framework to handle boundary-preserving warping for functional data defined on a specific interval. Main reference for the k-means algorithm: Sangalli L.M., Secchi P., Vantini S., Vitelli V. (2010) "k-mean alignment for curve clustering" . Main reference for the SRVF framework: Tucker, J. D., Wu, W., & Srivastava, A. (2013) "Generative models for functional data using phase and amplitude separation" . Package: r-cran-fdaconcur Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 297 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 163130 MD5sum: ed34f9dc1bb5ab922f518230318410df SHA1: 8177b8cf646fa8052bdf40a920218b5cc729d48e SHA256: b9bf43fd4f81c37a94a66afb7847b99184dea41f95c273a7f9f7d23b3aaadc4b SHA512: 99958ee61382900b898c264db70c93b539969149c7e84e2215908c6f0628e00d11bb6bac3da784e9c4ae500d8a6c6ce569b01e5153a4668165f91294efdd139c Homepage: https://cran.r-project.org/package=fdaconcur Description: CRAN Package 'fdaconcur' (Concurrent Regression and History Index Models for FunctionalData) Provides an implementation of concurrent or varying coefficient regression methods for functional data. The implementations are done for both dense and sparsely observed functional data. Pointwise confidence bands can be constructed for each case. Further, the influence of past predictor values are modeled by a smooth history index function, while the effects on the response are described by smooth varying coefficient functions, which are very useful in analyzing real data such as COVID data. References: Yao, F., Müller, H.G., Wang, J.L. (2005) . Sentürk, D., Müller, H.G. (2010) . Package: r-cran-fdadensity Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3661 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 3598288 MD5sum: 221f4c0a71427f4cc093aaa78ecc82b0 SHA1: ef22544bfa71d45aaebb4eddec1f4f22e31bda5d SHA256: c645c978ee8730f1369b37055f8040b9e5cf49986fe0e028be0e9762839dc596 SHA512: a545911336392265d58e51a885072a035952c12d469aad7447ac3a0bacb090048680a801e965a086a6ce59ad450d39a677360fbaf204123c76318ce3d8ac69fe Homepage: https://cran.r-project.org/package=fdadensity Description: CRAN Package 'fdadensity' (Functional Data Analysis for Density Functions by Transformationto a Hilbert Space) An implementation of the methodology described in Petersen and Mueller (2016) for the functional data analysis of samples of density functions. Densities are first transformed to their corresponding log quantile densities, followed by ordinary Functional Principal Components Analysis (FPCA). Transformation modes of variation yield improved interpretation of the variability in the data as compared to FPCA on the densities themselves. The standard fraction of variance explained (FVE) criterion commonly used for functional data is adapted to the transformation setting, also allowing for an alternative quantification of variability for density data through the Wasserstein metric of optimal transport. Package: r-cran-fdamixed Architecture: arm64 Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 534 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 206028 MD5sum: 43e4e39667fd2d5891c7440c108eac23 SHA1: 89e04dd8c55356750561154330cd4da236ceaae3 SHA256: 2a96464e3ea8d8cc2e4dce4f45e35996161d664afdf374ba7b08de21c17a34d9 SHA512: 3bfdb1d7fdb81bdb79d2a60a438d2ddc0efb981356c82e40580f77ac6f505598f815a2e6dbb8a3a7cbcdf0528264e41202d148f5791b1efaa2795447b511d0d1 Homepage: https://cran.r-project.org/package=fdaMixed Description: CRAN Package 'fdaMixed' (Functional Data Analysis in a Mixed Model Framework) Likelihood based analysis of 1-dimension functional data in a mixed-effects model framework. Matrix computation are approximated by semi-explicit operator equivalents with linear computational complexity. Markussen (2013) . Package: r-cran-fdaoutlier Architecture: arm64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 838 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), 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_arm64.deb Size: 678578 MD5sum: 3b6294ef91d019089433332c1dbe1cb7 SHA1: 23b02cd0e279de9870ad954c788745d2c1b8b54b SHA256: 3d2433edfc97db23c0503d17f2d8401c9b92442420826f8db08fca82316f754b SHA512: 24f3d588d6a5085482423b1169047d159f4c8517dd9971124bfb87acb9db75635a0659274783430faad945daf41caa15f6fcbb1ded2a5d28cf6c2250ae924b3c 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: 2246 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_arm64.deb Size: 1571288 MD5sum: 7bfe77d8741287350069c1e8fe73e634 SHA1: ff18a6c6706737c85275ff821fde67627d870110 SHA256: a994dae2a055910c62c215c4ddbb3c24b8654ba70e33b0b8d9df75a31521b224 SHA512: 4f145993e6545a0125ca4aef5c286bf81cd5846eec5bef320ccbd989e105bb8d913ddc59fa842bf571ed9ff4f50752d1686315ff28d18ede1cc8f7c925fd27f2 Homepage: https://cran.r-project.org/package=fdapace Description: CRAN Package 'fdapace' (Functional Data Analysis and Empirical Dynamics) A versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. This core algorithm yields covariance and mean functions, eigenfunctions and principal component (scores), for both functional data and derivatives, for both dense (functional) and sparse (longitudinal) sampling designs. For sparse designs, it provides fitted continuous trajectories with confidence bands, even for subjects with very few longitudinal observations. PACE is a viable and flexible alternative to random effects modeling of longitudinal data. There is also a Matlab version (PACE) that contains some methods not available on fdapace and vice versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry). References: Wang, J.L., Chiou, J., Müller, H.G. (2016) ; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) . Package: r-cran-fdapde Architecture: arm64 Version: 1.1-21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8942 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_arm64.deb Size: 2581296 MD5sum: 84e79d4276644a6c4acb42ec89d577aa SHA1: bfdae609f697ec151a4246ae0d283e1f5000cacc SHA256: 84f3998d0a471966054996165eaa14e7dc9704c2c218b66a6db9bc630ec152f1 SHA512: 580945c725fe2d8e0994b0c890134faf2df59cba4c4aa5ff727aa1d4ee6727dc7a09d9e25f0f5faeefdfd33594f51a9748068c12b0b9c4271ef5a59e773341eb Homepage: https://cran.r-project.org/package=fdaPDE Description: CRAN Package 'fdaPDE' (Physics-Informed Spatial and Functional Data Analysis) An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization. See Sangalli, L. M. (2021) "Spatial Regression With Partial Differential Equation Regularisation" for an overview. The release 1.1-9 requires R (>= 4.2.0) to be installed on windows machines. Package: r-cran-fdarep Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 405 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 191352 MD5sum: fd02f8f04090796ee32eeefd1973aea0 SHA1: ccb224f7e86ac642a16b8a1db9a53d470b006c19 SHA256: 928153b562db9ac7d470463802f6fcb941ae7cf096e3cda53711b42994d64e60 SHA512: 81dc556e550b1e86903967eb7571744d140cdbd715aa6eaed38214eb38d9420267cffe910f0528243581937b5146f3dd94154e03f26cb7d362e73a8f23dc58a6 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: arm64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2597 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_arm64.deb Size: 1080330 MD5sum: fe63fc953ac6329f9093cce7df23d56c SHA1: c084e853e991e2e3e55d1439da2878c6f6589b62 SHA256: 4fd4fb890b774c87226efe1b592a63150d93f267f3bb06676167e3423443f518 SHA512: 679f816d9d82a40f35e0de8a0f01d3fb7a2b68cd5ef86a256975c1e61fac0b4d75c52eba252aaaf30f3e0b06954839bf1b9d4b4b4701fcbb06ed1afbe6e9da88 Homepage: https://cran.r-project.org/package=fdaSP Description: CRAN Package 'fdaSP' (Sparse Functional Data Analysis Methods) Provides algorithms to fit linear regression models under several popular penalization techniques and functional linear regression models based on Majorizing-Minimizing (MM) and Alternating Direction Method of Multipliers (ADMM) techniques. See Boyd et al (2010) for complete introduction to the method. Package: r-cran-fdasrvf Architecture: arm64 Version: 2.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4282 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_arm64.deb Size: 3893038 MD5sum: fa95ab14261c45064367cbb06cde768a SHA1: 9784a7e0ece17ee03bd758a16f45a2f41791f7e7 SHA256: 874228fd25ed484cf6c42c1c5f2950f2104b8c1889405f183a802a9930f3d38c SHA512: bd32a590da1a34aa075ea140ee7b3a23b03a0badbaa74754b86b656d899265b850cfb4b8397f5164ee8b6147fed6cfedf4df5378d7b69947f826167654e2e489 Homepage: https://cran.r-project.org/package=fdasrvf Description: CRAN Package 'fdasrvf' (Elastic Functional Data Analysis) Performs alignment, PCA, and modeling of multidimensional and unidimensional functions using the square-root velocity framework (Srivastava et al., 2011 and Tucker et al., 2014 ). This framework allows for elastic analysis of functional data through phase and amplitude separation. Package: r-cran-fddm Architecture: arm64 Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2956 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_arm64.deb Size: 1493150 MD5sum: 244c4c58ea7d5e6e9f63004ee7cecdcf SHA1: 9e218514d56aee200e6b5e0f853e7ddb1fd684dc SHA256: 15da0f59bf47d055ae96a6bcbda2dfe59f1b9cade74cfaab39e95c09fec959b2 SHA512: 0fc3442acd50b569b47c0754f0393f6b97c83f4556b1a7113373c720e7b56e0ecb17fb26c60c37d24cf28854da75a83b6885375a57c7c599da6e850cdc07d97c Homepage: https://cran.r-project.org/package=fddm Description: CRAN Package 'fddm' (Fast Implementation of the Diffusion Decision Model) Provides the probability density function (PDF), cumulative distribution function (CDF), the first-order and second-order partial derivatives of the PDF, and a fitting function for the diffusion decision model (DDM; e.g., Ratcliff & McKoon, 2008, ) with across-trial variability in the drift rate. Because the PDF, its partial derivatives, and the CDF of the DDM both contain an infinite sum, they need to be approximated. 'fddm' implements all published approximations (Navarro & Fuss, 2009, ; Gondan, Blurton, & Kesselmeier, 2014, ; Blurton, Kesselmeier, & Gondan, 2017, ; Hartmann & Klauer, 2021, ) plus new approximations. All approximations are implemented purely in 'C++' providing faster speed than existing packages. Package: r-cran-fdesigns Architecture: arm64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 739 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_arm64.deb Size: 509288 MD5sum: 8b28bb3f44d602c4468ab7c5bac92c92 SHA1: 93010df9d230c44aedc936f943a206bdb8e43b2e SHA256: d6a82529447b02656218b115fe2eeb7a421611f45c80fff9bc55bdaea9538a23 SHA512: 7de170a08a28fe33e2c4937543af9d0bb567c232a54dbb8319214fd625749a627c02bb281d4eaceafc0d6cee7167da79e9e6118c83cdc7f259eb9a07d84485fc Homepage: https://cran.r-project.org/package=fdesigns Description: CRAN Package 'fdesigns' (Optimal Experimental Designs for Dynamic/Functional Models) Optimal experimental designs for functional linear and functional generalised linear models, for scalar responses and profile/dynamic factors. The designs are optimised using the coordinate exchange algorithm. The methods are discussed by Michaelides (2023) . Package: r-cran-fdma Architecture: arm64 Version: 2.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 744 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_arm64.deb Size: 596258 MD5sum: 801b457cca8bbfe111fbc31441fd89d3 SHA1: ea7483950ba9e1c621ea45b5542c95b54c976e2b SHA256: 6860f7e0006bd11685924776574cc48685cdf98c7df2e89ab07409155cfa2923 SHA512: 6b875ab326f169024c53434ed6aa67173a8b6ce876a0fb56e2d7706a43b897f9f0a7e99bff8cc76d0ec5c047317ef24033016586a73e5f7ba6b820cd115899d8 Homepage: https://cran.r-project.org/package=fDMA Description: CRAN Package 'fDMA' (Dynamic Model Averaging and Dynamic Model Selection forContinuous Outcomes) Allows to estimate dynamic model averaging, dynamic model selection and median probability model. The original methods are implemented, as well as, selected further modifications of these methods. In particular the user might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using 'Google Trends'. The code is written in a way which minimises the computational burden (which is quite an obstacle for dynamic model averaging if many variables are used). For example, this package allows for parallel computations and Occam's window approach. The package is designed in a way that is hoped to be especially useful in economics and finance. Main reference: Raftery, A.E., Karny, M., Ettler, P. (2010) . Package: r-cran-fdott Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 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_arm64.deb Size: 186560 MD5sum: 966706ba18a520724dba2a7dd8d942c6 SHA1: 810b845a84626095b1886b7b093bb04f456af4ab SHA256: e15388471ce8011864e0d493efbff6cbab613b0c599c7aeee44db9a16bf15598 SHA512: be4592fc42d1d24f8633bd48f39bdabf38455a80e844c752ff5cfd74f41243c1144661c586dd0875bbd40777075ba440e7dc7aa1dbe0bc84ce762d814a7a2997 Homepage: https://cran.r-project.org/package=FDOTT Description: CRAN Package 'FDOTT' (Optimal Transport Based Testing in Factorial Design) Perform optimal transport based tests in factorial designs as introduced in Groppe et al. (2025) via the FDOTT() function. These tests are inspired by ANOVA and its nonparametric counterparts. They allow for testing linear relationships in factorial designs between finitely supported probability measures on a metric space. Such relationships include equality of all measures (no treatment effect), interaction effects between a number of factors, as well as main and simple factor effects. Package: r-cran-fdrtool Architecture: arm64 Version: 1.2.18-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 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_arm64.deb Size: 139208 MD5sum: 105c57d9b294f17ad9fdf8e34c893e22 SHA1: 149fa0dc91e654bc861d0f09b64bcf359e82ce30 SHA256: 29a80698953efee16fcb52c4ad5c5ab63e87def26d7b946898f1458bc875cd5c SHA512: 45d67bc1618c94a2e02d314b754997780aa848ecc0214f71ca76435baec0f13b00e84dea6cf334bb3bd31b696966a188d7898c0734765ce78492f8174aef0c66 Homepage: https://cran.r-project.org/package=fdrtool Description: CRAN Package 'fdrtool' (Estimation of (Local) False Discovery Rates and Higher Criticism) Estimates both tail area-based false discovery rates (Fdr) as well as local false discovery rates (fdr) for a variety of null models (p-values, z-scores, correlation coefficients, t-scores). The proportion of null values and the parameters of the null distribution are adaptively estimated from the data. In addition, the package contains functions for non-parametric density estimation (Grenander estimator), for monotone regression (isotonic regression and antitonic regression with weights), for computing the greatest convex minorant (GCM) and the least concave majorant (LCM), for the half-normal and correlation distributions, and for computing empirical higher criticism (HC) scores and the corresponding decision threshold. Package: r-cran-fdx Architecture: arm64 Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 696 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_arm64.deb Size: 388056 MD5sum: 95bee198db614093d38b4df721170187 SHA1: 22139100bdd0ca3df0bdb4690a29ebd4078afdb4 SHA256: 77fc89019bfe8c45a9f1cd65c612bcccf776a8c66cbc5c7210e472b7a8152736 SHA512: 845167eb6da3eae001700283c0a05a9f34604e4cf7ca28f2931844d8bf9bfffbfe22f65824ec3fd96243b0b481de2b2a45324dc5a3c1041dab7f5668dc5b3226 Homepage: https://cran.r-project.org/package=FDX Description: CRAN Package 'FDX' (False Discovery Exceedance Controlling Multiple TestingProcedures) Multiple testing procedures for heterogeneous and discrete tests as described in Döhler and Roquain (2020) . The main algorithms of the paper are available as continuous, discrete and weighted versions. They take as input the results of a test procedure from package 'DiscreteTests', or a set of observed p-values and their discrete support under their nulls. A shortcut function to obtain such p-values and supports is also provided, along with wrappers allowing to apply discrete procedures directly to data. Package: r-cran-feather Architecture: arm64 Version: 0.4.0-1.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_arm64.deb Size: 18474 MD5sum: d6f473e6d2861d39edc6c2b8c9d09d84 SHA1: 3a62169584fff07e54da1f0f3ec93bd472f8370d SHA256: 85347e7bdba5c15ce5469340f72ee8c63973f58bff4b74f85a638d135d2c028b SHA512: 5b2749924ea4ec2b8f2a78472f1b10fd1570cf59e97b7ac03581eaabf32658b800c1a49119fcb736ace0d9aff3f1903568a4a5f8a745e52e64ef8b8b8c186060 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: arm64 Version: 2.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3180 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 2598514 MD5sum: 8267f17a7fd26f30623af8e0940976aa SHA1: 2edd86bf36692f337ce80c6fde913a5d44c1a545 SHA256: 8f6a51db1165e00b3fb0f9512063bd2408c335ccfb380e168f8282df85b63e94 SHA512: 906d2a73e03b19174fb9d4ac189ac4eda7ea22253c120c1a8401c843ec89b48b4a45fd60c3348a2234b4d404254d583eaa53a34c394fa7baab6f5ac2c168e936 Homepage: https://cran.r-project.org/package=fect Description: CRAN Package 'fect' (Fixed Effects Counterfactual Estimators) Provides tools for estimating causal effects in panel data using counterfactual methods, as well as other modern DID estimators. It is designed for causal panel analysis with binary treatments under the parallel trends assumption. The package supports scenarios where treatments can switch on and off and allows for limited carryover effects. It includes several imputation estimators, such as Gsynth (Xu 2017), linear factor models, and the matrix completion method. Detailed methodology is described in Liu, Wang, and Xu (2024) and Chiu et al. (2025) . Optionally integrates with the "HonestDiDFEct" package for sensitivity analyses compatible with imputation estimators. "HonestDiDFEct" is not on CRAN but can be obtained from . Package: r-cran-fedmatch Architecture: arm64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 583 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringdist, r-cran-snowballc, r-cran-stringr, r-cran-purrr, r-cran-rcpp, r-cran-forcats, r-cran-data.table, r-cran-magrittr, r-cran-scales, r-cran-bh Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-fedmatch_2.1.0-1.ca2604.1_arm64.deb Size: 207352 MD5sum: b6dc0407ff1eabf64801c8fbc9677e9f SHA1: 82037d8ca7ef392c8c68716d374bc2da91c19d8d SHA256: c06f5232bbb5163672ab3439a94dc04c72a0adc4f187a24457e522896c240ead SHA512: 80143e76c12f2f40ba21d78ac0fd2c1bbe615e1d97cc36b8c7e3685d35a58f3409d65e86a01fc7be2bca60e67f0af25bb7446792bd1be255c89c39347326f0b4 Homepage: https://cran.r-project.org/package=fedmatch Description: CRAN Package 'fedmatch' (Fast, Flexible, and User-Friendly Record Linkage Methods) Provides a flexible set of tools for matching two un-linked data sets. 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Package: r-cran-fegarch Architecture: arm64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1993 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_arm64.deb Size: 1355574 MD5sum: 6420538441c87df1378588a5b93f1f35 SHA1: 802d020eb54a105f60d3b2d939cfde190437237f SHA256: b9d2e22c995de56a29224cce6a3b4348a4ec1920f72cae06cf7074b398a33f1d SHA512: 97a14e98a0df3850032c4ea29a6b8fad610ba27b19d48db0ff7a69ec5c6b22a98174f76afb7a4d7e197f3b54bcfa5803e90897d361726a54a4d42f7ff6d27f47 Homepage: https://cran.r-project.org/package=fEGarch Description: CRAN Package 'fEGarch' (SM/LM EGARCH & GARCH, VaR/ES Backtesting & Dual LM Extensions) Implement and fit a variety of short-memory (SM) and long-memory (LM) models from a very broad family of exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models, such as a MEGARCH (modified EGARCH), FIEGARCH (fractionally integrated EGARCH), FIMLog-GARCH (fractionally integrated modulus Log-GARCH), and more. The FIMLog-GARCH as part of the EGARCH family is discussed in Feng et al. (2023) . For convenience and the purpose of comparison, a variety of other popular SM and LM GARCH-type models, like an APARCH model, a fractionally integrated APARCH (FIAPARCH) model, standard GARCH and fractionally integrated GARCH (FIGARCH) models, GJR-GARCH and FIGJR-GARCH models, TGARCH and FITGARCH models, are implemented as well as dual models with simultaneous modelling of the mean, including dual long-memory models with a fractionally integrated autoregressive moving average (FARIMA) model in the mean and a long-memory model in the variance, and semiparametric volatility model extensions. Parametric models and parametric model parts are fitted through quasi-maximum-likelihood estimation. Furthermore, common forecasting and backtesting functions for value-at-risk (VaR) and expected shortfall (ES) based on the package's models are provided. Package: r-cran-fenmlm Architecture: arm64 Version: 2.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1883 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_arm64.deb Size: 999424 MD5sum: edc9a93bd5a4ba728151c4200b62d903 SHA1: 9058a201196de714506561f6c0e2c3c32f380b04 SHA256: c867c1ebb71e76278b22ef5290d6a3506705ad1b30eb18bd68b8ce827b1c1394 SHA512: c81eb8b46324a1575d047666e7612024b7b3f59599d01db625bb880bda1f5835b403fea712b4d9f0be4d6c24b9ec969a5d6741b089a7d961c4da7770d408d990 Homepage: https://cran.r-project.org/package=FENmlm Description: CRAN Package 'FENmlm' (Fixed Effects Nonlinear Maximum Likelihood Models) Efficient estimation of maximum likelihood models with multiple fixed-effects. Standard-errors can easily and flexibly be clustered and estimations exported. Package: r-cran-ff Architecture: arm64 Version: 4.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1576 Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), 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_arm64.deb Size: 992358 MD5sum: 26f385bc9aad3f3dcc142c265bba610d SHA1: f5af6cc851d670a5c5ab4368a2ad6ba92700059e SHA256: fd6c3f005fbc69c0211ae9a6a8d645b9c769a9f5631d2bef4be7e8d72df2c1bf SHA512: 6ec13896d99bc2d6a6506940a53ac9e8a1ff34f6e162827c8eeb945e2b7897b7ee85943cc1db18d791e59ded311b27d1abd914f9e73fb9cf04465e7212104bc5 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. 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This package provides access to the two-dimensional 'FFT', the multivariate 'FFT', and the one-dimensional real to complex 'FFT' using the 'FFTW3' library. The package includes the functions fftw() and mvfftw() which are designed to mimic the functionality of the R functions fft() and mvfft(). The 'FFT' functions have a parameter that allows them to not return the redundant complex conjugate when the input is real data. Package: r-cran-fgarch Architecture: arm64 Version: 4052.93-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 939 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fbasics, r-cran-timedate, r-cran-timeseries, r-cran-fastica, r-cran-matrix, r-cran-cvar Suggests: r-cran-runit, r-cran-goftest Filename: pool/dists/resolute/main/r-cran-fgarch_4052.93-1.ca2604.1_arm64.deb Size: 677996 MD5sum: 1c661df17e98328d3ded935ae4f456bd SHA1: bb211b83dc37a44b2e9e883d8887a89e792bc9fa SHA256: dba671d6978987952f670c65bf71bd5f18dad6ad64fc3babc4073fc818a8c01c SHA512: 6f59995282f708bedbfd5638130bb416019e0b7fae02525f54f0a4e1d3ffdd2d6d471c12ebc3473e98965c8509033e747d7011cabfdb71972a82a51d731186c6 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. 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Package: r-cran-fhdi Architecture: arm64 Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 461 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fhdi_1.4.1-1.ca2604.1_arm64.deb Size: 197052 MD5sum: f3c78eb1239dece93490c0b702ae811d SHA1: 33cfdbcd8ca67fda74cdc0a62a96e795fef3ec00 SHA256: 5a56b9608af63ab42d91157795f06a4c30247acb702ffecd6d7ee76609fd48c2 SHA512: 79a61a56eb534f21e567f45994b735e27bcf14a341f390fd158605855bd8e93b62f4bf6108168b76e127fb5dcf3fa883fa846db95d02f231b3705de831cddd9b 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: arm64 Version: 0.1-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 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_arm64.deb Size: 260942 MD5sum: bc330d2c7e5cfa4933a1e91d25a9ff64 SHA1: ad66fb93c4f5a4cf1cf6fa7e8d5651af3430c5c3 SHA256: 256401d6b6aed1d41b01d4e6507d6d2afb3847a29d3b9d2d3b30a6a0a86703c9 SHA512: d0be54de57bc71fe6a056589262d2345697840804fd099dc77135f4c544262ef8ca9be74ee1308498776980c6510dabaa29dd41b5dc033c3882fc3e36759c054 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: arm64 Version: 1.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 380 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 168386 MD5sum: 2b7367a96ad48bea44de8852460b06e5 SHA1: 4154fd9835e497f671a017b3b3f2c6aaa61125ba SHA256: d2a0bdf7df8c243978cc28efb1396137e56bdcac67c66f2eb32c4e48d52e6b4b SHA512: 7709c9a5933db07e1953d7f51618d01bbfb5b324c3d733c2c77839cd2e4b37165f1172337d6890c1be5a2694bca3c7b0573c6b299684da46a07850b9610cb30a 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) . 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Al. (2022) . Key functionality is implemented in C++ for scalability. 'fido' replaces the previous package 'stray'. Package: r-cran-fields Architecture: arm64 Version: 17.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4874 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_arm64.deb Size: 4789974 MD5sum: a41227282ce42927b2b3447d28f0b5b8 SHA1: 849ef374e411abe83a5c316e8dd4e83fe6586a8e SHA256: 19afe20bff67d448e16cec9d48714d8a1aea79b890ef359b079fe6076f292edf SHA512: 78d92007f2353821d27351758e67785eb6d44be58fb91a6b64a7db16302b8f38f8c6f1bc340cd47bf8c1ba4b6d09dadeb8c1659edc1802b7ae0c6679c6affb2e Homepage: https://cran.r-project.org/package=fields Description: CRAN Package 'fields' (Tools for Spatial Data) For curve, surface and function fitting with an emphasis on splines, spatial data, geostatistics, and spatial statistics. The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter). A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview. All graphics functions focus on using base R graphics. The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding the source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI . Development of this package was supported in part by the National Science Foundation Grant 1417857, the National Center for Atmospheric Research, and Colorado School of Mines. See the Fields URL for a vignette on using this package and some background on spatial statistics. 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Package: r-cran-filling Architecture: arm64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 889 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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-cvxr, r-cran-rcpp, r-cran-rdpack, r-cran-roptspace, r-cran-rspectra, r-cran-nabor, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-filling_0.2.4-1.ca2604.1_arm64.deb Size: 720312 MD5sum: 759fc9d835f7eab99eee6c40540d001c SHA1: fe64f31e551ee647a38284dd32fdfb14e1fc5537 SHA256: f07271e5acff93dc739c29f4320fe34ad995b8f2c489467cab061b034c46d5d4 SHA512: cc9f092ee6b1091de96d577ce7e7c3164eec348a3ddac3c75c17b1c5de0b9ddd6567c6d57c760f992ac83f39d1cdc2a37067b80d3e698fbc7e0c96ee66a5af7b Homepage: https://cran.r-project.org/package=filling Description: CRAN Package 'filling' (Matrix Completion, Imputation, and Inpainting Methods) Filling in the missing entries of a partially observed data is one of fundamental problems in various disciplines of mathematical science. For many cases, data at our interests have canonical form of matrix in that the problem is posed upon a matrix with missing values to fill in the entries under preset assumptions and models. We provide a collection of methods from multiple disciplines under Matrix Completion, Imputation, and Inpainting. See Davenport and Romberg (2016) for an overview of the topic. Package: r-cran-finalsize Architecture: arm64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1369 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_arm64.deb Size: 491876 MD5sum: c0d4bd6a927532861e8151787f1bdfee SHA1: cdfdb40e73a18c824339f11556e5d286b57d2b24 SHA256: 7bd12ea86a498e3d20b08502b3c2a5a0b8301c37669e00e0247cce8cc522f821 SHA512: b6cecb1fe3c2c5c92e374aadcd3e7ef260dbef425ca27a74671276fa9814dd518e02e06af794ab1a90665dc34ffd0fe7bdc12aa9db3881950d4ae28842d9035d Homepage: https://cran.r-project.org/package=finalsize Description: CRAN Package 'finalsize' (Calculate the Final Size of an Epidemic) Calculate the final size of a susceptible-infectious-recovered epidemic in a population with demographic variation in contact patterns and susceptibility to disease, as discussed in Miller (2012) . Package: r-cran-fingerprint Architecture: arm64 Version: 3.5.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-runit, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fingerprint_3.5.10-1.ca2604.1_arm64.deb Size: 244278 MD5sum: fea9989fe94a9ba645af92467e5fa388 SHA1: 092fbc19f57c7306f91b28d17c94e0107d6ea5da SHA256: 6d986825ea4cb17e12bbc64cdca67265de763dc7f571fdf2fe95e0047ce76bc1 SHA512: d3ad4406956deb2c9a6d7797f654d49407118ad5b1d945204d44d4077b21bc98d3345260f6ccebdb57f30fa65f5243dadb669fe87a628ae2b3e7da29dc94b042 Homepage: https://cran.r-project.org/package=fingerprint Description: CRAN Package 'fingerprint' (Functions to Operate on Binary Fingerprint Data) Functions to manipulate binary fingerprints of arbitrary length. A fingerprint is represented by an object of S4 class 'fingerprint' which is internally represented a vector of integers, such that each element represents the position in the fingerprint that is set to 1. The bitwise logical functions in R are overridden so that they can be used directly with 'fingerprint' objects. A number of distance metrics are also available (many contributed by Michael Fadock). Fingerprints can be converted to Euclidean vectors (i.e., points on the unit hypersphere) and can also be folded using OR. Arbitrary fingerprint formats can be handled via line handlers. Currently handlers are provided for CDK, MOE and BCI fingerprint data. Package: r-cran-fingerpro Architecture: arm64 Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3807 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2456192 MD5sum: ba3284fb00fde554f54d330aeb4b92b7 SHA1: 295c7c062099593ba8cb497eb55d800b232c9722 SHA256: 43eebd002ab3a9fe519123ed8d5e057eef3db50343fcad5e77e8a60807395729 SHA512: da8dd022f654a65b5cf09dca4e6ba13cbdb0805209ff919c2d573f38be4bd8f800a87067fc8d67d8b479cb486788e2708684bdaecc6f5bd4bad9857fc8faf5f9 Homepage: https://cran.r-project.org/package=fingerPro Description: CRAN Package 'fingerPro' (Unmixing Model Framework) Quantifies the provenance of sediments by applying a mixing model algorithm to end sediment mixtures based on a comprehensive characterization of the sediment sources. The 'fingerPro' model builds upon the foundational concept of using mass balance linear equations for sediment source quantification by incorporating several distinct technical advancements. It employs an optimization approach to normalize discrepancies in tracer ranges and minimize the objective function. Latin hypercube sampling is used to explore all possible combinations of source contributions (0-100%), mitigating the risk of local minima. Uncertainty in source estimates is quantified through a Monte Carlo routine, and the model includes additional metrics, such as the normalized error of the virtual mixture, to detect mathematical inconsistencies, non-physical solutions, and biases. A new linear variability propagation (LVP) method is also included to address and quantify potential bias in model outcomes, particularly when dealing with dominant or non-contributing sources and high source variability, offering a significant advancement for field studies where direct comparison with theoretical apportionments is not feasible. In addition to the unmixing model, a complete framework for tracer selection is included. Several methods are implemented to evaluate tracer behaviour by considering both source and mixture information. These include the Consistent Tracer Selection (CTS) method to explore all tracer combinations and select the optimal ones improving the robustness and interpretability of the model results. A Conservative Balance (CB) method is also incorporated to enable the use of isotopic tracers. The package also provides several graphical tools to support data exploration and interpretation, including box plots, correlation plots, Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA). Package: r-cran-finity Architecture: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 95710 MD5sum: 128a3977b0d771f2af083099217b1aba SHA1: a31511c27cc725687a56b9119035ca26d8563121 SHA256: 261c8c5d182e36e1b7a22ef5c6eca599692bcf5caeb844fa1ba54df21bb9a708 SHA512: 1d3e3bace4b5b0a955b46649045acb2c245aa29c9d29fea9e5d52abff30d418cd9681141d7d45b8344f1a3f824e439f46208f78d08318b542b5e55c0099c225b Homepage: https://cran.r-project.org/package=finity Description: CRAN Package 'finity' (Test for Finiteness of Moments in a Distribution) The purpose of this package is to tests whether a given moment of the distribution of a given sample is finite or not. For heavy-tailed distributions with tail exponent b, only moments of order smaller than b are finite. Tail exponent and heavy- tailedness are notoriously difficult to ascertain. But the finiteness of moments (including fractional moments) can be tested directly. This package does that following the test suggested by Trapani (2016) . Package: r-cran-finnet Architecture: arm64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 941 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 704766 MD5sum: caf9728ee2e85a059846ac788f6e05df SHA1: 9c6f1b0ea8e829daba08df3ec27ffa9c77865110 SHA256: a5aa8839f5158aa15e279adf5cd8d8b3f195cc1de8b43ec9d066b27c25450c01 SHA512: 8847815418d422fb8235359e1c698f58a91d92c36ef9007505cefff5e45d22398c6fe0e9758fc4ec43b4c5b6b6fd329cd2f2627c16188008cddaaf05bd4c850e Homepage: https://cran.r-project.org/package=FinNet Description: CRAN Package 'FinNet' (Quickly Build and Manipulate Financial Networks) Providing classes, methods, and functions to deal with financial networks. Users can easily store information about both physical and legal persons by using pre-made classes that are studied for integration with scraping packages such as 'rvest' and 'RSelenium'. Moreover, the package assists in creating various types of financial networks depending on the type of relation between its units depending on the relation under scrutiny (ownership, board interlocks, etc.), the desired tie type (valued or binary), and renders them in the most common formats (adjacency matrix, incidence matrix, edge list, 'igraph', 'network'). There are also ad-hoc functions for the Fiedler value, global network efficiency, and cascade-failure analysis. Package: r-cran-fio Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1243 Depends: libc6 (>= 2.39), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-clipr, r-cran-emoji, r-cran-fs, r-cran-miniui, r-cran-readxl, r-cran-rlang, r-cran-shiny, r-cran-rdpack, r-cran-r6 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-bench, r-cran-leontief, r-cran-ggplot2, r-cran-writexl, r-cran-callr, r-cran-testthat, r-cran-fiodata Filename: pool/dists/resolute/main/r-cran-fio_1.0.0-1.ca2604.1_arm64.deb Size: 699748 MD5sum: 874f590a00a3ede233326c3935a0dc6e SHA1: 2a17e005d141a6422a5d47646b238472d5528a2f SHA256: 6f09e36ef79f1c0115cf7127454c1bf9a15f65d8c7d02e2898b98686333345e2 SHA512: f9cd747f8e5fd7d5287b5bf93f8ff7828ccce73c75d38a6cffb11cf603e633333c33b28e04dcbb65764c866d0722b530935ef005f0f4f84060e6460213d18f79 Homepage: https://cran.r-project.org/package=fio Description: CRAN Package 'fio' (Friendly Input-Output Analysis) Simplifies the process of economic input-output analysis by combining user-friendly interfaces with high-performance computation. It provides tools for analyzing both single-region and multi-regional economic systems through a hybrid architecture that pairs R's accessibility with Rust's computational efficiency. Package: r-cran-fire Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1879 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-fire_1.0.1-1.ca2604.1_arm64.deb Size: 1745192 MD5sum: a65f00b4788742a2966d60edd48c7aa5 SHA1: 584ac4bf1714bb54514bc72a83d144ec785e18c1 SHA256: ef5510c67e103db704ddfda11bc757b14067eaf340f93ccc63373019db06dccd SHA512: 8d95777bdcdec0b7fed8fc7f56b06412ee37095697e080c25caaa5b7a9bfdad3442e7df4a903017a11a92f424f3bb32884516d70ac78b34fc168cf5b6f7b76f2 Homepage: https://cran.r-project.org/package=FiRE Description: CRAN Package 'FiRE' (Finder of Rare Entities (FiRE)) The algorithm assigns rareness/ outlierness score to every sample in voluminous datasets. The algorithm makes multiple estimations of the proximity between a pair of samples, in low-dimensional spaces. To compute proximity, FiRE uses Sketching, a variant of locality sensitive hashing. For more details: Jindal, A., Gupta, P., Jayadeva and Sengupta, D., 2018. Discovery of rare cells from voluminous single cell expression data. Nature Communications, 9(1), p.4719. . Package: r-cran-firm Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1430 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-seurat, r-cran-rann, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-firm_0.1.2-1.ca2604.1_arm64.deb Size: 693216 MD5sum: 998c113b2da3071f17f461379453db21 SHA1: 13fe5ea4b8862a627823f4dff256fc9aff7d5074 SHA256: 38062930c78eaa3460524fbcac342509c5716cf024bec03b839f881d7c33c74c SHA512: e8cb686607a73e7a1a7fa52ef07e564f6f2468600c7e36159f7c3fede28487d648aa60c1c80ea59d754ef1205f3ee6a8518870520dd40f5992532365a1a2fecb Homepage: https://cran.r-project.org/package=FIRM Description: CRAN Package 'FIRM' (Flexible Integration of Single-Cell RNA-Seq Data) Provides functions for the flexible integration of heterogeneous scRNA-seq datasets across multiple tissue types, platforms, and experimental batches. Implements the method described in Ming (2022) . The package incorporates modified 'C++' source code from the 'flashpca' library (Abraham, 2014-2016 ) for efficient principal component analysis, and the 'Spectra' library (Qiu, 2016-2025) for large-scale eigenvalue and singular value decomposition; see 'inst/COPYRIGHTS' for details on third-party code. Package: r-cran-fishmethods Architecture: arm64 Version: 1.13-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2345 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-mass, r-cran-boot, r-cran-lme4, r-cran-bootstrap, r-cran-numderiv, r-cran-tmb Filename: pool/dists/resolute/main/r-cran-fishmethods_1.13-1-1.ca2604.1_arm64.deb Size: 1628390 MD5sum: 92446777970f78fb98b509994d62fcae SHA1: 613da7003e67736a5f196c90b5772a39a9ba2358 SHA256: a5da566fb5c2f7257ca3a91677ff1e5850d5ecc580b91af2491937e9fc7028af SHA512: 7f6106766ceafc401dedf7bae8b54a594c9eeaacf0a87d5347d378fdfa24d8252c49963e6e1198ce638306c5ba1ac950699de9fb2ce6961389a2efaa485c3b17 Homepage: https://cran.r-project.org/package=fishmethods Description: CRAN Package 'fishmethods' (Fishery Science Methods and Models) Functions for applying a wide range of fisheries stock assessment methods. Package: r-cran-fishmod Architecture: arm64 Version: 0.29.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fishmod_0.29.2-1.ca2604.1_arm64.deb Size: 107698 MD5sum: 624c6a1ac6ac99893661654e1de457fb SHA1: a5455789f93e312cf40ebaa3c8587a5d4b1e3075 SHA256: 922dff2df8c5e3dc06c3239588d4fdeb35176f2f5053890427fd9dc784afe957 SHA512: 823927eed09bb928caa9305e608caf594eaddc564dd5225c363629fe6a7bf91e1f0b038f419385dfd01418123eacdb094318dee6b84ac499e1349a32803fa87c Homepage: https://cran.r-project.org/package=fishMod Description: CRAN Package 'fishMod' (Fits Poisson-Sum-of-Gammas GLMs, Tweedie GLMs, and DeltaLog-Normal Models) Fits models to catch and effort data. Single-species models are 1) delta log-normal, 2) Tweedie, or 3) Poisson-gamma (G)LMs. Package: r-cran-fispro Architecture: arm64 Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5374 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rdpack, r-cran-rcpp, r-cran-bh Suggests: r-cran-testthat, r-cran-rlang, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-fispro_1.1.4-1.ca2604.1_arm64.deb Size: 745350 MD5sum: 83e25a445dfcd562e38be56e906d8962 SHA1: 8ff74b590a4b822046ce4ed9fb156b651385b953 SHA256: 782ef1d67f746fe96ab0e8bbb9b1ee6222eab32d2d4918556b92ce777f59ddac SHA512: 8e09e10b5a6e76966a75ba4e7960ad63d3adf84b5eee7c237c172b39ca33daec2185cfd6f424d08a147be70cdc8e84ca19f3c2a78ef3b3baebcdf7666d7e15a7 Homepage: https://cran.r-project.org/package=FisPro Description: CRAN Package 'FisPro' (Fuzzy Inference System Design and Optimization) Fuzzy inference systems are based on fuzzy rules, which have a good capability for managing progressive phenomenons. This package is a basic implementation of the main functions to use a Fuzzy Inference System (FIS) provided by the open source software 'FisPro' . 'FisPro' allows to create fuzzy inference systems and to use them for reasoning purposes, especially for simulating a physical or biological system. Package: r-cran-fixes Architecture: arm64 Version: 0.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1235 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-fixest, r-cran-broom, r-cran-tibble, r-cran-rlang, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-haven, r-cran-testthat, r-cran-plotly, r-cran-tidyr, r-cran-did, r-cran-didimputation Filename: pool/dists/resolute/main/r-cran-fixes_0.8.1-1.ca2604.1_arm64.deb Size: 806502 MD5sum: 63cf2cb0b39ce162ae0b3ad6334eee05 SHA1: a4515b0b093501f7f4c4c89a97c84872f2b28303 SHA256: 029c1c66860fb3b802526aac3f8f8e53053381457306c34199e325d50e010fe9 SHA512: f350d487caedf1752621f460d46afc190bc37ef4fd6b879dfac6cfbd1c98d6cbfa2dab93a60b1a863ec53910b03d2b87b5295dd7c16cf6273a5aa2529ebc454e Homepage: https://cran.r-project.org/package=fixes Description: CRAN Package 'fixes' (Tools for Creating and Visualizing Fixed-Effects Event StudyModels) Provides functions for creating, analyzing, and visualizing event study models using fixed-effects regression. Supports staggered adoption, multiple confidence intervals, flexible clustering, and panel/time transformations in a simple workflow. Package: r-cran-fixest Architecture: arm64 Version: 0.14.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8074 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-numderiv, r-cran-nlme, r-cran-sandwich, r-cran-rcpp, r-cran-dreamerr, r-cran-stringmagic Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-data.table, r-cran-plm, r-cran-mass, r-cran-pander, r-cran-ggplot2, r-cran-lfe, r-cran-tinytex, r-cran-pdftools, r-cran-emmeans, r-cran-estimability, r-cran-aer, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-fixest_0.14.1-1.ca2604.1_arm64.deb Size: 3496644 MD5sum: 37d09257b654a47b87b93a066ce4c9cb SHA1: 03cc4345e79a685d00fa1cd9ea063fa122c0d75e SHA256: bcb585694a0ecb7d7fa117708ed6f0fa1327b0e6a729d94b42f5fd2705a90937 SHA512: 1837a7b5f2008edf1b16032eea076275736edfc534a73e47bf1f33aef19aaac29e42e3ba27ea20061070ba77937586148a2c29e757f2d6ffbec00a6d4a44d0ce Homepage: https://cran.r-project.org/package=fixest Description: CRAN Package 'fixest' (Fast Fixed-Effects Estimations) Fast and user-friendly estimation of econometric models with multiple fixed-effects. Includes ordinary least squares (OLS), instrumental variables (IV), generalized linear models (GLM), maximum likelihood estimation (ML), and the negative binomial. The core of the package is based on optimized parallel C++ code, scaling especially well for large data sets. The method to obtain the fixed-effects coefficients is based on Bergé, Butts, McDermott (2026) . Further provides tools to export and view the results of several estimations with intuitive design to change the standard-errors. Package: r-cran-fkf.sp Architecture: arm64 Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mathjaxr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-fkf, r-cran-nfcp Filename: pool/dists/resolute/main/r-cran-fkf.sp_0.3.4-1.ca2604.1_arm64.deb Size: 76424 MD5sum: 45aad6ef2a3e9ed70393826ea3bc186e SHA1: 7b8af3ff9a1fd53847e57f64feb86ac28a561798 SHA256: a45e850d062e12b4f72563f50cfc2628cc594279b702155d0e7117289d96b78f SHA512: 2609772d064c0a6217fb2d4365f8faac14dc07d8ba34c8b5fa77e960589b93ff256952dc79058a7273da256bff2ea21932a9d28e63f61f03fd35cfcd9e099afc Homepage: https://cran.r-project.org/package=FKF.SP Description: CRAN Package 'FKF.SP' (Fast Kalman Filtering Through Sequential Processing) Fast and flexible Kalman filtering and smoothing implementation utilizing sequential processing, designed for efficient parameter estimation through maximum likelihood estimation. Sequential processing is a univariate treatment of a multivariate series of observations and can benefit from computational efficiency over traditional Kalman filtering when independence is assumed in the variance of the disturbances of the measurement equation. Sequential processing is described in the textbook of Durbin and Koopman (2001, ISBN:978-0-19-964117-8). 'FKF.SP' was built upon the existing 'FKF' package and is, in general, a faster Kalman filter/smoother. Package: r-cran-fkf Architecture: arm64 Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fkf_0.2.6-1.ca2604.1_arm64.deb Size: 123764 MD5sum: 74cc50cafccb7d9a9466f8fe7450bd1b SHA1: 8f5f43f443b251113bc8e5b38e98f4e37fcee1c6 SHA256: 2ad14362f3ad324334b64873ff11d38f578fd6f0f27e94f8e8410ac462c1ab96 SHA512: 658f3e58c6a881fa488d91e6864a2e0b7e779e86f7ecbbee2fc10f264cbbd1e30e3da1eb07ad5cd3fbcf9ecd2aa096d56117fefa264bfeb420d62aea74f375a5 Homepage: https://cran.r-project.org/package=FKF Description: CRAN Package 'FKF' (Fast Kalman Filter) This is a fast and flexible implementation of the Kalman filter and smoother, which can deal with NAs. 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Package: r-cran-fksum Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 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-rarpack, r-cran-mass, r-cran-matrix, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-fksum_1.0.1-1.ca2604.1_arm64.deb Size: 220040 MD5sum: 9caeec8b23a9c14dd2dd1044c106fe71 SHA1: 20fd68606b23f7ab943ac28513437a367b6b990f SHA256: b14398bf1d1be855888f4f411e9901ef35a7bbb6af67032fadb4f68d9dd4622d SHA512: 36c75c6902a26774d8f88eeb79d8461c1a86ffdc23434dcedfd8ac70d69f3ad2ef05c83af792a43e617a13005904f34d7f4342ee3518b94b7f372a281bcc045a Homepage: https://cran.r-project.org/package=FKSUM Description: CRAN Package 'FKSUM' (Fast Kernel Sums) Implements the method of Hofmeyr, D.P. (2021) for fast evaluation of univariate kernel smoothers based on recursive computations. Applications to the basic problems of density and regression function estimation are provided, as well as some projection pursuit methods for which the objective is based on non-parametric functionals of the projected density, or conditional density of a response given projected covariates. The package is accompanied by an instructive paper in the Journal of Statistical Software . Package: r-cran-flam Architecture: arm64 Version: 3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 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 Filename: pool/dists/resolute/main/r-cran-flam_3.2-1.ca2604.1_arm64.deb Size: 163878 MD5sum: a823ed12db5b9a8f5c04a1bcdf6227dd SHA1: 6739bbe599a3452c328b74854abe5e722a68f419 SHA256: 830196bba74d7ae507c337569587f9b00ed684255b4e3155bf8d0651f0412f33 SHA512: 871eadf49237edcdf665105bba7878fee78a1131f74e80a31cef40d7c8594e3446975aa8a81353e4970a158806aee9c37a159b03467eb5dc782dc727df7dfdd7 Homepage: https://cran.r-project.org/package=flam Description: CRAN Package 'flam' (Fits Piecewise Constant Models with Data-Adaptive Knots) Implements the fused lasso additive model as proposed in Petersen, A., Witten, D., and Simon, N. (2016). Fused Lasso Additive Model. Journal of Computational and Graphical Statistics, 25(4): 1005-1025. Package: r-cran-flan Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 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_arm64.deb Size: 309574 MD5sum: 93e9abc31caa0e80cfebc2dea3020fce SHA1: 3224ccb034057fdeb5c2560862a0a3177aca1d0f SHA256: 1ea0b342685159eaa711f1b716cc8d0d12f1ab0a6728d5af1a02872aebc7eb9e SHA512: 628fea7d5ed74cff5267d75bd9e893f662da1aeb59873558cf14c6f47c8dd15b50d1009535d619f0cf70941379b2f2248618178ab3240ae2294a6a0c85265b50 Homepage: https://cran.r-project.org/package=flan Description: CRAN Package 'flan' (FLuctuation ANalysis on Mutation Models) Tools for fluctuations analysis of mutant cells counts. Main reference is A. Mazoyer, R. Drouilhet, S. Despreaux and B. Ycart (2017) . Package: r-cran-flankr Architecture: arm64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 Depends: libc6 (>= 2.17), 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-flankr_1.2.0-1.ca2604.1_arm64.deb Size: 185772 MD5sum: 3496734d0ef327a2aa960ee056ec5d25 SHA1: 1697683e0ae8af1ea292d8b1e344f7d437366329 SHA256: a92a05f5754b7bf7910a065e3a2c5a84962d018f73dbc27a15c6291c64815025 SHA512: 74aa469e709124b020e3063b1015ee1ebe83a64cb3ed9d2d973c2dd381f4d2dca14904896850201c2697b22de450037387bff745ae0ac2dac563896d01e732ec Homepage: https://cran.r-project.org/package=flankr Description: CRAN Package 'flankr' (Implementing Computational Models of Attentional Selectivity) A set of methods to simulate from and fit computational models of attentional selectivity. 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Package: r-cran-flare Architecture: arm64 Version: 1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 818 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-mass, r-cran-matrix, r-cran-igraph Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-flare_1.8-1.ca2604.1_arm64.deb Size: 737146 MD5sum: c519212489d1a5b92d922c219fd310c3 SHA1: ca01f3425a046d42f9b3b736694b3103185b48f0 SHA256: 7f917a9f448833198bcf7d77f0883966f52b3115385145b57153a67fd05a6f0f SHA512: 4cc3202c72b57329521f4bf8a920f277c9b3f084caf1a218d2f521dcbbd05405100dfa3d49446eecfe352563cb863700b650cf81e63f2d7aa5228618ee7db5fa Homepage: https://cran.r-project.org/package=flare Description: CRAN Package 'flare' (Family of Lasso Regression) Provides implementations of a family of Lasso variants, including Dantzig Selector, LAD Lasso, SQRT Lasso, and Lq Lasso, for estimating high-dimensional sparse linear models. 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Package: r-cran-flashclust Architecture: arm64 Version: 1.1-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-flashclust_1.1-4-1.ca2604.1_arm64.deb Size: 22844 MD5sum: dc264a0475ae2f0ce8dccbd7fb121361 SHA1: d7237a3dac9ba12f12b31ac050d4682e39791690 SHA256: 4290da3bd27f7428e0d9dac8954d13a6fc1d5f2a853c1d52254aec98c4efedfe SHA512: 3b25c88b2ae44d06c231c5d601cd0925d130727ba19ee75e557b2b899bcbf07f0239e680a2bb38469b21356c5a118d6ced0861af14e52182ec319263b66a118b Homepage: https://cran.r-project.org/package=flashClust Description: CRAN Package 'flashClust' (Implementation of Fast Hierarchical Clustering) A fast implementation of hierarchical clustering that incorporates original code from Fionn Murtagh. 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Package: r-cran-flexbcf Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 456 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 Filename: pool/dists/resolute/main/r-cran-flexbcf_1.0.2-1.ca2604.1_arm64.deb Size: 154872 MD5sum: ad2725761fe7f063c326bf6caee7f42c SHA1: 2a88687cded84896b80767e63fb08afc052a8faa SHA256: afa04bcb04e1aa6ad0a22540eaa6764bef2edc01f00349609a2537e4191d5f8a SHA512: 14370b409bdcee8264f83ae88cae34fbe45286c0a7b6f8d434c376b63287c432bd3e1683b636400ff40d945df9a46a2bc12258b6901faca5a8a6656df217e1d3 Homepage: https://cran.r-project.org/package=flexBCF Description: CRAN Package 'flexBCF' (Fast & Flexible Implementation of Bayesian Causal Forests) A faster implementation of Bayesian Causal Forests (BCF; Hahn et al. 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These include various forms of the negative binomial (NB-1, NB-2, NB-P, generalized negative binomial, etc.), Poisson-Lognormal, other compound Poisson distributions, the Generalized Waring model, etc. Information on the different forms of the negative binomial are described by Greene (2008) . For treatises on count models, see Cameron and Trivedi (2013) and Hilbe (2012) . For the implementation of under-reporting in count models, see Wood et al. (2016) . For prediction methods in random parameter models, see Wood and Gayah (2025) . For estimating random parameters using maximum simulated likelihood, see Greene and Hill (2010) ; Gourieroux and Monfort (1996) ; or Hensher et al. (2015) . 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Package: r-cran-flexpolyline Architecture: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2290 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_arm64.deb Size: 658256 MD5sum: 25354c09ccee1b21c3a769158c14e14b SHA1: 942be51f1589a40a0e639d64c980596340a4cd05 SHA256: fbb0138b272971034a246ddf0944d3a1cd44cfcb43259f37a3761a214c862803 SHA512: f677f530e2fc2777ec7fbffd74095b8441e9a81f8c69293827b6066b03e2324113d0ddf5847451bddc82e8450ddfa229364c7ba38480a858816f6c8ba52f7973 Homepage: https://cran.r-project.org/package=flexpolyline Description: CRAN Package 'flexpolyline' (Flexible Polyline Encoding) Binding to the C++ implementation of the flexible polyline encoding by HERE . The flexible polyline encoding is a lossy compressed representation of a list of coordinate pairs or coordinate triples. The encoding is achieved by: (1) Reducing the decimal digits of each value; (2) encoding only the offset from the previous point; (3) using variable length for each coordinate delta; and (4) using 64 URL-safe characters to display the result. Package: r-cran-flexreg Architecture: arm64 Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9372 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_arm64.deb Size: 1968300 MD5sum: c7763bbd45bae6382f748c3af3917f35 SHA1: 9743ac89f70b8ddb345b5ffc5a42ed6625411925 SHA256: ac9b101508bef17d1454af02961759128f9d3c7b0bc78c4e3711697509c29bbb SHA512: ef6ae82c01372305c7e8e870fe33043ebcdfc0f212745063c7cb9a78572a23ff0202dfd0d49a99192596ab2e36d9d7137ad747022edda553e55edde2c2ef5e55 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: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 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_arm64.deb Size: 189102 MD5sum: 7e39ffc730d0bdf61e4e46bc0db27568 SHA1: 87c0d770df773b76edb89f00d49fcea9e3fb16c8 SHA256: 221ea7e31b3b8d74f684db64d07ac5f312b1305673306b97643cdb4e9dbab8d7 SHA512: 5fe02bebad49960e093996a73aa52edf6e75e2d403271105ebd13f757ecff500b73a8d6eed52d24e73d24f8e0a31daa09b92dc6e942600dfa6af47e0bb688b15 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. Package: r-cran-flexrsurv Architecture: arm64 Version: 2.0.18-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1659 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-orthogonalsplinebasis, r-cran-epi, r-cran-formula, r-cran-formula.tools, r-cran-statmod, r-cran-numderiv, r-cran-r.utils, r-cran-matrix Suggests: r-cran-relsurv, r-cran-mexhaz, r-cran-ggplot2, r-cran-date, r-cran-lubridate Filename: pool/dists/resolute/main/r-cran-flexrsurv_2.0.18-1.ca2604.1_arm64.deb Size: 1490850 MD5sum: f493e64b84d6b5e1720dbc919722ab64 SHA1: 1f54863580c661aea879fd3307b0e45b7c6e753f SHA256: cebace5821476eebf96a74359cb54132611895b86e6d2af3cc1c7ea16762225a SHA512: 85e6c74cf5f6b512b740cdef6319e64141c0f49294f134d3cb4654f7194aff88a7609c222259a10b82279d26a9ecf4e898acb0409b5c5d555efd64217ddafd32 Homepage: https://cran.r-project.org/package=flexrsurv Description: CRAN Package 'flexrsurv' (Flexible Relative Survival Analysis) Package for parametric relative survival analyses. It allows to model non-linear and non-proportional effects and both non proportional and non linear effects, using splines (B-spline and truncated power basis), Weighted Cumulative Index of Exposure effect, with correction model for the life table. Both non proportional and non linear effects are described in Remontet, L. et al. (2007) and Mahboubi, A. et al. (2011) . Package: r-cran-flexsurv Architecture: arm64 Version: 2.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3176 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-assertthat, r-cran-desolve, r-cran-generics, r-cran-magrittr, r-cran-mstate, r-cran-matrix, r-cran-muhaz, r-cran-mvtnorm, r-cran-numderiv, r-cran-quadprog, r-cran-rcpp, r-cran-rlang, r-cran-rstpm2, r-cran-purrr, r-cran-statmod, r-cran-tibble, r-cran-tidyr, r-cran-dplyr, r-cran-tidyselect, r-cran-ggplot2 Suggests: r-cran-splines2, r-cran-flexsurvcure, r-cran-survminer, r-cran-lubridate, r-cran-rmarkdown, r-cran-colorspace, r-cran-eha, r-cran-knitr, r-cran-msm, r-cran-testthat, r-cran-th.data, r-cran-broom, r-cran-covr Filename: pool/dists/resolute/main/r-cran-flexsurv_2.3.2-1.ca2604.1_arm64.deb Size: 2506394 MD5sum: a0a22b5d364cad2fc715a3a1f78b0a09 SHA1: f771210a870eb775d5313e65d78ae7b4efd9aadf SHA256: a7a6ec45ad8d9f9774513c6e56c4e5ab527563d0d45c84a925a998caaa6e24f2 SHA512: 97e72250c628b68459765ed9759b21a3f9689af681648d05d6dbf2749d1ed1a7f6c529f5807ee98705bab57a889210414948e9375ed70783b21311f2318bd341 Homepage: https://cran.r-project.org/package=flexsurv Description: CRAN Package 'flexsurv' (Flexible Parametric Survival and Multi-State Models) Flexible parametric models for time-to-event data, including the Royston-Parmar spline model, generalized gamma and generalized F distributions. 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Package: r-cran-flexurba Architecture: arm64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2375 Depends: libc6 (>= 2.17), 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-exactextractr, r-cran-fastmatch, r-cran-geos, r-cran-ggplot2, r-cran-ggspatial, r-cran-jsonlite, r-cran-lifecycle, r-cran-magrittr, r-cran-nngeo, r-cran-rcpp, r-cran-sf, r-cran-terra, r-cran-tidyterra Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-flexurba_0.2.3-1.ca2604.1_arm64.deb Size: 2090282 MD5sum: a25a6b5bc4f245f14b235467a243e84b SHA1: 385548284ecc41341f0f03733c55ac0f41c182ea SHA256: 8469f211422feae193a2c4135fde7a547938c9f48936dcf8af3b52a5858ce8d3 SHA512: b3516c1b382cc95b7b526dbb259af772f9eac2eb67c85dc8ac492a1d04ce8a51b9d3e5ebca67deb9b2f9d1f27e2369c7d89b80f2272ec4efc3b3489130f31606 Homepage: https://cran.r-project.org/package=flexurba Description: CRAN Package 'flexurba' (Construct Flexible Urban Delineations) Enables the construction of flexible urban delineations that can be tailored to specific applications or research questions, see Van Migerode et al. 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Package: r-cran-flexvarjm Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 518 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 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_arm64.deb Size: 329770 MD5sum: 91e44c6bcea165af2bb3e6cb3f7ac5ce SHA1: 0bd7e942c337556bad557ead3e8f3b5d2a98507d SHA256: 21a665149387ab6fd0be410c17fd2199ad7f2faa133c99d849e1aba87aa7b6fe SHA512: 373f55b83c9a78c749b6c77f48c089502eaae7e2145b0b9794fd6caa09c58819d8b413995eb105f2a8a1362e6083630ecb2a99fd624d19d7b9aaad10cc64b111 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. In the joint model framework, the package handles left truncation and allows a flexible dependence structure between the competing events and the longitudinal marker. The estimation is performed under the frequentist framework, using the Marquardt-Levenberg algorithm. (Courcoul, Tzourio, Woodward, Barbieri, Jacqmin-Gadda (2023) ). Package: r-cran-flint Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2665 Depends: libc6 (>= 2.38), libflint22 (>= 3.4.0), libgmp10 (>= 2:6.3.0+dfsg), libmpfr6 (>= 4.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-flint_0.1.4-1.ca2604.1_arm64.deb Size: 1504730 MD5sum: 72b814c7f882641f33a5bb9ec7795d53 SHA1: 23b4b08453e0bd143c1ba98a7cee00486b8e4530 SHA256: c53041c908ed5125165f8ba49728be82c24d19f8008462aa11056b7d2a25cdc0 SHA512: 814bd0d904015d148d29461c74c642251c5769dee57763e48d3e0c050ae17877ed1249cefadbd63a8f57196b910ab96a456f948df03742faae503b335d3f4812 Homepage: https://cran.r-project.org/package=flint Description: CRAN Package 'flint' (Fast Library for Number Theory) An R interface to 'FLINT' , a C library for number theory. 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Package: r-cran-flintyr Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 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-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_arm64.deb Size: 147368 MD5sum: ebb0ae622b2894502f1600aacc1fc13c SHA1: 9a788e4eb27787c6349d822734b18c6d7efc287d SHA256: 63495aa8bf27d334c722aac0d6ced69582e40ac3c77ba8199ba9190d96229d0a SHA512: 318acccb8d16fa482a0185c22584d1fc337d55247bb04a5ea44f9efcd3f7f279db30a1d6c6e9e08a80ff5dc75e36644ef2590bcc76c16026668ead22effa808b 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. Such a procedure implicitly assumes that the sample is exchangeable. This package provides a flexible non-parametric test of this exchangeability assumption, allowing the user to specify the feature dependencies by hand as long as features can be grouped into disjoint independent sets. This package also allows users to test a dual hypothesis, which is, given that the sample is exchangeable, does a proposed grouping of the features into disjoint sets also produce statistically independent sets of features? See Aw, Spence and Song (2023) for the accompanying paper. Package: r-cran-flip Architecture: arm64 Version: 2.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 514 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cherry, r-cran-e1071, r-cran-plyr, r-cran-somemtp Filename: pool/dists/resolute/main/r-cran-flip_2.5.1-1.ca2604.1_arm64.deb Size: 417284 MD5sum: e29d39bcd91687af2ecf3fc16b6a62e8 SHA1: 196a5678f101f529af25de83be88d2fb9ba453d4 SHA256: 40d80f1e072602029672b745686516eed725eb65e5a3de90fa21a767b7af68f5 SHA512: fdc54da51acbba229da84440c1e51fba1929a1394e4c6ed649824c40a8e841b3eb8ca843e0cdc1a2ccb67034e5e23ec0574ed54e4e424a4b63b33211bc5c2808 Homepage: https://cran.r-project.org/package=flip Description: CRAN Package 'flip' (Multivariate Permutation Tests) It implements many univariate and multivariate permutation (and rotation) tests. Allowed tests: the t one and two samples, ANOVA, linear models, Chi Squared test, rank tests (i.e. Wilcoxon, Mann-Whitney, Kruskal-Wallis), Sign test and Mc Nemar. 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See Fei and others (2024) . 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Package: r-cran-flsa Architecture: arm64 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_arm64.deb Size: 77060 MD5sum: 9d1ac2d505b4b81d28eb82d6ea464e1a SHA1: 3a8430c1441b019a26b865b202ecf697b622f991 SHA256: 83b1de8e8ffabea6df679cd0bc8b41c599e7c31ed4767e69efa4761ca3924c24 SHA512: a04e7f7a8b315dd341c342d5b89417eb4417d664ace4ac8572bdc8983485622e324b9defe055a35e0079d57a35ee623f0eeedca736188aef2105b3a813e06624 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) . 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The solvers differ from the mainstream in the options of (i) restricting subset size, (ii) bounding subset elements, (iii) mining real-value multisets with predefined subset sum errors, (iv) finding one or more subsets in limited time. A novel algorithm for mining the one-dimensional Subset Sum induced algorithms for the multi-Subset Sum and the multidimensional Subset Sum. The multi-threaded framework for the latter offers exact algorithms to the multidimensional Knapsack and the Generalized Assignment problems. Historical updates include (a) renewed implementation of the multi-Subset Sum, multidimensional Knapsack and Generalized Assignment solvers; (b) availability of bounding solution space in the multidimensional Subset Sum; (c) fundamental data structure and architectural changes for enhanced cache locality and better chance of SIMD vectorization; (d) option of mapping floating-point instance to compressed 64-bit integer instance with user-controlled precision loss, which could yield substantial speedup due to the dimension reduction and efficient compressed integer arithmetic via bit-manipulations; (e) distributed computing infrastructure for multidimensional subset sum; (f) arbitrary-precision zero-margin-of-error multidimensional Subset Sum accelerated by a simplified Bloom filter. The package contains a copy of 'xxHash' from . Package vignette () detailed a few historical updates. Functions prefixed with 'aux' (auxiliary) are independent implementations of published algorithms for solving optimization problems less relevant to Subset Sum. Package: r-cran-fluidsynth Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 Depends: libc6 (>= 2.17), libfluidsynth3 (>= 2.0.5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-av, r-cran-rappdirs Filename: pool/dists/resolute/main/r-cran-fluidsynth_1.0.2-1.ca2604.1_arm64.deb Size: 53938 MD5sum: 10000988913bfc88ad57679eeba13a9c SHA1: 4f47b904d359a83364e846c6b54b9de255108035 SHA256: 71f8e701218ac6d00487582dec4e7626717c76aec41c415398b7ad5d2071094b SHA512: 4229a08637a9a106d3914424076196efd71fdad7eaa6dec8ccf0530f7e3dd444165ad5fb6cab45c417445f5da52d8edc13811152577d46ca7d37581a6bea6e54 Homepage: https://cran.r-project.org/package=fluidsynth Description: CRAN Package 'fluidsynth' (Read and Play Digital Music (MIDI)) Bindings to 'libfluidsynth' to parse and synthesize MIDI files. It can read MIDI into a data frame, play it on the local audio device, or convert into an audio file. 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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: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 606 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-microbenchmark Filename: pool/dists/resolute/main/r-cran-fmds_0.1.5-1.ca2604.1_arm64.deb Size: 334964 MD5sum: 26f1dd6cc5be72bd9854a57250830beb SHA1: 8cb99dabf0ebfb503a0442ed6b493b077e979033 SHA256: eade396bf84dae4ba09ffdeda8965f117bd7951be6a9f065562b339121e36870 SHA512: 885e007c82dddb4c60e4fd255d9472a6c90d9bb104ec0fd1114d5cfdf3e98bde421ad1cd8a7f3c16741f9f840d063db4255a33634ef2e7e0e04b85d9f363cc3b 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. 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Barroso and Busing (2025) "e-RDOP, Relative Density-Based Outlier Probabilities, Extended to Proximity Mapping". Manuscript submitted for publication. Package: r-cran-fmdu Architecture: arm64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 136222 MD5sum: 16ce681eb116fc3fcffa960e8fd1a238 SHA1: 2fd91bfcfcea6e3a60c0a9860b9803fca75bc53e SHA256: eb1a0b77098d9e30b6af385be02be74885ae9bb2a5ee19aa923dca360dfa3d25 SHA512: 635bffcedd7432ce2561988054780fe882022b4f7f784243cc637bdc1fc91074092a7f4e396da75c6f406a2aee1bfa6d73490806c9f14186126c744c99f27f3d Homepage: https://cran.r-project.org/package=fmdu Description: CRAN Package 'fmdu' ((Restricted) [external] Multidimensional Unfolding) Functions for performing (external) multidimensional unfolding. 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The core 'fmesher' library code was originally part of the 'INLA' package, and implements parts of "Triangulations and Applications" by Hjelle and Daehlen (2006) . 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Package: r-cran-foresight Architecture: arm64 Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2418 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-ga, r-cran-rcpp, r-cran-rlang, r-cran-directlabels, r-cran-cowplot, r-cran-jsonlite, r-cran-progress, r-cran-scales, r-cran-viridislite, r-cran-fields, r-cran-lattice, r-cran-mvtnorm, r-cran-matrix, r-cran-soilhyp, r-cran-dfoptim, r-cran-rgn, r-cran-foreach, r-cran-blrpm, r-cran-doparallel, r-cran-dplyr, r-cran-lubridate, r-cran-tidyr, r-cran-zoo, r-cran-airgr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-foresight_2.0.0-1.ca2604.1_arm64.deb Size: 1698246 MD5sum: 732cf068dc3ddcddc230097fba4aa7bd SHA1: 8e1cc9a7ae82a2d281bad5c52b3aeca97f308d9a SHA256: 4d361a00a7eea22c39aa2c06173067a3d396648651e154c00936856ccf28fe73 SHA512: 0a40ea08de7ea359f1dbddf08241d5504ea1c7c9d3b603111ff4f75db6c975f762428e026ec4d1748d450555a332777a0589837f0675f34ccf898a884eae0397 Homepage: https://cran.r-project.org/package=foreSIGHT Description: CRAN Package 'foreSIGHT' (Systems Insights from Generation of Hydroclimatic Timeseries) A tool to create hydroclimate scenarios, stress test systems and visualize system performance in scenario-neutral climate change impact assessments. Scenario-neutral approaches 'stress-test' the performance of a modelled system by applying a wide range of plausible hydroclimate conditions (see Brown & Wilby (2012) and Prudhomme et al. (2010) ). These approaches allow the identification of hydroclimatic variables that affect the vulnerability of a system to hydroclimate variation and change. This tool enables the generation of perturbed time series using a range of approaches including simple scaling of observed time series (e.g. Culley et al. (2016) ) and stochastic simulation of perturbed time series via an inverse approach (see Guo et al. (2018) ). It incorporates 'Richardson-type' weather generator model configurations documented in Richardson (1981) , Richardson and Wright (1984), as well as latent variable type model configurations documented in Bennett et al. (2018) , Rasmussen (2013) , Bennett et al. (2019) to generate hydroclimate variables on a daily basis (e.g. precipitation, temperature, potential evapotranspiration) and allows a variety of different hydroclimate variable properties, herein called attributes, to be perturbed. Options are included for the easy integration of existing system models both internally in R and externally for seamless 'stress-testing'. A suite of visualization options for the results of a scenario-neutral analysis (e.g. plotting performance spaces and overlaying climate projection information) are also included. Version 1.0 of this package is described in Bennett et al. (2021) . As further developments in scenario-neutral approaches occur the tool will be updated to incorporate these advances. Package: r-cran-forestbalance Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-grf, r-cran-mass, r-cran-matrix, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-osqp, r-cran-rmarkdown, r-cran-testthat, r-cran-weightit Filename: pool/dists/resolute/main/r-cran-forestbalance_0.1.0-1.ca2604.1_arm64.deb Size: 287430 MD5sum: 6ae466e5a324ee31921aac6f46c06145 SHA1: 6963ab775b1ec251eddfa94fd6c1da6ed6768638 SHA256: 0056b3f54e113387c38fdea1e20340a413c7090947f112c10a11faccb74eb43a SHA512: ecd5cc9f8eca9568fb26ce40f5b2704dac5d6ba5195c15ce32b55a786a6e82cdee7ab5a5f831aa8ebffe3a04c070313d5283c344565305b5d4285c45edcd28bf Homepage: https://cran.r-project.org/package=forestBalance Description: CRAN Package 'forestBalance' (Balancing Confounder Distributions with Forest Energy Balancing) Estimates average treatment effects using kernel energy balancing with random forest similarity kernels. A multivariate random forest jointly models covariates, outcome, and treatment to build a similarity kernel between observations. This kernel is then used for energy balancing to create weights that control for confounding. The method is described in De and Huling (2025) . Package: r-cran-forestcontrol Architecture: arm64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 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-purrr, r-cran-tibble, r-cran-magrittr, r-cran-dplyr Suggests: r-cran-testthat, r-cran-randomforest, r-cran-ranger, r-cran-parsnip, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-forestcontrol_0.2.2-1.ca2604.1_arm64.deb Size: 150394 MD5sum: d5ecd3be88dcc049c05a1983288abac2 SHA1: 57d41b8587e07f9ebe3d8d2537b08020a8e9d3de SHA256: e5d594f81007c3c06dec1a5bcbcd94d3b44b2ecd4db88e1e599c890936fce313 SHA512: 3b98e68b2fd1743f707a92b672b354fc00749d66f2b351de79dc2789a075f6cfb299393bb8e00593109658fdf8717f72a1bd70dd53f01198cc0888826d3ec23d Homepage: https://cran.r-project.org/package=forestControl Description: CRAN Package 'forestControl' (Approximate False Positive Rate Control in Selection Frequencyfor Random Forest) Approximate false positive rate control in selection frequency for random forest using the methods described by Ender Konukoglu and Melanie Ganz (2014) . Methods for calculating the selection frequency threshold at false positive rates and selection frequency false positive rate feature selection. Package: r-cran-fortls Architecture: arm64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4872 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-dbscan, r-cran-distance, r-cran-htmlwidgets, r-cran-lidr, r-cran-moments, r-cran-plotly, r-cran-progress, r-cran-raster, r-cran-rcpp, r-cran-rcsf, r-cran-reticulate, r-cran-scales, r-cran-sf, r-cran-tidyr, r-cran-voxr, r-cran-vroom, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-devtools, r-cran-kableextra, r-cran-knitr, r-cran-rmarkdown, r-cran-systemfonts, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fortls_2.0.1-1.ca2604.1_arm64.deb Size: 4119074 MD5sum: 251d895a4a7f6d21739378976c0f7f6d SHA1: a95ad115a09ccd1c59f31ca28f520e31ea339bf1 SHA256: 7f691508707f82d45087a8f963aa580e9090a82d5a9d63a3ccf70441ff6307f5 SHA512: b8e0efdce87e7fe74845b5c893b94e48840e51f63e8df10b87458060766713e89746261290aa6b09ce0047be1482127b152d76d75a5523df55161dbed3779e45 Homepage: https://cran.r-project.org/package=FORTLS Description: CRAN Package 'FORTLS' (Automatic Processing of Terrestrial-Based Technologies PointCloud Data for Forestry Purposes) Process automation of point cloud data derived from terrestrial-based technologies such as Terrestrial Laser Scanner (TLS) or Mobile Laser Scanner. 'FORTLS' enables (i) detection of trees and estimation of tree-level attributes (e.g. diameters and heights), (ii) estimation of stand-level variables (e.g. density, basal area, mean and dominant height), (iii) computation of metrics related to important forest attributes estimated in Forest Inventories at stand-level, and (iv) optimization of plot design for combining TLS data and field measured data. Documentation about 'FORTLS' is described in Molina-Valero et al. (2022, ). Package: r-cran-fourierin Architecture: arm64 Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 467 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 Suggests: r-cran-mass, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-tidyr, r-cran-purrr, r-cran-ggplot2, r-cran-lattice, r-cran-rbenchmark, r-cran-testthat, r-cran-covr, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-fourierin_0.2.5-1.ca2604.1_arm64.deb Size: 142878 MD5sum: 81fec32309a203af20dfe317315270f7 SHA1: 6f028d4f447e154cbc45e3fd3af8cb97ad0e6104 SHA256: 171e0a717e25496b22394450a11c0fc6b896d47e36d772a71e1ac9f79858053e SHA512: 37cc75b8e770db6d603182c7ae8e92c002c89c3546d65cd5019baa1db6b4c4fe1df83e3ac3de57bc6f41099f1c306511b416c95f0b2916bdfbc6999bb8c370d1 Homepage: https://cran.r-project.org/package=fourierin Description: CRAN Package 'fourierin' (Computes Numeric Fourier Integrals) Computes Fourier integrals of functions of one and two variables using the Fast Fourier transform. The Fourier transforms must be evaluated on a regular grid for fast evaluation. Package: r-cran-fozziejoin Architecture: arm64 Version: 0.0.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4985 Depends: libc6 (>= 2.39), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble Suggests: r-cran-babynames, r-cran-dplyr, r-cran-fuzzyjoin, r-cran-knitr, r-cran-microbenchmark, r-cran-qdapdictionaries, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fozziejoin_0.0.13-1.ca2604.1_arm64.deb Size: 1425412 MD5sum: ae4edb4d968fe385ca503b6ae2b6271b SHA1: 740b564f64a5552468914f9990f064d71dcc5960 SHA256: dc1cbc0abf4ad6b3b4271beb18aadee6259d9f4465e59fb41fce91f9e57d0215 SHA512: a290cfe01ea6ff031667a77d7123d034a4a5e072df5edf133cbf59b108bd22ca489814691e2975780d06b209a1e7ccddd4858127434f4481e828fabcfc11e45a Homepage: https://cran.r-project.org/package=fozziejoin Description: CRAN Package 'fozziejoin' (Utilities for Joining Dataframes with Inexact Matching) Provides functions for joining data frames based on inexact criteria, including string distance, Manhattan distance, Euclidean distance, and interval overlap. This API is designed as a modern, performance-oriented alternative to the 'fuzzyjoin' package (Robinson 2026) . String distance functions utilizing 'q-grams' are adapted with permission from the 'textdistance' 'Rust' crate (Orsinium 2024) . Other string distance calculations rely on the 'rapidfuzz' 'Rust' crate (Bachmann 2023) . Interval joins are backed by a Adelson-Velsky and Landis tree as implemented by the 'interavl' 'Rust' crate . Package: r-cran-fpeek Architecture: arm64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 68994 MD5sum: c122cc18b8bd0fb676fd1264036ee6df SHA1: c60725479af701e630d222cec575596e083a28cc SHA256: 84c2cab7ce27855d68e9e9fb1720833d518763a2abaee95da184bebab7d6fdca SHA512: 86efbae663b646e25e5d34cd5be42f9d0a28335a0d7a7eede580b9c7ba9ae651597e00e515275ecb8a22d2202b4e66d27bf889eb8036f57d61f7bf8657722696 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. Operations are made without reading the entire file before starting, resulting in good performances with large files. This package provides an alternative to a simple use of the 'head', 'tail', 'wc' and 'iconv' programs that are not always available on machine where R is installed. Package: r-cran-fplot Architecture: arm64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3107 Depends: libc6 (>= 2.17), 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-stringmagic, r-cran-dreamerr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-fixest, r-cran-pdftools Filename: pool/dists/resolute/main/r-cran-fplot_1.2.0-1.ca2604.1_arm64.deb Size: 2491634 MD5sum: 80c8175029c7b8440434d032fbefa993 SHA1: 00119dbda3124ad2693389a2a2c5e11525950e38 SHA256: 8874af567f8becb43b378bfdac07aba22597244ea6f7ebe9fc2b13fda7460d7f SHA512: 2bca6e61022c5e5f9fa57e5d8ad817e2ae7f997d281d09833ff0a971a9434a7b776c8719ffcade4ae0fa20763f6f6f46312646c56d56ebea146afc01f5591e0f Homepage: https://cran.r-project.org/package=fplot Description: CRAN Package 'fplot' (Automatic Distribution Graphs Using Formulas) Easy way to plot regular/weighted/conditional distributions by using formulas. The core of the package concerns distribution plots which are automatic: the many options are tailored to the data at hand to offer the nicest and most meaningful graphs possible -- with no/minimum user input. Further provide functions to plot conditional trends and box plots. See for more information. Package: r-cran-fpod Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3242 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-data.table Suggests: r-cran-knitr, r-cran-mixtools, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fpod_1.0.1-1.ca2604.1_arm64.deb Size: 1517464 MD5sum: 31ff6bc6982d6453cac73a0d9c7de7a3 SHA1: dc6f0207530d37379e3918459910c9f3a4759b10 SHA256: d2010b7e13a6dcb38ed3c5641b4e4faab2be63f167074d30d1830ed3672726a2 SHA512: 2e2663879f38ea817fe85a1cc029ebf8e000c3e3e3af1f0ad9b938510df8e3457171cdd39b910498568bb8451aaa984e1128db3fa7b8e84fde45237f96713d6e Homepage: https://cran.r-project.org/package=fpod Description: CRAN Package 'fpod' (Read and Process 'FPOD' and 'CPOD' Data) Read 'FPOD' and 'CPOD' data into 'R' directly from the 'FPOD' data files (i.e. .CP1, .CP3, .FP1 and .FP3 files). The 'FPOD' data files contain binary data, so they can't trivially be read into 'R' using the usual approach, e.g. fread() or read.csv(). This package decodes the binary data and imports all the data in one go (i.e. header/metadata, clicks, 'KERNO' classifications, environmental data and pseudo-WAV data). It is then trivial to aggregate data as you please, e.g. detection-positive-minutes per time block. The advantage of handling data processing in 'R' is a long topic, but suffice it to say that it 1) simplifies things (many fewer steps, as different vars have to be exported in multiple goes in the official 'FPOD' app), and more importantly, 2) makes data processing transparent and reproducible. References: Pirotta et al. 2014 . Package: r-cran-fpop Architecture: arm64 Version: 2019.08.26-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-fpop_2019.08.26-1.ca2604.1_arm64.deb Size: 29652 MD5sum: 91db77d3a63621e70b162207d62f8e2f SHA1: 61345e498ef0f4f3876a6643432f7c23d7b0a19a SHA256: f75e527474aa12105890e4303416c0898b9ce1aa8a1420482af9347301c01ecd SHA512: c887a6ed521f364aacab884c4c180ddbb739c3e78df97573780a6196afdbd3f7e613c1a52f14339f2399565244b820563a5571668845e13d3846d599565d45e9 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. The 'fpop' package is a wrapper to a C++ implementation of the fpop (Functional Pruning Optimal Partioning) algorithm described in Maidstone et al. 2017 . The problem of detecting changepoints in an univariate sequence is formulated in terms of minimising the mean squared error over segmentations. The fpop algorithm exactly minimizes the mean squared error for a penalty linear in the number of changepoints. Package: r-cran-fpopw Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: libc6 (>= 2.17), 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-fpopw_1.1-1.ca2604.1_arm64.deb Size: 61496 MD5sum: e500591e35b5cae67ba72e738f5ea180 SHA1: 3df08ad33d9d6be074bee393d43b2e81d03429ed SHA256: 18bfbeb19c0c255becce629553c951fde3c996ea85bb5908e38a20e54feaabae SHA512: 2836c08e1b7dbb49087a2dc6cc978a64ba35893f6c1d4dfadeb7df28db062d4cc7174095803c8a6e6d8f27182b62b97e93fbedbe0235d0c5ebf13f01d5853af4 Homepage: https://cran.r-project.org/package=fpopw Description: CRAN Package 'fpopw' (Weighted Segmentation using Functional Pruning and OptimalPartioning) Weighted-L2 FPOP Maidstone et al. (2017) and pDPA/FPSN Rigaill (2010) algorithm for detecting multiple changepoints in the mean of a vector. Also includes a few model selection functions using Lebarbier (2005) and the 'capsushe' package. Package: r-cran-fpow Architecture: arm64 Version: 0.0-3-1.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-fpow_0.0-3-1.ca2604.1_arm64.deb Size: 13216 MD5sum: 656deec43b397feefeae774c75af2201 SHA1: 0dfe43264c7f2c9968ec3168522fdb903397d9bc SHA256: 37f40aad05a42515cc2ee39198d807f5d637dfff18f720b04a9448556ec0ecda SHA512: 65fe385e51ff4e637b91f6954988e85763d35265d37bb895dd68fb23b97bdefaf100723882028d97df6f9aeb814c74a4b1c2e383ceac594f147c897dad9d1a30 Homepage: https://cran.r-project.org/package=fpow Description: CRAN Package 'fpow' (Computing the Noncentrality Parameter of the Noncentral FDistribution) Returns the noncentrality parameter of the noncentral F distribution if probability of type I and type II error, degrees of freedom of the numerator and the denominator are given. It may be useful for computing minimal detectable differences for general ANOVA models. This program is documented in the paper of A. Baharev, S. Kemeny, On the computation of the noncentral F and noncentral beta distribution; Statistics and Computing, 2008, 18 (3), 333-340. Package: r-cran-fproc Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 Depends: libc6 (>= 2.17), 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, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-fproc_0.1.0-1.ca2604.1_arm64.deb Size: 199216 MD5sum: 216b6ac6aa0222c48c4756eef43566cb SHA1: f0b87a4ac39fb62073fb52a2d225008898d7361b SHA256: bfe510d79cf8d4d98eafae6e0d1132ad0e0a79e98b4e5b9084a00485f6d50ada SHA512: f73dea94b63fb68173d15278bad2f1b4a3c68d3f4be251fc39c14ebd03a4b3eab40d16de274c2f28d4215f3f03a04cdd7ef24e8c42d7cb538fa3ba5309fd47b1 Homepage: https://cran.r-project.org/package=fpROC Description: CRAN Package 'fpROC' (Fast Partial Receiver Operating Characteristic (ROC) Test forEcological Niche Modeling) Provides optimized 'C++' code for computing the partial Receiver Operating Characteristic (ROC) test used in niche and species distribution modeling. The implementation follows Peterson et al. (2008) . Parallelization via 'OpenMP' was implemented with assistance from the 'DeepSeek' Artificial Intelligence Assistant (). Package: r-cran-frab Architecture: arm64 Version: 0.0-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1342 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 737572 MD5sum: 5a8831e788bc5380a645f5b19bb9abd3 SHA1: 504056a6d31f75f548a36fdf64cdda1dd5f04be4 SHA256: 1508026eb863878314f8a9188223c075d809902b8675a48d5a075df0a526a433 SHA512: 556e535668d7849f28b7f0b32d6d04cdc8f87ae29996bc582ba9d51d2c9b76a63458141cb4b6278bea7209147ca33c30734e91dfb9c670233561a2a95417f1a7 Homepage: https://cran.r-project.org/package=frab Description: CRAN Package 'frab' (How to Add Two R Tables) Methods to "add" two R tables; also an alternative interpretation of named vectors as generalized R tables, so that c(a=1,b=2,c=3) + c(b=3,a=-1) will return c(b=5,c=3). Uses 'disordR' discipline (Hankin, 2022, ). Extraction and replacement methods are provided. The underlying mathematical structure is the Free Abelian group, hence the name. To cite in publications please use Hankin (2023) . Package: r-cran-fracdiff Architecture: arm64 Version: 1.5-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 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_arm64.deb Size: 98116 MD5sum: dcc8e1dac54c058fd23383df3aff8d9c SHA1: 9934ef57ed164a661f7eabcdcf9baa5b8b9228e9 SHA256: edd66b77c5e389724dd92d37f3c58e54f8780cd223cc9f1ae8d521a674c924db SHA512: 918ef2a9686019eb9d0bf0b8d109c3a75b76c1be61f8afa888244eb7d51307bf11183ca9846f1d475c3dfd64f4d036958a3a8d0808815f125d1b5120a9bf366c Homepage: https://cran.r-project.org/package=fracdiff Description: CRAN Package 'fracdiff' (Fractionally Differenced ARIMA aka ARFIMA(P,d,q) Models) Maximum likelihood estimation of the parameters of a fractionally differenced ARIMA(p,d,q) model (Haslett and Raftery, Appl.Statistics, 1989); including inference and basic methods. Some alternative algorithms to estimate "H". Package: r-cran-fractional Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 147828 MD5sum: 5efe1a506d8f42e8a5b89448a3c44ea1 SHA1: b5491ae8229f8e9e9092ef10af3e10ddf17052eb SHA256: 97ec4064cf3429161b0876f32c31d7b1b5d94fe1da0d9329a86d828ab7993e90 SHA512: 66f466341791efa8e0b54328def47bd41e5764c211a0f57621e1643f76860b41cd3f67916a5e9b6030d555873cd461aea81d7e7aa7103a855aeae1c9cb4e3b16 Homepage: https://cran.r-project.org/package=fractional Description: CRAN Package 'fractional' (Vulgar Fractions in R) The main function of this package allows numerical vector objects to be displayed with their values in vulgar fractional form. This is convenient if patterns can then be more easily detected. In some cases replacing the components of a numeric vector by a rational approximation can also be expected to remove some component of round-off error. The main functions form a re-implementation of the functions 'fractions' and 'rational' of the MASS package, but using a radically improved programming strategy. Package: r-cran-fracture Architecture: arm64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 113488 MD5sum: 8114a8c23c94da8c88e393ec2314cd33 SHA1: 8b01c260a02584c83cc63c996b160ca3a0cfde84 SHA256: 83122c15d53326527b76c6ea20f70fb03272b2ebe6c96354a29a05949802d4a3 SHA512: be29a620b39f1e45e05242edeef14587325ddba8c960239a1385db8ee498f2678b6856fcc140d74033e48276173a0c80116edf87b48df336bd25ec451f61d8e9 Homepage: https://cran.r-project.org/package=fracture Description: CRAN Package 'fracture' (Convert Decimals to Fractions) Provides functions for converting decimals to a matrix of numerators and denominators or a character vector of fractions. Supports mixed or improper fractions, finding common denominators for vectors of fractions, limiting denominators to powers of ten, and limiting denominators to a maximum value. Also includes helper functions for finding the least common multiple and greatest common divisor for a vector of integers. Implemented using C++ for maximum speed. Package: r-cran-frailtyem Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 888 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_arm64.deb Size: 688242 MD5sum: 74b1f72f9ab2fcab2a083105affef1b1 SHA1: 382ad34e2214f402f033450ae15ce9657e3b5685 SHA256: 4ea677e4a76bf466a988a0c365753879890ab041268dcf6e584b9d2770faf8fc SHA512: 921121c7c4b0d7738ac2ef2b69a97828b7b9562420a9eb912c12eb13055a6d692b0651ce5dfebb53e63a9e98e6a120f4cdcdc9d1918489b5785aafa5edae819c Homepage: https://cran.r-project.org/package=frailtyEM Description: CRAN Package 'frailtyEM' (Fitting Frailty Models with the EM Algorithm) Contains functions for fitting shared frailty models with a semi-parametric baseline hazard with the Expectation-Maximization algorithm. Supported data formats include clustered failures with left truncation and recurrent events in gap-time or Andersen-Gill format. Several frailty distributions, such as the the gamma, positive stable and the Power Variance Family are supported. Package: r-cran-frailtymmpen Architecture: arm64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1546 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_arm64.deb Size: 1310598 MD5sum: 403bf3aefb4008a6ab5ce14ad0bfb8b0 SHA1: cb7543929ba084a9758f9e26ca696b96865fb963 SHA256: f38d24f2b940f47f88cdcfbb16049d92035bffdfe283832d38924a3dfea29a6d SHA512: eb394c3d9b30ed15d610d5adddde6f5b213c84ed50bf5481e96f3ea62655e03184d51d4d07f93bb0132203c74444bc08d990b83d40dc2a6980dce9bca072d57a Homepage: https://cran.r-project.org/package=frailtyMMpen Description: CRAN Package 'frailtyMMpen' (Efficient Algorithm for High-Dimensional Frailty Model) The penalized and non-penalized Minorize-Maximization (MM) method for frailty models to fit the clustered data, multi-event data and recurrent data. Least absolute shrinkage and selection operator (LASSO), minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalized functions are implemented. All the methods are computationally efficient. These general methods are proposed based on the following papers, Huang, Xu and Zhou (2022) , Huang, Xu and Zhou (2023) . Package: r-cran-frailtypack Architecture: arm64 Version: 3.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8776 Depends: libc6 (>= 2.40), 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_arm64.deb Size: 5551808 MD5sum: 464b97529ac5866fe4e1d2f3f6fa4572 SHA1: 63e95d30211a5ee4960ebaffe32b940de7ad5ddf SHA256: 244082d463b614700216aa5b5c6c34976110460ace048675d92b65a33cbdd9ef SHA512: b56b0be69e134a5e455ede0e0a6487e6bded46f2d447c23814f4e88dadaed6f19e21c7f51d253de87a2accee776143db5e5b1a2a711f23db29d38f51ea8fdf39 Homepage: https://cran.r-project.org/package=frailtypack Description: CRAN Package 'frailtypack' (Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints) The following several classes of frailty models using a penalized likelihood estimation on the hazard function but also a parametric estimation can be fit using this R package: 1) A shared frailty model (with gamma or log-normal frailty distribution) and Cox proportional hazard model. Clustered and recurrent survival times can be studied. 2) Additive frailty models for proportional hazard models with two correlated random effects (intercept random effect with random slope). 3) Nested frailty models for hierarchically clustered data (with 2 levels of clustering) by including two iid gamma random effects. 4) Joint frailty models in the context of the joint modelling for recurrent events with terminal event for clustered data or not. A joint frailty model for two semi-competing risks and clustered data is also proposed. 5) Joint general frailty models in the context of the joint modelling for recurrent events with terminal event data with two independent frailty terms. 6) Joint Nested frailty models in the context of the joint modelling for recurrent events with terminal event, for hierarchically clustered data (with two levels of clustering) by including two iid gamma random effects. 7) Multivariate joint frailty models for two types of recurrent events and a terminal event. 8) Joint models for longitudinal data and a terminal event. 9) Trivariate joint models for longitudinal data, recurrent events and a terminal event. 10) Joint frailty models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time and/or longitudinal endpoints with the possibility to use a mediation analysis model. 11) Conditional and Marginal two-part joint models for longitudinal semicontinuous data and a terminal event. 12) Joint frailty-copula models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time endpoints. 13) Generalized shared and joint frailty models for recurrent and terminal events. Proportional hazards (PH), additive hazard (AH), proportional odds (PO) and probit models are available in a fully parametric framework. For PH and AH models, it is possible to consider type-varying coefficients and flexible semiparametric hazard function. Prediction values are available (for a terminal event or for a new recurrent event). Left-truncated (not for Joint model), right-censored data, interval-censored data (only for Cox proportional hazard and shared frailty model) and strata are allowed. In each model, the random effects have the gamma or normal distribution. Now, you can also consider time-varying covariates effects in Cox, shared and joint frailty models (1-5). The package includes concordance measures for Cox proportional hazards models and for shared frailty models. 14) Competing Joint Frailty Model: A single type of recurrent event and two terminal events. 15) functions to compute power and sample size for four Gamma-frailty-based designs: Shared Frailty Models, Nested Frailty Models, Joint Frailty Models, and General Joint Frailty Models. Each design includes two primary functions: a power function, which computes power given a specified sample size; and a sample size function, which computes the required sample size to achieve a specified power. 16) Weibull Illness-Death model with or without shared frailty between transitions. Left-truncated and right-censored data are allowed. 17) Weibull Competing risks model with or without shared frailty between the transitions. Left-truncated and right-censored data are allowed. Moreover, the package can be used with its shiny application, in a local mode or by following the link below. Package: r-cran-frailtysurv Architecture: arm64 Version: 1.3.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 978 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-nleqslv, r-cran-reshape2, r-cran-ggplot2, r-cran-numderiv, r-cran-rcpp Suggests: r-cran-knitr, r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-frailtysurv_1.3.8-1.ca2604.1_arm64.deb Size: 658856 MD5sum: 8db8769c7494d8cfdd6afbf0afb2e1d9 SHA1: 677a108a71d34ab59ef4c537b69224e8ff1c3277 SHA256: 3be180ea992ac0411cc270ed452b97742b1e9c43f0fb000948e05a7bf1a384e1 SHA512: eee8c6dc0cf2dab782565ac7bd12c3aaa452a042fb4e1aa8bd5753b9d8fe3a147c908e67778947b752cdb7b88784667db444ad716591e9882de3a4fc542a20b7 Homepage: https://cran.r-project.org/package=frailtySurv Description: CRAN Package 'frailtySurv' (General Semiparametric Shared Frailty Model) Simulates and fits semiparametric shared frailty models under a wide range of frailty distributions using a consistent and asymptotically-normal estimator. Currently supports: gamma, power variance function, log-normal, and inverse Gaussian frailty models. Package: r-cran-free Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 45240 MD5sum: ec87126947181e3183d12ca89c2c7271 SHA1: 6f7dc954b95c2a83ebc36c40bbdfbaec5ae78fad SHA256: 73d1f2679933dea51cb0559369e4a8c9a2dda1b59db4fe81e19ff520a0642d5c SHA512: 29b77c6bd76b179fe62d4c18ae6e351849aacbc471d1f1a4400f4892d765697f56c1a4175420d55c59f588f60e4ea7412c619c26972d2a74bf2fb1049c01d07b Homepage: https://cran.r-project.org/package=free Description: CRAN Package 'free' (Flexible Regularized Estimating Equations) Unified regularized estimating equation solver. Currently the package includes one solver with the l1 penalty only. More solvers and penalties are under development. Reference: Yi Yang, Yuwen Gu, Yue Zhao, Jun Fan (2021) . Package: r-cran-freealg Architecture: arm64 Version: 1.1-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1136 Depends: libc6 (>= 2.17), 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 Suggests: r-cran-knitr, r-cran-testthat, r-cran-magrittr, r-cran-markdown, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-freealg_1.1-8-1.ca2604.1_arm64.deb Size: 848038 MD5sum: f05620cafb32802f828c21a6891755cb SHA1: 32fce46f86c5d5c281127517599fe8774265435d SHA256: eb3894dcde96b862ea835c5b35b38d9d4ffcb6287cfa324234ff42f6e6cb4b4a SHA512: 76caf4e6de895d028a55f69ae7deef82e8b45315e7a69e48e2df30a3e0384d32dd47a299f0bd47f9e10a78b5aaef69e98e042aa43002419a681a7d8e761fa1e6 Homepage: https://cran.r-project.org/package=freealg Description: CRAN Package 'freealg' (The Free Algebra) The free algebra in R with non-commuting indeterminates. Uses 'disordR' discipline (Hankin, 2022, ). To cite the package in publications please use Hankin (2022) . Package: r-cran-freebird Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-scalreg, r-cran-rmosek, r-cran-matrix, r-cran-mass Filename: pool/dists/resolute/main/r-cran-freebird_1.0-1.ca2604.1_arm64.deb Size: 54490 MD5sum: b6bf1ce15f82a32d964974a507b7fc86 SHA1: bb5a9239683408bf581a3661de23e1015d3eec82 SHA256: b1b49e921fb829eabed116b05127109c20a515cc238ed682786bf5da865842ea SHA512: 41e703e5012a6b1f0efadccce5935bafd622a4f656e95db31226efb75af5111eebfffd727df011df40de50a99920685195bd8edd3406fd972b9f6f7500aff6ab Homepage: https://cran.r-project.org/package=freebird Description: CRAN Package 'freebird' (Estimation and Inference for High Dimensional Mediation andSurrogate Analysis) Estimates and provides inference for quantities that assess high dimensional mediation and potential surrogate markers including the direct effect of treatment, indirect effect of treatment, and the proportion of treatment effect explained by a surrogate/mediator; details are described in Zhou et al (2022) and Zhou et al (2020) . This package relies on the optimization software 'MOSEK', . Package: r-cran-freestiler Architecture: arm64 Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4310 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_arm64.deb Size: 2318102 MD5sum: 6012626ea0e46bb178791f1f7a151852 SHA1: b6f35962e801f5d1305051fcdd545986a35c07e5 SHA256: 00023ef3bdc47acad154be76509ccc70364168dae8d1845e6fb0b6143f07795f SHA512: c84b17411fd5e9e4e5c96bae72b478c38e40b465dda778da09a82697060f07964a7a60c57e1b53a26d24b716a6e951f44c04b37690139fddc3f99e9e2cfc9dd0 Homepage: https://cran.r-project.org/package=freestiler Description: CRAN Package 'freestiler' (Create Vector Tiles from Spatial Data) Create vector tile archives in 'PMTiles' format from 'sf' spatial data frames. Supports 'Mapbox Vector Tile' ('MVT') and 'MapLibre Tile' ('MLT') output formats. Uses a 'Rust' backend via 'extendr' for fast, in-memory tiling with zero external system dependencies. Package: r-cran-fresa.cad Architecture: arm64 Version: 3.4.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3471 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_arm64.deb Size: 2944524 MD5sum: 122ea8f77248dd16a1fb8f3b6128db73 SHA1: 287261b57bb255ec7e31c420540ea959baef59b2 SHA256: 409a1f29dd85cb3eefa71572c2b2b9c2ad3ef3d6b029bb4d6b73eb145556f219 SHA512: 59219fd710c572a8439de0c579d211518784bdc38f53b1fdbe0ae79c1af8ddf3d543ce119ea27e5847e4e81a35e0f0e4574bdd2750bddfa8c5f15010df30a071 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: arm64 Version: 2.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9152 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_arm64.deb Size: 7542784 MD5sum: 12c629b5ceccd94788fe6b83c8681154 SHA1: 2922d48c91fa29eba0a51b5117e105f5ef8cb148 SHA256: f2a1d7316fa708b32b965bf70a014a86a9e3bbd8651e3964a7e1cdd0251bd6e9 SHA512: e8e551793a50a717ce9aa23edd57323864f9b02a5231357f932683dfa94ea17cea46017aa1533a18f1a0f4aff5c15ff16fe046690a0a8f41c66b51e7bb390931 Homepage: https://cran.r-project.org/package=FRK Description: CRAN Package 'FRK' (Fixed Rank Kriging) A tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach models the field, and hence the covariance function, using a set of basis functions. This fixed-rank basis-function representation facilitates the modelling of big data, and the method naturally allows for non-stationary, anisotropic covariance functions. Discretisation of the spatial domain into so-called basic areal units (BAUs) facilitates the use of observations with varying support (i.e., both point-referenced and areal supports, potentially simultaneously), and prediction over arbitrary user-specified regions. `FRK` also supports inference over various manifolds, including the 2D plane and 3D sphere, and it provides helper functions to model, fit, predict, and plot with relative ease. Version 2.0.0 and above also supports the modelling of non-Gaussian data (e.g., Poisson, binomial, negative-binomial, gamma, and inverse-Gaussian) by employing a generalised linear mixed model (GLMM) framework. Zammit-Mangion and Cressie describe `FRK` in a Gaussian setting, and detail its use of basis functions and BAUs, while Sainsbury-Dale, Zammit-Mangion, and Cressie describe `FRK` in a non-Gaussian setting; two vignettes are available that summarise these papers and provide additional examples. Package: r-cran-frlr Architecture: arm64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 55280 MD5sum: f4c49409d7b3eec1b94d1de2e9bcf908 SHA1: 7eee48ecc08c3b62cc234ba67892e6e672662c5d SHA256: 32d889103aeca75eedc61a065444c84edfc3158615c35b168b4c74c1d1f04c77 SHA512: 6e6fd262bfc61c5649cb70115562786230b5432b2b0ec0b45ac9414f711a5d835f673bba46af4adaa746542f79919c00ff18dcd454f4acbe1d625df7e04cd88a Homepage: https://cran.r-project.org/package=fRLR Description: CRAN Package 'fRLR' (Fit Repeated Linear Regressions) When fitting a set of linear regressions which have some same variables, we can separate the matrix and reduce the computation cost. This package aims to fit a set of repeated linear regressions faster. More details can be found in this blog Lijun Wang (2017) . 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Package: r-cran-fso Architecture: arm64 Version: 2.1-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 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_arm64.deb Size: 94168 MD5sum: 7ecb6344d7c045ee1701b18707dc0d3a SHA1: de6508bfb476cd6f7d3cc3873bb757a0a225c18d SHA256: 44708ee94d28cb185e8740027b1f4e36176e0b44ef578ababfe8b7a41ac0e896 SHA512: a8cdd9b2ab969df4bf7919330bb3783186c6fcbd6b4cd6c23235add2a5b8178e1b92e99212188d5f2bcea1937b65080e2728f89b1307e2d24e94ff8313a1e5b3 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: arm64 Version: 0.9.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 107128 MD5sum: 8506b68f9ddbe268a9ba98dcc57a610e SHA1: bb26d7ee14a381f888921895d8294a7fd87fce87 SHA256: 3b6dd81abccdee35d301ba497182e0fc2ae8b469eb7edf12b2ba8b3dcbe1eb9b SHA512: bbbc1b84587e9c1bd4846debd048c79d7f3a73811740905e3067fd7e7fb7087f13ecb04d87e135c578792150c99a2f4b941929a1df32b22025ae9ff7b05e070d 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-funbootband Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 393 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 166962 MD5sum: a037614185642f6837556857c4cd9495 SHA1: 87942c8153c3b1049f2637bbaa2713dde3da5648 SHA256: 97bf5777c6cab32f7eec9b0aab3c6e1c768d6814e37d902ccb75125f23fad166 SHA512: 0facb672ae7542ed2feb187447b5e3b57e99aeadcb5a45966f893abddcec59cb781e3e7756562dd75a7eb1076b894b6dfb29b34ad3e3357426af893625def714 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. 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Package: r-cran-funcdiv Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 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_arm64.deb Size: 135942 MD5sum: bc53e44f6127608f610bd19a1120767d SHA1: b66a1410575214df67c001b005307283be80d6ed SHA256: 864bb7a44d0784fa5c9ac1ca63fa87401e5a50b2f6ceb18a3922b9961699778c SHA512: ea0b4e505ead485f316ded33377d7086eaf6d0601d323465b79ff33be545b4ea8603997fb6359e37e97c5ea2647932aaca59fcb68e20247861d18f80e3c5d279 Homepage: https://cran.r-project.org/package=FuncDiv Description: CRAN Package 'FuncDiv' (Compute Contributional Diversity Metrics) Compute alpha and beta contributional diversity metrics, which is intended for linking taxonomic and functional microbiome data. See 'GitHub' repository for the tutorial: . Citation: Gavin M. Douglas, Sunu Kim, Morgan G. I. Langille, B. Jesse Shapiro (2023) . Package: r-cran-funcharts Architecture: arm64 Version: 1.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1841 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_arm64.deb Size: 1496880 MD5sum: 0efc55beaf172c5b72d56e236b3b3e86 SHA1: e3fc08486bd84eab344d64dbc96c76a174d35f40 SHA256: cbe706ca40c56940d983fe2ca0aa76bea1e292b6487c23d1acfee4a9a14d6b42 SHA512: 8e35e1a567f20e822c498d9b133e3a76ace9bd44897ddc2a4be6ba9d21efa3b5ef6d5134c4f6d3c75d43364751dd375ddff0c0af9fee5a82d61a6e144e7aeec2 Homepage: https://cran.r-project.org/package=funcharts Description: CRAN Package 'funcharts' (Functional Control Charts) Provides functional control charts for statistical process monitoring of functional data, using the methods of Capezza et al. (2020) , Centofanti et al. (2021) , Capezza et al. (2024) , Capezza et al. (2024) , Centofanti et al. (2025) , Capezza et al. (2025) . The package is thoroughly illustrated in the paper of Capezza et al (2023) . Package: r-cran-funchisq Architecture: arm64 Version: 2.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1010 Depends: libc6 (>= 2.17), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 545488 MD5sum: aa42e3fd20ed9842ab6fba053a2a1030 SHA1: 768abcde843c5e1898140ffcf42281e3ed7576a0 SHA256: c743b3d6f7dfed22cdf94b01abbef5de730677771315e09ffaec9b947ba02a31 SHA512: 19379233b160527933c15e48ede4e173aa39d71d8be8a68a9ada1ff5352fcf6fa4e37f2328741f10c2d337f5627a9103abb2b8432f26b3460e7da8a7170fe03f Homepage: https://cran.r-project.org/package=FunChisq Description: CRAN Package 'FunChisq' (Model-Free Functional Chi-Squared and Exact Tests) Statistical hypothesis testing methods for inferring model-free functional dependency using asymptotic chi-squared or exact distributions. Functional test statistics are asymmetric and functionally optimal, unique from other related statistics. Tests in this package reveal evidence for causality based on the causality-by- functionality principle. They include asymptotic functional chi-squared tests (Zhang & Song 2013) , an adapted functional chi-squared test (Kumar & Song 2022) , and an exact functional test (Zhong & Song 2019) (Nguyen et al. 2020) . The normalized functional chi-squared test was used by Best Performer 'NMSUSongLab' in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges (Hill et al. 2016) . A function index (Zhong & Song 2019) (Kumar et al. 2018) derived from the functional test statistic offers a new effect size measure for the strength of functional dependency, a better alternative to conditional entropy in many aspects. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependency not possible with symmetrical Pearson's chi-squared or Fisher's exact tests. Package: r-cran-funitroots Architecture: arm64 Version: 4052.82-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 707 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-timeseries, r-cran-fbasics, r-cran-urca Suggests: r-cran-runit, r-cran-interp Filename: pool/dists/resolute/main/r-cran-funitroots_4052.82-1.ca2604.1_arm64.deb Size: 603248 MD5sum: 8090f49ecb3b25fd5ef9550722948d17 SHA1: a7ff3535ea07006eab6c96e6d2ddc3874f1a2a03 SHA256: 85759efd6a5fbf62bade785793dabf0907b6d92075f73c2719eb6b89f44afd59 SHA512: eb94491a8b3802112172f8ac90aeb36477f6afb8ca4f969b48455f5ac8bd1d3d760303dd4510377b8cb7d4e7a492c4b7e16a099b5b00fc002b07500f4fb3a914 Homepage: https://cran.r-project.org/package=fUnitRoots Description: CRAN Package 'fUnitRoots' (Rmetrics - Modelling Trends and Unit Roots) Provides four addons for analyzing trends and unit roots in financial time series: (i) functions for the density and probability of the augmented Dickey-Fuller Test, (ii) functions for the density and probability of MacKinnon's unit root test statistics, (iii) reimplementations for the ADF and MacKinnon Test, and (iv) an 'urca' Unit Root Test Interface for Pfaff's unit root test suite. Package: r-cran-funmodisco Architecture: arm64 Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5909 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 5464628 MD5sum: c0163465c7c6b1db27c43608b0f4a1ef SHA1: 18e950e209ed2f343c3e259254a55c4376dcd573 SHA256: 6eedd75312afba6e0e66a3fc15661b42cc3027919235957b8079910f6fafa540 SHA512: 6c462268bc14fa912cd6fbbfc77f443b9a0c4838616d86104fbeeeb788f69204da7b95724f4eef4ca792cf04e2811859f33ffe9c9cf9ba1f358e0420bd61a2c4 Homepage: https://cran.r-project.org/package=funMoDisco Description: CRAN Package 'funMoDisco' (Motif Discovery in Functional Data) Efficiently implementing two complementary methodologies for discovering motifs in functional data: ProbKMA and FunBIalign. Cremona and Chiaromonte (2023) "Probabilistic K-means with Local Alignment for Clustering and Motif Discovery in Functional Data" is a probabilistic K-means algorithm that leverages local alignment and fuzzy clustering to identify recurring patterns (candidate functional motifs) across and within curves, allowing different portions of the same curve to belong to different clusters. It includes a family of distances and a normalization to discover various motif types and learns motif lengths in a data-driven manner. It can also be used for local clustering of misaligned data. Di Iorio, Cremona, and Chiaromonte (2023) "funBIalign: A Hierarchical Algorithm for Functional Motif Discovery Based on Mean Squared Residue Scores" applies hierarchical agglomerative clustering with a functional generalization of the Mean Squared Residue Score to identify motifs of a specified length in curves. This deterministic method includes a small set of user-tunable parameters. Both algorithms are suitable for single curves or sets of curves. The package also includes a flexible function to simulate functional data with embedded motifs, allowing users to generate benchmark datasets for validating and comparing motif discovery methods. Package: r-cran-fuser Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 162628 MD5sum: 6b55f3a2de0173734b88abebef28c827 SHA1: 2833af16efac1afc8a11652e8341eaac1a11441f SHA256: 2cb4cbcba5de1d3fc996c6bcc78a4a951860491eadfe119568891ac0eeeb59a9 SHA512: 9a9397fbb2cd56be797a493af15124ba427fba0735df8c8a342e0f664e6fa936e1f4c16d45da4dd657d02d0447dbb508774c02aa4c8ecbf256a5efed874e6c01 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: arm64 Version: 0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 701 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-fuzzyranktests_0.5-1.ca2604.1_arm64.deb Size: 520278 MD5sum: d2c80bed08598f01b2fa7e8350feb6eb SHA1: 859273f9e51fc0a208b0abaa3f5c6f37b0b4d903 SHA256: ab431c8e89f2c2d4a9af1f611a5bbf3d2bd89cc33a1c99dbfa8879de4f7776ba SHA512: 16212d0440a777fc35b268b86fc0878b64b9bfa67c38555aac9ef521266fa83f3c5bae2266de0fc2377d3ad29d592b92f46d0c1d913fe92dbda8aa18cac077c2 Homepage: https://cran.r-project.org/package=fuzzyRankTests Description: CRAN Package 'fuzzyRankTests' (Fuzzy Rank Tests and Confidence Intervals) Does fuzzy tests and confidence intervals (following Geyer and Meeden, Statistical Science, 2005, ) for sign test and Wilcoxon signed rank and rank sum tests. Package: r-cran-fuzzysimres Architecture: arm64 Version: 0.4.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 555 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fuzzynumbers, r-cran-palasso Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-fuzzysimres_0.4.8-1.ca2604.1_arm64.deb Size: 434966 MD5sum: fa072b98fee80f88f0cf10fbe19c6925 SHA1: 983981c900362e87393704b19662e1a7028edfa9 SHA256: 411488eb86bc7591d0b8b7cc1e9fe06c2ba34ba4e027ff4be106d1e2192cd31f SHA512: d8cd1505517d47467f6d78398044deda9acf61f343491a4ebc92a002fb64bb987e651da8cf975f91d39566e9bd64190c8fce99628f7f00d490924e37972b8566 Homepage: https://cran.r-project.org/package=FuzzySimRes Description: CRAN Package 'FuzzySimRes' (Simulation and Resampling Methods for Epistemic Fuzzy Data) Random simulations of fuzzy numbers are still a challenging problem. The aim of this package is to provide the respective procedures to simulate fuzzy random variables, especially in the case of the piecewise linear fuzzy numbers (PLFNs, see Coroianua et al. (2013) for the further details). Additionally, the special resampling algorithms known as the epistemic bootstrap are provided (see Grzegorzewski and Romaniuk (2022) , Grzegorzewski and Romaniuk (2022) , Romaniuk et al. (2024) ) together with the functions to apply statistical tests and estimate various characteristics based on the epistemic bootstrap. The package also includes real-life datasets of epistemic fuzzy triangular and trapezoidal numbers. The fuzzy numbers used in this package are consistent with the 'FuzzyNumbers' package. Package: r-cran-fuzzystring Architecture: arm64 Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 640 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 344722 MD5sum: 1049de0780deca296c70bf17f987399c SHA1: 11900b92d8dac6b3ee7929c613ad9d24793bfcb5 SHA256: 5c3b63590fcdc15a9a0ecee7ffb241b8d30746fb4d80e5a3805cfc8d43ec6ee6 SHA512: 97a67bd6dec88071231713eaaa1a53f15505f25c1b14c9eb300e6b20ccdbbf536e5c7546f9b46dd95c2692ce2333d5cb88d372384b18587a8263d4f210108dcf 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: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 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_arm64.deb Size: 164396 MD5sum: 7122f0df7282a111656f04fa5499285b SHA1: 7626ea9256b6fa0f9d0ee8dacf0f17367e2b2ee9 SHA256: e2df92baeae1a682a647707ae0b0a03b9480db6361ce62716a4e6e4de0c3e507 SHA512: 4040532213d171c65c7c0e3ac357717fd57106109109f4328e4cb5c8e23f812ed3c44f5a9c22c6b5a157094e66d2bb5d938bb81fa24d9927b9204daac7d30453 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: arm64 Version: 3.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 43894 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_arm64.deb Size: 2457660 MD5sum: 91cb88a4ee8cfe03941091c3f216863e SHA1: fa71410adbcfd38c1612437b4f73e6a075a4df6e SHA256: 1c5a5a230e86fa28a29878c69137cf5e661bc1c1fa7368c7ea1b384676ce5fa5 SHA512: dfe8e9c16d55c2bf531cccfaf1055271cb31b842693ef675be1ff0f8896e8cc4b06685d09851197fede33da913592b616d7f36ac8b933e50549063561266dda1 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: arm64 Version: 0.99.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 116854 MD5sum: aac7acdc4b0855e2082e13415ce3c8b7 SHA1: 7bf84118c22e2b4a00e5705bacba5e0bc8b4f3c3 SHA256: d2792d94f8630c6dbb7ea51d29b0207d4c170ae20d68d407df59c067fabc5c73 SHA512: cef60145f8847db2bc8e98d1ffe3cda05385ad09dd44b092873f5586d5f89455b731be1dfb1c0670f2adce0576f5d69d73554b3da85fe4752962cb322b552e72 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: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 711 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gadjid_0.1.0-1.ca2604.1_arm64.deb Size: 284904 MD5sum: a2f0e9bc9a3f26b0f332d29c919a442f SHA1: bea76b29cae41dd11329678be661da57ab7eb81e SHA256: 19a987d6c3f74b6fc63b08f2116542d903e5918d8ef5f0d60c84f278ebbf085a SHA512: 074206de93415dfd6c4f7204c352e0235dcd873ef1df61dae928c00afee2ec198baa44f77c888d7cfbc9625ec759c5bb19f4fdcab243805fe2251b5561023bbe 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: arm64 Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 702 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_arm64.deb Size: 272208 MD5sum: 13ff0e045553e6aa8395f401b7efb3c8 SHA1: 03e28753c9dd48b1ffefa3558a880e908dd684f0 SHA256: 44c06fbeb19f53a0d838dbf4e7faca356ac65c4a67e157ba3bfbd35ff7ef6e36 SHA512: b94f5d4a6edd6a0dd9f65fcd309b8a6c57952aa32a37a2e60e61c6f1c245831289243bf01d42f77668e1a2e79c0aea45e020677ef4c5ebe4d021e8ad9745b6ac Homepage: https://cran.r-project.org/package=GAGAs Description: CRAN Package 'GAGAs' (Global Adaptive Generative Adjustment Algorithm for GeneralizedLinear Models) Fits linear regression, logistic and multinomial regression models, Poisson regression, Cox model via Global Adaptive Generative Adjustment Algorithm. For more detailed information, see Bin Wang, Xiaofei Wang and Jianhua Guo (2022) . This paper provides the theoretical properties of Gaga linear model when the load matrix is orthogonal. Further study is going on for the nonorthogonal cases and generalized linear models. These works are in part supported by the National Natural Foundation of China (No.12171076). Package: r-cran-galamm Architecture: arm64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5009 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_arm64.deb Size: 2986244 MD5sum: 583aee13a39c1db0e838a2345838d455 SHA1: 934c4e11891595bb07b34f85b3779f7bf6ab8a6d SHA256: cd6d49ae03d152f830ec8c228aeec28dd3f3d6e395dc9a114356b6177506b3d5 SHA512: 467654c0b733e0498d4e09b31963fdc904eba6020706175f3ef9a3614b9b722ea1586c1c929d1581f34dd691b8a06ab0bb719b72b5e6342969f6c8102a4cb00d 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: arm64 Version: 1.22-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 464 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_arm64.deb Size: 309536 MD5sum: 0afd61edf5389a3f2ce99b3105e6010e SHA1: 7d41135ab915109226c8f333d401ca8679ead2fb SHA256: dc30f9854dd73e447abc522ad76cdbe7b312a1f9fac09dea7e5a38c3afe8fad1 SHA512: 2e906637d538bd03d9da56d543e8350e1641df6de879373c78d5dc1c430572ba4b0c00a60136aaad54cd726d844b7fa5d807693a7dfe0ec03b9e67d55345b81a Homepage: https://cran.r-project.org/package=gam Description: CRAN Package 'gam' (Generalized Additive Models) Functions for fitting and working with generalized additive models, as described in chapter 7 of "Statistical Models in S" (Chambers and Hastie (eds), 1991), and "Generalized Additive Models" (Hastie and Tibshirani, 1990). Package: r-cran-gamesga Architecture: arm64 Version: 1.1.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 48088 MD5sum: 557c883ce1151e46a8db94f38d04efb3 SHA1: 4f3eaec4ef0976c273900f993ad803fe8dd0756f SHA256: 519fa2104e7348cca12c8fec136f22ddf02e1da019fbfdfb83f643c637581a99 SHA512: a48007f0b44bae81d67535753876d7a69130bb1b0abf6f2772ff92aacaea9ebbc3b55eac2946fa23317c3179c923ef89f194f68fcc41fe3cb49116a25703c606 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: arm64 Version: 1.13-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1189 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1122958 MD5sum: 41b1580f9911a9c642d51131396c5991 SHA1: 54203cad2ae06231fc7b5af679e48c97d196f001 SHA256: 5f5b242adbecc51a01abaf7877bc7a0e3c4c2523e5884b47b3a714fa35f281c3 SHA512: 46fc9dcf681e06bb3f3d4e586552a6a50ecea272cb521251e5abb06e518d7213ba15ce47763560f2838d15d21789e70851295cce8689481dcc972019fec1a85d 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: arm64 Version: 6.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3622 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 3441300 MD5sum: 9eb5a4b25b40dc691ac0f6a30bc93a0a SHA1: 23fb54a4568f28205651b242c569b28214e7ba9d SHA256: 0eacea69ba574fce9b4b21b4bde7a4c71012a4b49e04672efa781fe20e1f009b SHA512: 5bc2eb7e40bef09cb491e183d6c8d6c393463cef391f969ea92b1df0fb0e7adae5fdf471cc5a0e1e1960409338984d8b395a6cdb68722971db2da897ef39bbbf Homepage: https://cran.r-project.org/package=gamlss.dist Description: CRAN Package 'gamlss.dist' (Distributions for Generalized Additive Models for Location Scaleand Shape) A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), . 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Package: r-cran-gamlss Architecture: arm64 Version: 5.5-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1522 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-gamlss.data, r-cran-gamlss.dist, r-cran-nlme, r-cran-mass, r-cran-survival Suggests: r-cran-distributions3 Filename: pool/dists/resolute/main/r-cran-gamlss_5.5-0-1.ca2604.1_arm64.deb Size: 1407870 MD5sum: 61dda7aa80921996a40e12605389e0ce SHA1: 994524c87b4cad971e54fd49028d5729ae145b30 SHA256: 069c4be6ef0b2e6f0f7f308e5e0bce875b57ce99499fdcf69c2e795739d871c0 SHA512: b9ea78538f66647b6389a48fbe6a9c8531e5fdaba620e553aa9e5e9dd302224774c755bbbe88e158f7f177f44d630e4eb2527644841b7a469d4c9a05d27b3f1f Homepage: https://cran.r-project.org/package=gamlss Description: CRAN Package 'gamlss' (Generalized Additive Models for Location Scale and Shape) Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), . 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Package: r-cran-gammslice Architecture: arm64 Version: 2.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 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_arm64.deb Size: 101598 MD5sum: 645787b15d63ef53058ad2ba6d37c28e SHA1: 6ce4738ce6833bb382f2a450503a79ebd6689980 SHA256: 50edc82e90fa184bb15d0b1170c4acb3e021ed8d253a82324c99c6d2ef847bfd SHA512: 0f003683e65e7401ae9e6f540bde56d3864f6e95bd296ce8688567aca6a4ee95781b66170af0aef57398f955a34a3ed7611ca7f5b444f4f4c2eb304104b4c947 Homepage: https://cran.r-project.org/package=gammSlice Description: CRAN Package 'gammSlice' (Generalized Additive Mixed Model Analysis via Slice Sampling) Uses a slice sampling-based Markov chain Monte Carlo to conduct Bayesian fitting and inference for generalized additive mixed models. Generalized linear mixed models and generalized additive models are also handled as special cases of generalized additive mixed models. The methodology and software is described in Pham, T.H. and Wand, M.P. (2018). Australian and New Zealand Journal of Statistics, 60, 279-330 . Package: r-cran-gamreg Architecture: arm64 Version: 0.3-1.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-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_arm64.deb Size: 97154 MD5sum: f29dfa103b57a63c0345d116aec3433d SHA1: 4d5e3f23e9f994f94cb2fb29dc27d61a918b77d3 SHA256: 84b8a2720f52db715ada874a29470ff79930ecf7028c70835878bc508386951f SHA512: 86febe0d0ebac64217d76b503b24750286c4bc534ba6fa919dc1de68ed0827401953a60556aaf8ff4557cfa6cb130a59b7820514704a9b2010c20735736ddb35 Homepage: https://cran.r-project.org/package=gamreg Description: CRAN Package 'gamreg' (Robust and Sparse Regression via Gamma-Divergence) Robust regression via gamma-divergence with L1, elastic net and ridge. Package: r-cran-gamsel Architecture: arm64 Version: 1.8-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 858 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_arm64.deb Size: 355562 MD5sum: cbc832ff40fefad3ce2b7ced05f2d055 SHA1: 6639b02042083d8901e4de9ec56f9a7e3884b284 SHA256: 4c00279db188f80fd9cd687a343708ca8592d4efc5c95acb8add84a7a3349fc1 SHA512: 689720c2f26ff69866ae63ae9ba6851d0fb6a2616284e1107afecd77dcc635b09b6d0a52af39ab349f41099964264926250355d8ff2b48fa3ae24191086b40bd Homepage: https://cran.r-project.org/package=gamsel Description: CRAN Package 'gamsel' (Fit Regularization Path for Generalized Additive Models) Using overlap grouped-lasso penalties, 'gamsel' selects whether a term in a 'gam' is nonzero, linear, or a non-linear spline (up to a specified max df per variable). It fits the entire regularization path on a grid of values for the overall penalty lambda, both for gaussian and binomial families. See for more details. Package: r-cran-gamselbayes Architecture: arm64 Version: 2.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1269 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 908108 MD5sum: 51a21270053a41fa6bf746ea0b137aeb SHA1: 5168dad46f824dc8f57dcf34fbdc974b8062e543 SHA256: 3dd80640ff5fd41bfead56b4b73388d915421db578fec1b9c4e0fc05f1d37ab6 SHA512: e4337cee216013d85c5fbaa3dc98dc66c1c70b18013018904d75458c8496230de31f900eeb9d4a8dce2026c9c1d214fcddf6a673e5218614d1d9a0fde72f58a1 Homepage: https://cran.r-project.org/package=gamselBayes Description: CRAN Package 'gamselBayes' (Bayesian Generalized Additive Model Selection) Generalized additive model selection via approximate Bayesian inference is provided. Bayesian mixed model-based penalized splines with spike-and-slab-type coefficient prior distributions are used to facilitate fitting and selection. The approximate Bayesian inference engine options are: (1) Markov chain Monte Carlo and (2) mean field variational Bayes. Markov chain Monte Carlo has better Bayesian inferential accuracy, but requires a longer run-time. Mean field variational Bayes is faster, but less accurate. The methodology is described in He and Wand (2024) . Package: r-cran-gamstransfer Architecture: arm64 Version: 3.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1290 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_arm64.deb Size: 768340 MD5sum: 0f8bea38a539f376e89ebe838971bb5a SHA1: b1850cff2273a503e23b1d798b6743aa2f035698 SHA256: cc7f78e61786b35cb192da4b0d3add30d623b456fca8a6ed4b3576e476fb71c8 SHA512: b1a184c98b23bb662c3c6018b312e38378763bdea1b1769c8cec3ed23c4f2abfd21db0381ce56e2946896939b88639fa7df18d07b39a708cc886dffa8d94ae9f Homepage: https://cran.r-project.org/package=gamstransfer Description: CRAN Package 'gamstransfer' (A Data Interface Between 'GAMS' and R) Read, analyze, modify, and write 'GAMS' (General Algebraic Modeling System) data. The main focus of 'gamstransfer' is the highly efficient transfer of data with 'GAMS' , while keeping these operations as simple as possible for the user. The transfer of data usually takes place via an intermediate GDX (GAMS Data Exchange) file. Additionally, 'gamstransfer' provides utility functions to get an overview of 'GAMS' data and to check its validity. Package: r-cran-gandatamodel Architecture: arm64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1033 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_arm64.deb Size: 690790 MD5sum: c320abd943f4b749243d22c64c4eefa3 SHA1: 4179d56d990b166752a92fc609d3be0d29ed5b4c SHA256: cefdb08b1f0644a0c9fb546072d101f8afae27286ee7207329e5507b69a64000 SHA512: b37c008fb0c7f2287ed002490c4fc6772a2ac0c12ed0f861aae1c134ba9e320418bd931cb04a917df2e16d7249cd3bc5ea3e5c1a1a3b8d6d696142b805e804c7 Homepage: https://cran.r-project.org/package=ganDataModel Description: CRAN Package 'ganDataModel' (Build a Metric Subspaces Data Model for a Data Source) Neural networks are applied to create a density value function which approximates density values for a data source. The trained neural network is analyzed for different levels. For each level metric subspaces with density values above a level are determined. The obtained set of metric subspaces and the trained neural network are assembled into a data model. A prerequisite is the definition of a data source, the generation of generative data and the calculation of density values. These tasks are executed using package 'ganGenerativeData' . Package: r-cran-gangenerativedata Architecture: arm64 Version: 2.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1317 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_arm64.deb Size: 1004728 MD5sum: 57bf4e06390d6e5b2ed43b8f2a48dfd4 SHA1: 580df1016cfcefc108a2163402ee8f7774d9cb62 SHA256: 05768d6cf8861793fa1e344d512c0e3a433f3a0ca4c50fef1ab497de9da18dcf SHA512: 94352d6294623b0d7583a0742574b123c34058ec112fb3ebf49f397165cd42105487156001b07b1cec93b292032f5d46ac95ce68987090a7782570ea225506ca Homepage: https://cran.r-project.org/package=ganGenerativeData Description: CRAN Package 'ganGenerativeData' (Generate Generative Data for a Data Source) Generative Adversarial Networks are applied to generate generative data for a data source. A generative model consisting of a generator and a discriminator network is trained. During iterative training the distribution of generated data is converging to that of the data source. Direct applications of generative data are the created functions for data evaluation, missing data completion and data classification. A software service for accelerated training of generative models on graphics processing units is available. Reference: Goodfellow et al. (2014) . Package: r-cran-gap Architecture: arm64 Version: 1.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2066 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_arm64.deb Size: 1152734 MD5sum: 5e13c17ed6eb21bbd2bae6f1a0d9a58c SHA1: 687803dc533fe8e4eed991973dd9fd26f8168d22 SHA256: 3ec444a9d1e583e49c2fdce09194bb0645adba906214553ed495b96ae785ffaf SHA512: 3daadb4dded37d86af383dda72414ccc3331227eb56a4a88fe8e95b5237d0f55c2562ceaa766018867d1ab734cfb2ffb8f71da65816f64eba43c26d33fb0f476 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: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 160280 MD5sum: 252a6fc2efb5ab16e535ebdeac128c5a SHA1: 24a2e22db6afb70a9b7f6c78e23057f968933d7e SHA256: 60ac8f56552051e84c180a3ca3c7afecca569fad55036fc2093f15dfa8f83165 SHA512: 98029b116f16b8f6018788fe58d80a94dd5cb9bffbd188eaa7b9f8d71949f8ee924471db68b949e043519bf812a9ea1606639f05d10d754e0906c050c3e917eb Homepage: https://cran.r-project.org/package=GAPR Description: CRAN Package 'GAPR' (Generalized Association Plots) Provides a comprehensive framework for visualizing associations and interaction structures in matrix-formatted data using Generalized Association Plots (GAP). The package implements multiple proximity computation methods (e.g., correlation, distance metrics), ordering techniques including hierarchical clustering (HCT) and Rank-2-Ellipse (R2E) seriation, and optional flipping strategies to enhance visual symmetry. It supports a variety of covariate-based color annotations, allows flexible customization of layout and output, and is suitable for analyzing multivariate data across domains such as social sciences, genomics, and medical research. The method is based on Generalized Association Plots introduced by Chen (2002) and further extended by Wu, Tien, and Chen (2010) . Package: r-cran-garchx Architecture: arm64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo Suggests: r-cran-tvgarch, r-cran-lgarch Filename: pool/dists/resolute/main/r-cran-garchx_1.6-1.ca2604.1_arm64.deb Size: 121926 MD5sum: 8301bf55e6f41c7244167a1a65bca7fa SHA1: 7e1d220cac20fa3d7c2af98e9fe1834e84480677 SHA256: 3fcfd5069d3eb4695ceafa996fb47dd4891a52031d99f08e235cae8cae94f92c SHA512: 7fa3163e8f0e34d03c0f96c52e086bd9666d18dbc787a1cdef4705383ab2721e9af4fb56b90732527a207b22a8a084ef6a0aaa0137069ce812f79ec455cc4bcf 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: arm64 Version: 1.0.25-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 479 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 221304 MD5sum: ba03634071de450bbe6f8448cebb7878 SHA1: 87828e10b3656871d05f5cf4ca1a378f1d00eb71 SHA256: b542ebc8ff141a0f2be6417bc1cffd4543171e851ead4bb7d59bfc855e7def36 SHA512: a264245c05e95f72434e9af43da0fc4b0bc54380585a3397f19ad90a9d3f309d8a2d01e2609ab9e18a16c1ea33ce72578e5de3cb47cb473ef85b723c1b9dc722 Homepage: https://cran.r-project.org/package=gaselect Description: CRAN Package 'gaselect' (Genetic Algorithm (GA) for Variable Selection fromHigh-Dimensional Data) Provides a genetic algorithm for finding variable subsets in high dimensional data with high prediction performance. The genetic algorithm can use ordinary least squares (OLS) regression models or partial least squares (PLS) regression models to evaluate the prediction power of variable subsets. By supporting different cross-validation schemes, the user can fine-tune the tradeoff between speed and quality of the solution. Package: r-cran-gasp Architecture: arm64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1084 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_arm64.deb Size: 869376 MD5sum: 3162be80a1ebfc5b7a02ba9b60865368 SHA1: 1086b84f3ff78d83509db3fd97a384ed03e8e776 SHA256: 31b8e27a1c6baf10e73cc9b9a333d731b324cb70132dfdaf219f8d28d106ec75 SHA512: 0c3ab7a08a9a4562b1bc23d8dff72e094b5666222c446780b319f86cb599f9e9a3143a5079985d832fa7ca5acbbf03992ab0918505865f09aaedbfb8c07ebe0e 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", . 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It also implements a data driven method for graph signal denoising/regression, for details see De Loynes, Navarro, Olivier (2019) . The package also provides an interface to the SuiteSparse Matrix Collection, , a large and widely used set of sparse matrix benchmarks collected from a wide range of applications. Package: r-cran-gastempt Architecture: arm64 Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3886 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_arm64.deb Size: 1056640 MD5sum: 30a5bb5d4f5b09d7a5a72c579ab87063 SHA1: 479391f5076b6f7c176378d932764ec0c447b28e SHA256: 37d5f89795b941e38e15e26c11309f7d9b081ba5d10456cae34ad7b4ecd613ed SHA512: 57e93d3181d13132eb961eb25d3145f58a1f510f2b9b4430d4e2b70185633847c665ea913d3c7cfc20cc558af092ec8ab05f1717dd21d87da3bc98815c6fb2ad Homepage: https://cran.r-project.org/package=gastempt Description: CRAN Package 'gastempt' (Analyzing Gastric Emptying from MRI or Scintigraphy) Fits gastric emptying time series from MRI or 'scintigraphic' measurements using nonlinear mixed-model population fits with 'nlme' and Bayesian methods with Stan; computes derived parameters such as t50 and AUC. Package: r-cran-gaston Architecture: arm64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5404 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_arm64.deb Size: 3012446 MD5sum: 638c8011f326b10ca15ebc27fe37cc4e SHA1: 26df0e57cc439015e747bdb9702472deed7b18d3 SHA256: c81756e84d392e0b41bcf2a8c2a353b4a514e94be2a6f5c16f8e012c0158c035 SHA512: 581bcae928d5269f253c54e1532b0244beebce3bc4f5456bb97bb455573cd0153349f4370a0fdb33b4930c8dc910a0aa211ffbaff79f9a07a314c3477b692b84 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 . 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Gaussian processes are commonly used in computer experiments to fit an interpolating model. The model is stored as an 'R6' object and can be easily updated with new data. There are options to run in parallel, and 'Rcpp' has been used to speed up calculations. For more info about Gaussian process software, see Erickson et al. (2018) . Package: r-cran-gausscov Architecture: arm64 Version: 1.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3519 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_arm64.deb Size: 3448116 MD5sum: fb9a6f8033cd6f345730a8cf6d1d26f9 SHA1: 86b16902095cb2df99ab050fd6e3b2c6c8a0c0c6 SHA256: fc88aa62cfb2774911000c90a875d8dd26bf86af19b8232f26294800e1465323 SHA512: 3c72e5c8acf6749fd37fc920e7ed6663d370c66117397d012e556bd73ec5621920680e6b68f677f0b0413d2fd1bdb3c18ce0836f7071b0256de1e57212b69d42 Homepage: https://cran.r-project.org/package=gausscov Description: CRAN Package 'gausscov' (The Gaussian Covariate Method for Variable Selection) The standard linear regression theory whether frequentist or Bayesian is based on an 'assumed (revealed?) truth' (John Tukey) attitude to models. This is reflected in the language of statistical inference which involves a concept of truth, for example confidence intervals, hypothesis testing and consistency. The motivation behind this package was to remove the word true from the theory and practice of linear regression and to replace it by approximation. The approximations considered are the least squares approximations. An approximation is called valid if it contains no irrelevant covariates. This is operationalized using the concept of a Gaussian P-value which is the probability that pure Gaussian noise is better in term of least squares than the covariate. The precise definition given in the paper "An Approximation Based Theory of Linear Regression". Only four simple equations are required. Moreover the Gaussian P-values can be simply derived from standard F P-values. Furthermore they are exact and valid whatever the data in contrast F P-values are only valid for specially designed simulations. A valid approximation is one where all the Gaussian P-values are less than a threshold p0 specified by the statistician, in this package with the default value 0.01. This approximations approach is not only much simpler it is overwhelmingly better than the standard model based approach. The will be demonstrated using high dimensional regression and vector autoregression real data sets. The goal is to find valid approximations. The search function is f1st which is a greedy forward selection procedure which results in either just one or no approximations which may however not be valid. If the size is less than than a threshold with default value 21 then an all subset procedure is called which returns the best valid subset. A good default start is f1st(y,x,kmn=15) The best function for returning multiple approximations is f3st which repeatedly calls f1st. For more information see the papers: L. Davies and L. Duembgen, "Covariate Selection Based on a Model-free Approach to Linear Regression with Exact Probabilities", , L. Davies, "An Approximation Based Theory of Linear Regression", 2024, . 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Package: r-cran-gbeta Architecture: arm64 Version: 0.1.0-1.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, 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_arm64.deb Size: 80634 MD5sum: 1a3994b957d98ddea74e2e28c977f3e9 SHA1: 970734ffb4ba5b4cd8cd226d837f6656c6dacfa8 SHA256: 5d221c66822e31573412068f9c5b558e612d018a3d1ea0edb12e3c63d8868884 SHA512: 9100850100dae474954b028a638ffaaf09c624da35e1cf01f4601adb658307c90c684952a5e112e7f31ff2df066ea3cada649c5dbf3dbb73bb4f94475a2eae07 Homepage: https://cran.r-project.org/package=gbeta Description: CRAN Package 'gbeta' (Generalized Beta and Beta Prime Distributions) Density, distribution function, quantile function, and random generation for the generalized Beta and Beta prime distributions. The family of generalized Beta distributions is conjugate for the Bayesian binomial model, and the generalized Beta prime distribution is the posterior distribution of the relative risk in the Bayesian 'two Poisson samples' model when a Gamma prior is assigned to the Poisson rate of the reference group and a Beta prime prior is assigned to the relative risk. References: Laurent (2012) , Hamza & Vallois (2016) , Chen & Novick (1984) . Package: r-cran-gbj Architecture: arm64 Version: 0.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 304 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_arm64.deb Size: 140438 MD5sum: fc96a86ca6557b7d3c29a2467a3ad601 SHA1: 3cc98db6c950f80f8ab2bb28dceb1dca014ee6a3 SHA256: e78b1c3485e31dd3c0d92806e217d4b90ba929ade366cddc3049816c512e4883 SHA512: e7695d858aed2752b1a402bb300bb34a267dc115e51b675b365306cc54ff80c0bc5bb7560e587bc4165a5f909b3ed9b977f731f7acb6c727abf165726bdd3c9d Homepage: https://cran.r-project.org/package=GBJ Description: CRAN Package 'GBJ' (Generalized Berk-Jones Test for Set-Based Inference in GeneticAssociation Studies) Offers the Generalized Berk-Jones (GBJ) test for set-based inference in genetic association studies. The GBJ is designed as an alternative to tests such as Berk-Jones (BJ), Higher Criticism (HC), Generalized Higher Criticism (GHC), Minimum p-value (minP), and Sequence Kernel Association Test (SKAT). All of these other methods (except for SKAT) are also implemented in this package, and we additionally provide an omnibus test (OMNI) which integrates information from each of the tests. The GBJ has been shown to outperform other tests in genetic association studies when signals are correlated and moderately sparse. Please see the vignette for a quickstart guide or Sun and Lin (2017) for more details. 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Package: r-cran-gbm Architecture: arm64 Version: 2.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 761 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_arm64.deb Size: 570480 MD5sum: e9a745a80c033ede38f868dc1ac08116 SHA1: 29a7fa3813b1bd45bb020938907d95536d742a97 SHA256: 441d3fa5cf2630d7e935bea2878cf92d2965e1f84775d9c5bb79873c19334e99 SHA512: 71ae1b3f118d12e88e7fc6315db247b3160c77a338a45a95efc21ebb687d766f8875ee74c8864b4ed4e83b1d735a2c93128e00955394263e72de44db635d78f3 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: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2034 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_arm64.deb Size: 835128 MD5sum: 8a2b7870deb14049f0f9d73f7ed9389a SHA1: 71de5a2aabc2f22b70bad4f2895c02aec539e74f SHA256: 0182e313c331b76ce6be1c507904619babc6c55d09e0382faea218c079072a27 SHA512: 886a4d9fc0796d4939367f2949d8c8ba94cd11873cd2297d32d4fe5b65edbbe2ead0efe7734a793937a4ea2825a1149db95e5f083014a7fd4eaa3c6966da6587 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: arm64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gcat_0.2-1.ca2604.1_arm64.deb Size: 76324 MD5sum: d3fb6ac4c08097d3ee68c93406bb518e SHA1: 148b81dfffed4ebbb7ad3bcf03f0f83560572ffe SHA256: c85d6ebce0a33cf3419d33cb61c4717d36618eeaeb689530878b909e79c673ea SHA512: 7a2c4053d81dfa37c01ce0514547ce383cc6b6bc7b4dd5f33e8ea8aeb19cc6ec14484791678bb42fde5842df66caeac984ae83054436b8a98a55775a8de567ed Homepage: https://cran.r-project.org/package=gCat Description: CRAN Package 'gCat' (Graph-Based Two-Sample Tests for Categorical Data) These are two-sample tests for categorical data utilizing similarity information among the categories. They are useful when there is underlying structure on the categories. Package: r-cran-gcdnet Architecture: arm64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 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_arm64.deb Size: 173326 MD5sum: b483f6630354a62ff78d0c6ae1a5e5b4 SHA1: cef89580f4a6c36857ef90f1e4b36fc384eb69ec SHA256: b14d77befd17e0d25f6c8aa5ac9a0e9ef7be43c737a932e8a6c02b6bddd8e797 SHA512: 531d804c11fc123ab95e4425d30275b0ad37adc6b476f7c2ddec0c6d2ce8bd335ae41d717eca2030ebc2c9065b201f0c8a80bf7c1fa1b660641c5369b59d232a Homepage: https://cran.r-project.org/package=gcdnet Description: CRAN Package 'gcdnet' (The (Adaptive) LASSO and Elastic Net Penalized Least Squares,Logistic Regression, Hybrid Huberized Support Vector Machines,Squared Hinge Loss Support Vector Machines and ExpectileRegression using a Fast Generalized Coordinate DescentAlgorithm) Implements a generalized coordinate descent (GCD) algorithm for computing the solution paths of the hybrid Huberized support vector machine (HHSVM) and its generalizations. Supported models include the (adaptive) LASSO and elastic net penalized least squares, logistic regression, HHSVM, squared hinge loss SVM and expectile regression. Package: r-cran-gckrig Architecture: arm64 Version: 1.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 509 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-eql, r-cran-fnn, r-cran-lattice, r-cran-latticeextra, r-cran-mvtnorm, r-cran-matrix, r-cran-mass, r-cran-numderiv, r-cran-scatterplot3d, r-cran-snowfall, r-cran-sp Filename: pool/dists/resolute/main/r-cran-gckrig_1.1.8-1.ca2604.1_arm64.deb Size: 329414 MD5sum: fdf01df2aa6c72bfee460abc2287fe31 SHA1: bcad9c54788947e3505dc5eb90b956be37db8a00 SHA256: 55e1830d2b19a1998d713f78e87eaa263aec885e6ac0be75c18d54952586a200 SHA512: 85b5d389d4c169a386c0eca6ec098e7d5d198af1cefe793b18f29ee82ece9f9fce79dddbd4291d8348757cbf97647710f26751b49904293a95a98788d41e892f Homepage: https://cran.r-project.org/package=gcKrig Description: CRAN Package 'gcKrig' (Analysis of Geostatistical Count Data using Gaussian Copulas) Provides a variety of functions to analyze and model geostatistical count data with Gaussian copulas, including 1) data simulation and visualization; 2) correlation structure assessment (here also known as the Normal To Anything); 3) calculate multivariate normal rectangle probabilities; 4) likelihood inference and parallel prediction at predictive locations. Description of the method is available from: Han and DeOliveira (2018) . Package: r-cran-gclm Architecture: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gclm_0.0.1-1.ca2604.1_arm64.deb Size: 37242 MD5sum: ce384128818671635aef8283c4fa1fb8 SHA1: a9c5aa97ae633d3271446bef066dc4d64f338365 SHA256: 5b56da007f47eaa4d17a3b7b92c87f97f4d26c5c01c3b66d0d08c4342b152ab9 SHA512: c584f3f8e43d7dffc0ad42508d7281689c7b921e7110cc7baea1b6b57a1621ff4606fdc6cb10b214ebb88df300d535aa0274d94c3a3964fad4f74d768f1d61ec Homepage: https://cran.r-project.org/package=gclm Description: CRAN Package 'gclm' (Graphical Continuous Lyapunov Models) Estimation of covariance matrices as solutions of continuous time Lyapunov equations. Sparse coefficient matrix and diagonal noise are estimated with a proximal gradient method for an l1-penalized loss minimization problem. Varando G, Hansen NR (2020) . Package: r-cran-gcmr Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 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_arm64.deb Size: 166590 MD5sum: 5f4438aed31285a047ceefa844af6ca7 SHA1: 5e7890ddf016287336e0ff82e62b1779aa7e5e4c SHA256: 96a18c2e4a2b10bb828d3403b163bd332844e9b42870559634f8b6165bff4c02 SHA512: 7cd3cc4bc3033406fd7a05e12f1f541e08cbdb8dcad39670372b5a9a13ff329eee5073f39ae258422711cd813f1ea8d28a49f1e95b993c658ec3cfcd46f4aa2f Homepage: https://cran.r-project.org/package=gcmr Description: CRAN Package 'gcmr' (Gaussian Copula Marginal Regression) Likelihood inference in Gaussian copula marginal regression models. Package: r-cran-gcpbayes Architecture: arm64 Version: 4.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 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_arm64.deb Size: 337814 MD5sum: 05cea93e067c8a8d3dc3eec155627718 SHA1: 26c1539b17163bdbd39bbc48b574bb9702e7ef15 SHA256: f6acaefb7055be60240a2439be2e39ca6d8a62e88b58283b111effd4ea2ea429 SHA512: a7566a08ae5a5c2a2a6140cbdad58d5e0e6885acb2ee5a77578185a6a21a286f616b7b9b557a7aeba53c184d1ae2b79f6d17c926b6ab319c94886901265a5b86 Homepage: https://cran.r-project.org/package=GCPBayes Description: CRAN Package 'GCPBayes' (Bayesian Meta-Analysis of Pleiotropic Effects Using GroupStructure) Run a Gibbs sampler for a multivariate Bayesian sparse group selection model with Dirac, continuous and hierarchical spike prior for detecting pleiotropy on the traits. This package is designed for summary statistics containing estimated regression coefficients and its estimated covariance matrix. The methodology is available from: Baghfalaki, T., Sugier, P. E., Truong, T., Pettitt, A. N., Mengersen, K., & Liquet, B. (2021) . Package: r-cran-gcpm Architecture: arm64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 973 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 616466 MD5sum: c91d01a47747ab45a53250bc1103e175 SHA1: d0f5ee5d234b0bf80b3996e9307fc81025c09e79 SHA256: 31d6d102b4c45cf4df643b81fc714d7981af126a726d53173a60c4efa213d757 SHA512: 364a9dd0c0ef16c05890c24d4040f3ffec178efc40356f5abc1b96952f4fdf2dc619ad75d47399ce30021488a73fd8e2f3db36864a7fc3dd1afdb1c095cf4894 Homepage: https://cran.r-project.org/package=GCPM Description: CRAN Package 'GCPM' (Generalized Credit Portfolio Model) Analyze the default risk of credit portfolios. Commonly known models, like CreditRisk+ or the CreditMetrics model are implemented in their very basic settings. The portfolio loss distribution can be achieved either by simulation or analytically in case of the classic CreditRisk+ model. Models are only implemented to respect losses caused by defaults, i.e. migration risk is not included. The package structure is kept flexible especially with respect to distributional assumptions in order to quantify the sensitivity of risk figures with respect to several assumptions. Therefore the package can be used to determine the credit risk of a given portfolio as well as to quantify model sensitivities. Package: r-cran-gcsm Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 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_arm64.deb Size: 120986 MD5sum: ad2f97aa0e80b4af5ac6eaeaef16bb9a SHA1: c57ed92fe56d5cb0a5d2b22603001153727fe208 SHA256: fac7db671a69debb68f5de40e3fd683abe151606e4bc0485307bbedb15939b40 SHA512: 895b78c906354084e71d37eefa7bd0db7f60107c4137c663aaf1603974378ece96b4fd6a8b7ee7265b2970358387cb7c21b62b712597b27579acc754a66e5465 Homepage: https://cran.r-project.org/package=GCSM Description: CRAN Package 'GCSM' (Implements Generic Composite Similarity Measure) Provides implementation of the generic composite similarity measure (GCSM) described in Liu et al. (2020) . The implementation is in C++ and uses 'RcppArmadillo'. Additionally, implementations of the structural similarity (SSIM) and the composite similarity measure based on means, standard deviations, and correlation coefficient (CMSC), are included. Package: r-cran-gctsc Architecture: arm64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1199 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_arm64.deb Size: 792994 MD5sum: 864d25dbd7a9f5a940e9727163b48f40 SHA1: 23b474a1324aa8949cfdccdf178012223b0ff078 SHA256: 15a339744cd7e3e2c17ecc7176007f8fc8b28da00a4df34934b842f7f2ca14b1 SHA512: 70ee8d80022bcb9e35fe7fea9c63d90389d83e1216b6b21a96937387ef9c0f964df771807fb593586df962d2a9b71ad0ef8ac60479b3803855dd9584e108cc57 Homepage: https://cran.r-project.org/package=gctsc Description: CRAN Package 'gctsc' (Gaussian and Student-t Copula Models for Count Time Series) Provides likelihood-based inference for Gaussian and Student-t copula models for univariate count time series. Supports Poisson, negative binomial, binomial, beta-binomial, and zero-inflated marginals with ARMA dependence structures. Includes simulation, maximum-likelihood estimation, residual diagnostics, and predictive inference. Implements Time Series Minimax Exponential Tilting (TMET) , an adaptation of minimax exponential tilting of Botev (2017) . Also provides a linear-cost implementation of the Geweke–Hajivassiliou–Keane (GHK) simulator following Masarotto and Varin (2012) , and the Continuous Extension (CE) approximation of Nguyen and De Oliveira (2025) . The package follows the S3 design philosophy of 'gcmr' but is developed independently. Package: r-cran-gdalcubes Architecture: arm64 Version: 0.7.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6095 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_arm64.deb Size: 3501220 MD5sum: 9371ab7f247afadd64643cf8dd3179c6 SHA1: 5ada8083bcb38c02734bbeff9bc2a9e99a009035 SHA256: 39ba1de4c2e44ab62e68ddf6a6b60ccac0c437e1d7b982c300203af0af7c79b5 SHA512: 215958556eb863bd6276c1c476f04d134b7737631a0d24649ea279867d7f697322b913ec5bf44e502315f06aacfa0ec51c2d5ca324e4bbd588b493ec768b759e Homepage: https://cran.r-project.org/package=gdalcubes Description: CRAN Package 'gdalcubes' (Earth Observation Data Cubes from Satellite Image Collections) Processing collections of Earth observation images as on-demand multispectral, multitemporal raster data cubes. Users define cubes by spatiotemporal extent, resolution, and spatial reference system and let 'gdalcubes' automatically apply cropping, reprojection, and resampling using the 'Geospatial Data Abstraction Library' ('GDAL'). Implemented functions on data cubes include reduction over space and time, applying arithmetic expressions on pixel band values, moving window aggregates over time, filtering by space, time, bands, and predicates on pixel values, exporting data cubes as 'netCDF' or 'GeoTIFF' files, plotting, and extraction from spatial and or spatiotemporal features. All computational parts are implemented in C++, linking to the 'GDAL', 'netCDF', 'CURL', and 'SQLite' libraries. See Appel and Pebesma (2019) for further details. Package: r-cran-gdalraster Architecture: arm64 Version: 2.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7094 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_arm64.deb Size: 3834408 MD5sum: 18ec4131849b2e3e61b670c81d603bd8 SHA1: 8eb600657af81ff6fb1a59fdc9e62abd49464a50 SHA256: 9310c1e92193264e45ac3fd90b8a913e1232b8176d49c43c4fec4bad55cd529a SHA512: 13ac2dce82808e2b9ca7f2df1ff103e5e387bda4d309e13fb07bd8fe32e9c160bad32e1ff3ec5e828f0facc9a50200416ec9658bdbe3e9bb1d1d2396c447de3e Homepage: https://cran.r-project.org/package=gdalraster Description: CRAN Package 'gdalraster' (Bindings to 'GDAL') API bindings to the Geospatial Data Abstraction Library ('GDAL', ). Implements the 'GDAL' Raster and Vector Data Models. Bindings are implemented with 'Rcpp' modules. Exposed C++ classes and stand-alone functions wrap much of the 'GDAL' API and provide additional functionality. Calling signatures resemble the native C, C++ and Python APIs provided by the 'GDAL' project. Class 'GDALRaster' encapsulates a 'GDALDataset' and its raster band objects. Class 'GDALVector' encapsulates an 'OGRLayer' and the 'GDALDataset' that contains it. Initial bindings are provided to the unified 'gdal' command line interface added in 'GDAL' 3.11. C++ stand-alone functions provide bindings to most 'GDAL' "traditional" raster and vector utilities, including 'OGR' facilities for vector geoprocessing, several algorithms, as well as the Geometry API ('GEOS' via 'GDAL' headers), the Spatial Reference Systems API, and methods for coordinate transformation. Bindings to the Virtual Systems Interface ('VSI') API implement standard file system operations abstracted for URLs, cloud storage services, 'Zip'/'GZip'/'7z'/'RAR', in-memory files, as well as regular local file systems. This provides a single interface for operating on file system objects that works the same for any storage backend. A custom raster calculator evaluates a user-defined R expression on a layer or stack of layers, with pixel x/y available as variables in the expression. Raster 'combine()' identifies and counts unique pixel combinations across multiple input layers, with optional raster output of the pixel-level combination IDs. Basic plotting capability is provided for raster and vector display. 'gdalraster' leans toward minimalism and the use of simple, lightweight objects for holding raw data. Currently, only minimal S3 class interfaces have been implemented for selected R objects that contain spatial data. 'gdalraster' may be useful in applications that need scalable, low-level I/O, or prefer a direct 'GDAL' API. Package: r-cran-gdina Architecture: arm64 Version: 2.9.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1763 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_arm64.deb Size: 1140312 MD5sum: d958e16c9a94f9e5fe429491dcdc7a6a SHA1: 6cca323c17eabed35cdc7cf4fad6f88847297434 SHA256: 40da291b0f44405091afa2fddf43a6896cff9d7ca2d2ee540c4c88ed523a2dfb SHA512: 2d0ba216015ed670a9975b24dc2967cc6ca9324552ede04a4a6bb17a4c08b9707eca4162e90c0b72b4b87bfc4ac183cd5806212102546711cc83f9251d8e332a Homepage: https://cran.r-project.org/package=GDINA Description: CRAN Package 'GDINA' (The Generalized DINA Model Framework) A set of psychometric tools for cognitive diagnosis modeling based on the generalized deterministic inputs, noisy and gate (G-DINA) model by de la Torre (2011) and its extensions, including the sequential G-DINA model by Ma and de la Torre (2016) for polytomous responses, and the polytomous G-DINA model by Chen and de la Torre for polytomous attributes. Joint attribute distribution can be independent, saturated, higher-order, loglinear smoothed or structured. Q-matrix validation, item and model fit statistics, model comparison at test and item level and differential item functioning can also be conducted. A graphical user interface is also provided. For tutorials, please check Ma and de la Torre (2020) , Ma and de la Torre (2019) , Ma (2019) and de la Torre and Akbay (2019). Package: r-cran-gdm Architecture: arm64 Version: 1.6.0-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5015 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-reshape2, r-cran-vegan, r-cran-doparallel, r-cran-foreach, r-cran-pbapply Suggests: r-cran-tinytest, r-cran-scales, r-cran-terra Filename: pool/dists/resolute/main/r-cran-gdm_1.6.0-7-1.ca2604.1_arm64.deb Size: 1275396 MD5sum: c6495e982b07a6fdbf667b2bbe702c17 SHA1: 5fdf41d6afcd3e8a5aad159ed92122b1b828d222 SHA256: 00028662b1c72b3ffd512e0c6b289d76900d0fe475aee92ad557d5b9e91324ad SHA512: 9912ceb1da1e28bc2a3c4c9da127ecf75ca66846465895850e04a050d01be91772b370fbcc888057c04088e8528cb55c5a3b4fe4b082a64d02d6b17bbcbb7ef1 Homepage: https://cran.r-project.org/package=gdm Description: CRAN Package 'gdm' (Generalized Dissimilarity Modeling) A toolkit with functions to fit, plot, summarize, and apply Generalized Dissimilarity Models. Mokany K, Ware C, Woolley SNC, Ferrier S, Fitzpatrick MC (2022) Ferrier S, Manion G, Elith J, Richardson K (2007) . 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Package: r-cran-gdsarm Architecture: arm64 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_arm64.deb Size: 61310 MD5sum: e808bf33a550976aa8beb0b4860ff68f SHA1: 71f8d929579414034364b3ba4a89ad8022fe90da SHA256: 20c805182f9da1aea7cc2e99900598a93dedd1cbf8d270605a42f760db226fc4 SHA512: 292f4a70a3e0e26e29d22620cc492dc3b0ab6aacb4f36163d4a25d92b38433ea2d97bf8a05a9c1566c86d9f66c7302dcfc1faf4b8d8ca4c25f2ca89439cd7880 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) . 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Package: r-cran-gen3sis Architecture: arm64 Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4909 Depends: libc6 (>= 2.17), 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-raster, r-cran-gdistance, r-cran-stringr, r-cran-bh Suggests: r-cran-knitr, r-cran-markdown, r-cran-testthat, r-cran-rmarkdown, r-cran-formatr Filename: pool/dists/resolute/main/r-cran-gen3sis_1.6.0-1.ca2604.1_arm64.deb Size: 2865204 MD5sum: e97ad407988e3603eb2c7e2813abb0db SHA1: c9c1f97793f8f96a0af41ed9799807701aa06eff SHA256: fde0561fd711e8292b88a291048d2ae79d3b800c34aeff23718a6d954eca9aca SHA512: c5a39e9bae6b001f221555486a36b5f4d436bebc05f1211b67527760d81175929f32e356a99ad2c35c41ef031d2c6104390903789b3c29d20b7e79155d38f2dc Homepage: https://cran.r-project.org/package=gen3sis Description: CRAN Package 'gen3sis' (General Engine for Eco-Evolutionary Simulations) Contains an engine for spatially-explicit eco-evolutionary mechanistic models with a modular implementation and several support functions. 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Package: r-cran-gena Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 182092 MD5sum: 972b863e4e7bcb213c20e2984d35bb68 SHA1: 39d259dbebb6264362a6893ef5caf6ff06c66a9c SHA256: eccb181dfe7cee51666580e64c25094b1b19d948799f9c82cbf63febbaebc3e6 SHA512: c0cab0a6102cac66e62a44ea87cc84c281ce576c0b7b678e186f6f9cce19beb04e0b7128a15427cb29932a7958168abfcfe9a6121ce3b5b371d629bb087ce2f4 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: arm64 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_arm64.deb Size: 373190 MD5sum: 74e243eb0247fe86a634424c5d7eb533 SHA1: 58763ddc5b78f66f87603fd65dcb51ef42beb88a SHA256: 33e893ca88d3231364a2adf15384d5812ebf707f6ddd144a59086fd8bbb087e5 SHA512: a77e8196754cadd63d10558b549454a7dab8b6eff96eb549f8e40ae4c78bcb7367e8a43cf4bb1eaf9b8a72a18cd5568228f8ef353428a235e0e79e92fdc88efe 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: arm64 Version: 1.2.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3073 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 809324 MD5sum: 43a148ebaa87ead04fe92ad381916e6a SHA1: b98dd44db24ac7884b114e7b8e44af68959a68e7 SHA256: a275655e321053b294f9c4db70fc68460d03c050e636a80394682651f03ca92e SHA512: 83905d3763fde8ef8e82eec71b9a3c0c1bbaa88bbcc847d3c61f08dea15d46cf09d22000bf60b1a58f444fc784f785e73d6e1ea02df83b375ca80fbb29b97ce7 Homepage: https://cran.r-project.org/package=genepop Description: CRAN Package 'genepop' (Population Genetic Data Analysis Using Genepop) Makes the Genepop software available in R. This software implements a mixture of traditional population genetic methods and some more focused developments: it computes exact tests for Hardy-Weinberg equilibrium, for population differentiation and for genotypic disequilibrium among pairs of loci; it computes estimates of F-statistics, null allele frequencies, allele size-based statistics for microsatellites, etc.; and it performs analyses of isolation by distance from pairwise comparisons of individuals or population samples. Package: r-cran-generalizedumatrix Architecture: arm64 Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2333 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_arm64.deb Size: 827594 MD5sum: 234e1e540743ee336d0a8a7018e77271 SHA1: afbaec5cd7cdc3a833d9bb21e67c267578b9efee SHA256: 96e7b828aacdffa7290260f47b0c1360662bcb129a1db87a83ff30e30b3ee40c SHA512: 4a23fdf6e3ed2aff1982c25887edf1e71aeea3f613a32a88422cd2402c2a499c4a78420a34a334c39f35e8324cc210b98f025ae01ba4ecd35c3d347922a64959 Homepage: https://cran.r-project.org/package=GeneralizedUmatrix Description: CRAN Package 'GeneralizedUmatrix' (Credible Visualization for Two-Dimensional Projections of Data) Projections are common dimensionality reduction methods, which represent high-dimensional data in a two-dimensional space. However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018] . This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is derived from the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) and the main algorithm called simplified self-organizing map for dimensionality reduction methods is published in . 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However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018] . This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is derived from the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) and the main algorithm called simplified self-organizing map for dimensionality reduction methods is published in Thrun, M.C. and Ultsch, A.: "Uncovering High-dimensional Structures of Projections from Dimensionality Reduction Methods" (2020) . Package: r-cran-generalizedwendland Architecture: arm64 Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1809 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_arm64.deb Size: 1415716 MD5sum: 67bb9914a6bdd26a7903b7007ec28a40 SHA1: d890ed3e3d60ad85c80fc894a43b7b811d94f6c7 SHA256: c87f611a30d9b5fdf456f6d31dc1aa380f0c4c5961b2af4830094466c7802517 SHA512: 290cab4f070aebfff9b8145bbe00d1593333bd86e88504bedb2be3cd1c06f2531f7e165f2c78aa54f661cced01d68b2d137ba40dfc46ee25aa1435a454ed5ec4 Homepage: https://cran.r-project.org/package=GeneralizedWendland Description: CRAN Package 'GeneralizedWendland' (Fully Parameterized Generalized Wendland Covariance Function) A fully parameterized Generalized Wendland covariance function for use in Gaussian process models, as well as multiple methods for approximating it via covariance interpolation. The available methods are linear interpolation, polynomial interpolation, and cubic spline interpolation. Moreno Bevilacqua and Reinhard Furrer and Tarik Faouzi and Emilio Porcu (2019) >. Moreno Bevilacqua and Christian Caamaño-Carrillo and Emilio Porcu (2022) . Reinhard Furrer and Roman Flury and Florian Gerber (2022) >. Package: r-cran-genest Architecture: arm64 Version: 1.4.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2447 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1667806 MD5sum: 3d0bc79ad23c410fb8b5613d63751f1c SHA1: 63155f44b33568f8804840b8233015daeeb1b1d7 SHA256: 90dc0b4375c8caaa356e0d6c8066a23f62c0db03b49d378e6a650f2629c73d61 SHA512: a58a355bf9341390df3319ef29f72e0560a93ddb675278a75532882759b87b19cef3e287bc22baf5acadfb89e262cfb0739aaa16f533f8a81e6250375139fee6 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: arm64 Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 314 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_arm64.deb Size: 86208 MD5sum: 156f4bc39b41e4446c6b2d4fb7586ebc SHA1: f505aab02a1a561b8be06bcfa23149254cc0a52e SHA256: f32805bfadf3d0c1b4c03a2f57c169bbd34dd34951ebcf68068a3ad26c98fb40 SHA512: 181cba55ae49cbf99b97294a62fa72c49c51626ef407141855b9e0980074d0fc2903148c2596af21fdca36949cc24b20ff7b15bc82d797687888ba067e595d01 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: arm64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 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_arm64.deb Size: 178250 MD5sum: 4d49e2fc80de6f7b200631324e008c9a SHA1: a5b98b7f34504e5ab3c769a50ae4b5de1a0e6632 SHA256: 0cfef8935e9fec3025f4b48b4474eb1184435cda502065868ff13b4e4076ebfd SHA512: 2ece87190be4e9205f431f7a15720c08a8f398bf5ec4ed9d272968b5c72d4a3ffc2d02ec4ffd28a4c159551d93708e5c9b3a2474efd3035179da389c1329c9b3 Homepage: https://cran.r-project.org/package=genieclust Description: CRAN Package 'genieclust' (Genie: Fast and Robust Hierarchical Clustering) Genie is a robust hierarchical clustering algorithm (Gagolewski, Bartoszuk, Cena, 2016 ). 'genieclust' is its faster, more capable implementation (Gagolewski, 2021 ). It enables clustering with respect to mutual reachability distances, allowing it to act as an alternative to 'HDBSCAN*' that can identify any number of clusters or their entire hierarchy. When combined with the 'deadwood' package, it can act as an outlier detector. Additional package features include the Gini and Bonferroni inequality indices, external cluster validity measures (e.g., the normalised clustering accuracy, the adjusted Rand index, the Fowlkes-Mallows index, and normalised mutual information), and internal cluster validity indices (e.g., the Calinski-Harabasz, Davies-Bouldin, Ball-Hall, Silhouette, and generalised Dunn indices). The 'Python' version of 'genieclust' is available via 'PyPI'. Package: r-cran-genio Architecture: arm64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 561 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 267812 MD5sum: 2c8640540743098885175d697a8fb1c4 SHA1: 816277c672da0206e935597c5497a10a9853851f SHA256: 709ce7b72edbd25b5c2391bb6954de8f3de1b5ac1760a7db5bea6da73fb6b13a SHA512: 04f3b7b705651ebf3633855a5700ee08579bf4a60a46d7761358bcda022bda4d625c46db68108fa171e3618e6d7f4565b86021cf807a33e66b00b8e90d5e699f Homepage: https://cran.r-project.org/package=genio Description: CRAN Package 'genio' (Genetics Input/Output Functions) Implements readers and writers for file formats associated with genetics data. Reading and writing Plink BED/BIM/FAM and GCTA binary GRM formats is fully supported, including a lightning-fast BED reader and writer implementations. Other functions are 'readr' wrappers that are more constrained, user-friendly, and efficient for these particular applications; handles Plink and Eigenstrat tables (FAM, BIM, IND, and SNP files). There are also make functions for FAM and BIM tables with default values to go with simulated genotype data. Package: r-cran-genlasso Architecture: arm64 Version: 1.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 284528 MD5sum: 8d953b9a676cee8412a21e8e2d1d2e25 SHA1: c62ef5ff811eca604be2d7421bd24f6fdb03bf60 SHA256: 69a789653d706877a5b065ffa7502a2d688a8ff3bc02dcec9524b72e57f65bf9 SHA512: 79bd0ea860f5a51a6aa39b725992ad0bd16dd60d6191015ae7fcf2ec0486392619fac3d6b654bd0598fe1dc50d881ddc2dc98455947b35b854d08447f443d282 Homepage: https://cran.r-project.org/package=genlasso Description: CRAN Package 'genlasso' (Path Algorithm for Generalized Lasso Problems) Computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two subproblems are given to improve stability and speed. See Taylor Arnold and Ryan Tibshirani (2016) . Package: r-cran-genlib Architecture: arm64 Version: 1.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1436 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-kinship2, r-cran-bootstrap, r-cran-matrix, r-cran-lattice, r-cran-quadprog, r-cran-foreach, r-cran-doparallel, r-cran-bh Filename: pool/dists/resolute/main/r-cran-genlib_1.1.10-1.ca2604.1_arm64.deb Size: 964012 MD5sum: 83d725efe3c11c116a9f9e8a18f35602 SHA1: d361fb6a0f10ad02bcca07c2c273dc8a78d50284 SHA256: 2cee72d41db0c8d89300f16e9f3736c47c67b39e551636c01afcd14a575dd635 SHA512: 9ef73a2a3b89ca5f507f4142de861ea5f07c9f05f5bbc39e7c7f1b8233e03838fb13595d11d0719f62886bce702978835307d0f350e686c831591eb405657a1a Homepage: https://cran.r-project.org/package=GENLIB Description: CRAN Package 'GENLIB' (Genealogical Data Analysis) Genealogical data analysis including descriptive statistics (e.g., kinship and inbreeding coefficients) and gene-dropping simulations. See: "GENLIB: an R package for the analysis of genealogical data" Gauvin et al. (2015) . Package: r-cran-genodds Architecture: arm64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 72888 MD5sum: 47e3dc78ee415843abfb6f87fa754e3e SHA1: 8d95e24f506bb9d36aa995f4ccb0dbd963e2d6ed SHA256: b5279e567818e7f82a0bbdcecf73c4eb68ceeeebf5fa95b1d1ba7202aaa575db SHA512: c5443b0bc2123e9dad583f979b33d7f3ba5dc90f43692ce5c85bfb01832d4756c4feb5052c3d34acd6d6ec63b93541448a1c8b5f6ea57e85564032feded8ece7 Homepage: https://cran.r-project.org/package=genodds Description: CRAN Package 'genodds' (Generalised Odds Ratios) Calculates Agresti's generalized odds ratios. For a randomly selected pair of observations from two groups, calculates the odds that the second group will have a higher scoring outcome than that of the first group. Package provides hypothesis testing for if this odds ratio is significantly different to 1 (equal chance). Package: r-cran-genomeadmixr Architecture: arm64 Version: 2.1.13.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3055 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-ggplot2, r-cran-ggridges, r-cran-hierfstat, r-cran-rcpp, r-cran-rcppparallel, r-cran-rlang, r-cran-stringr, r-cran-tibble, r-cran-vcfr, r-cran-rcpparmadillo Suggests: r-cran-dplyr, r-cran-junctions, r-cran-knitr, r-cran-magrittr, r-cran-rmarkdown, r-cran-testit, r-cran-testthat, r-cran-pbapply Filename: pool/dists/resolute/main/r-cran-genomeadmixr_2.1.13.2-1.ca2604.1_arm64.deb Size: 1778888 MD5sum: 80c7de7a37052b3980923565ceb1d6db SHA1: 533e7aea3e0dfb9e4ac62323f4d6f5a845a18955 SHA256: 78d99095521f02aa1525b18a9a9183a3cfb0d670c9b8e251b914b5ee7a97e5f3 SHA512: 12efd6e08333de2d85fa9da15a12546983800671e32d6c4f2a311784363af856aa1ac5bb532148855a4e091e2b2b5e3e8aa984687b45f756e75c49b6eea814a9 Homepage: https://cran.r-project.org/package=GenomeAdmixR Description: CRAN Package 'GenomeAdmixR' (Simulate Admixture of Genomes) Individual-based simulations forward in time, simulating how patterns in ancestry along the genome change after admixture. Full description can be found in Janzen (2021) . Package: r-cran-gensa Architecture: arm64 Version: 1.1.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 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_arm64.deb Size: 60946 MD5sum: c187d5cf41b589091ece236eba0d89e7 SHA1: 4078c5667ce2ab2a6f7ba5bc02b95726c146be43 SHA256: 801f97a63105d9f01737ae531a69fd44e082a08984655bd21361b84d26d60d29 SHA512: d77ee3e398c07d99f928996cfbc8e69320647ebb82ba84638c6b50f1cfa06893f34923adc99beb60c8aa06dd3412f4038c40dfe61a6850edb02a60454c8b92ef Homepage: https://cran.r-project.org/package=GenSA Description: CRAN Package 'GenSA' (R Functions for Generalized Simulated Annealing) Performs search for global minimum of a very complex non-linear objective function with a very large number of optima. Package: r-cran-genscore Architecture: arm64 Version: 1.0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1392 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 899194 MD5sum: e272e95371862cd1f7e101219f20c810 SHA1: 652c737b2bdb274fd025108f00d4b1d2104d2aff SHA256: ec9de527c912026cc4958db33d3f8ca1e837192f466c8bbf6714eb2f7f166d11 SHA512: 439dbd1364029025a73353166eafc1ace8f7ba1abe66608ec089fa1ca6345bdc7471641880b536e61205160e7357e385e708332deed2cc904e6632ce59ecdd06 Homepage: https://cran.r-project.org/package=genscore Description: CRAN Package 'genscore' (Generalized Score Matching Estimators) Implementation of the Generalized Score Matching estimator in Yu et al. (2019) for non-negative graphical models (truncated Gaussian, exponential square-root, gamma, a-b models) and univariate truncated Gaussian distributions. Also includes the original estimator for untruncated Gaussian graphical models from Lin et al. (2016) , with the addition of a diagonal multiplier. Package: r-cran-gensurv Architecture: arm64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 188 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-survival Filename: pool/dists/resolute/main/r-cran-gensurv_1.0.6-1.ca2604.1_arm64.deb Size: 87810 MD5sum: ad704773f55a85707155d23dfa5c75ce SHA1: 35435520e2cad01f833e067591f885b81c146a8d SHA256: 80daba56561435faf6b8e7fef454ac7db6e38ee0fe49c70048f434ed97e81e95 SHA512: 7283b759879c2e7fef7b3dba1530b057802ca21f8dbbeee6ed35181dc6e7ce40e42fab17873d1dee48bba60092c169c6d102dca7afff79b06603d463db91c42e Homepage: https://cran.r-project.org/package=genSurv Description: CRAN Package 'genSurv' (Generating Multi-State Survival Data) Generation of survival data with one (binary) time-dependent covariate. Generation of survival data arising from a progressive illness-death model. Package: r-cran-gensvm Architecture: arm64 Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 295 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_arm64.deb Size: 166654 MD5sum: b300c6e24168704a5ee42589bd4d4d9a SHA1: 0120e8894695283445ed0e07a38717e4e0e7a398 SHA256: 1e1c0baa095d48e9e1e76aa20bf52bcf99b8d875838cc8103340197e0ebda7fc SHA512: 5495e3e688630a2027c55cbfedf1b62ebd09f06071a8566cfd69f0148d5b39f16d448ce310c87df0f3153ca10f9bf8bc11fbf628dc71eed8ddaccc1ed6038edc Homepage: https://cran.r-project.org/package=gensvm Description: CRAN Package 'gensvm' (A Generalized Multiclass Support Vector Machine) The GenSVM classifier is a generalized multiclass support vector machine (SVM). This classifier aims to find decision boundaries that separate the classes with as wide a margin as possible. In GenSVM, the loss function is very flexible in the way that misclassifications are penalized. This allows the user to tune the classifier to the dataset at hand and potentially obtain higher classification accuracy than alternative multiclass SVMs. Moreover, this flexibility means that GenSVM has a number of other multiclass SVMs as special cases. One of the other advantages of GenSVM is that it is trained in the primal space, allowing the use of warm starts during optimization. This means that for common tasks such as cross validation or repeated model fitting, GenSVM can be trained very quickly. Based on: G.J.J. van den Burg and P.J.F. Groenen (2018) . Package: r-cran-geoadjust Architecture: arm64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1695 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fmesher, r-cran-terra, r-cran-sf, r-cran-summer, r-cran-matrix, r-cran-ggplot2, r-cran-fields, r-cran-tmb, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-geoadjust_2.0.1-1.ca2604.1_arm64.deb Size: 1034282 MD5sum: 97e541fa5d319f47d5059db2afedf1b4 SHA1: 895f79ad7cc94f5a53f15c44e513499169577059 SHA256: aa650f19c25ddc7554473674d29e996a32581789a61c63fb47ccdd8569cfca0e SHA512: 63f816b65124fe7ab664ac3f5a3c7d6acd79bf40f45f61269d7cd74aca61cc6f98728e519eb024a9c11874d4837edd28f1391f699482cd821503aecaf627cd70 Homepage: https://cran.r-project.org/package=GeoAdjust Description: CRAN Package 'GeoAdjust' (Accounting for Random Displacements of True GPS Coordinates ofData) The purpose is to account for the random displacements (jittering) of true survey household cluster center coordinates in geostatistical analyses of Demographic and Health Surveys program (DHS) data. Adjustment for jittering can be implemented either in the spatial random effect, or in the raster/distance based covariates, or in both. Detailed information about the methods behind the package functionality can be found in our two papers. Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2024) . Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2023) . Package: r-cran-geoarrow Architecture: arm64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 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_arm64.deb Size: 215586 MD5sum: 6193d7b13b512d9e4c70ba07250918a5 SHA1: a3542e29a9b72905e4c60a97f896e13920463ffe SHA256: 23dc3eb186fb2f1a9e3c94f0ff0a66054ce611b942749f5af4d1b5cd9c52ea56 SHA512: 24c25e339e5489cb625b7c6ddb9b981ae3872476b1b2bda0652381506cba34ac092cd998a08043400aa0caabc456f2135720760df09f51e3068a74e3944878f6 Homepage: https://cran.r-project.org/package=geoarrow Description: CRAN Package 'geoarrow' (Extension Types for Spatial Data for Use with 'Arrow') Provides extension types and conversions to between R-native object types and 'Arrow' columnar types. This includes integration among the 'arrow', 'nanoarrow', 'sf', and 'wk' packages such that spatial metadata is preserved wherever possible. Extension type implementations ensure first-class geometry data type support in the 'arrow' and 'nanoarrow' packages. Package: r-cran-geobayes Architecture: arm64 Version: 0.7.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 859 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0, r-cran-coda, r-cran-sp, r-cran-optimx Filename: pool/dists/resolute/main/r-cran-geobayes_0.7.6-1.ca2604.1_arm64.deb Size: 536350 MD5sum: 2fc8f6abc6d24a4e2055a95e86f72378 SHA1: 4522f8c23625d54a7fb97aaf74f3678d4379c5d3 SHA256: 922479fbf16a2d8b75709c9b5fcce707c4abb15557f3d6cb3f8fdd2b704f4fe4 SHA512: 06e6a90248491eea6e45af8f937122dedf72859603c307272a2dc82a16a259d9b9a1f78f3440b9ff7a6c2b5f13708f528350569b1841bcd65c92809d23e58808 Homepage: https://cran.r-project.org/package=geoBayes Description: CRAN Package 'geoBayes' (Analysis of Geostatistical Data using Bayes and Empirical BayesMethods) Functions to fit geostatistical data. The data can be continuous, binary or count data and the models implemented are flexible. Conjugate priors are assumed on some parameters while inference on the other parameters can be done through a full Bayesian analysis of by empirical Bayes methods. Package: r-cran-geocmeans Architecture: arm64 Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5832 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_arm64.deb Size: 4250462 MD5sum: 369a386c36643553e51b4194809491b9 SHA1: d964153f11353d615fa433c4ca36a7d4ca8d7204 SHA256: 56d9e780eba8e6718d278abae397c747c1a5c916293ca564e4a1fc93fde41959 SHA512: 6187b5a189e5dad9a3369929f78c7127e7ceef9c7a5b621ad8578f0bdaa4575a5a296a65299c862d0b321ac1ec9f9eb15abbe568d0b8b98d18be00d85722a55f Homepage: https://cran.r-project.org/package=geocmeans Description: CRAN Package 'geocmeans' (Implementing Methods for Spatial Fuzzy UnsupervisedClassification) Provides functions to apply spatial fuzzy unsupervised classification, visualize and interpret results. This method is well suited when the user wants to analyze data with a fuzzy clustering algorithm and to account for the spatial dimension of the dataset. In addition, indexes for estimating the spatial consistency and classification quality are proposed. The methods were originally proposed in the field of brain imagery (seed Cai and al. 2007 and Zaho and al. 2013 ) and recently applied in geography (see Gelb and Apparicio ). Package: r-cran-geocomplexity Architecture: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1778 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-sdsfun, r-cran-sf, r-cran-terra, r-cran-tibble, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-gdverse, r-cran-ggplot2, r-cran-infoxtr, r-cran-knitr, r-cran-rmarkdown, r-cran-spedm, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-geocomplexity_0.3.0-1.ca2604.1_arm64.deb Size: 862218 MD5sum: 6eb60c4666a7b7b4e97b5d36280eee02 SHA1: 44d3163b875d6b4dba8a1fe8f56a31c8d75ce3c8 SHA256: 91ab21041304d045cbb7ad45cd6e5bfc5d479cb4af3e35bc2bca20495c81cec7 SHA512: 78b67767f8efa61fd307e0c3c3375d203e1375745986dedb80bf59e7fbe15fda0807b17ea28a333e55c5b9f039827f27e614fa92867dc0d911e49d88db62af3e Homepage: https://cran.r-project.org/package=geocomplexity Description: CRAN Package 'geocomplexity' (Mitigating Spatial Bias Through Geographical Complexity) The geographical complexity of individual variables can be characterized by the differences in local attribute variables, while the common geographical complexity of multiple variables can be represented by fluctuations in the similarity of vectors composed of multiple variables. In spatial regression tasks, the goodness of fit can be improved by incorporating a geographical complexity representation vector during modeling, using a geographical complexity-weighted spatial weight matrix, or employing local geographical complexity kernel density. Similarly, in spatial sampling tasks, samples can be selected more effectively by using a method that weights based on geographical complexity. By optimizing performance in spatial regression and spatial sampling tasks, the spatial bias of the model can be effectively reduced. Package: r-cran-geodist Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1500 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 484712 MD5sum: 44ee8e2ae5ec2436b578511e67809347 SHA1: 7799bcbfe7a1f5ee650c9421bea20f5475ce654c SHA256: c311a0469a72d0fcdb079e753de67154e95d0fc0db21494ecd47754aba2d2987 SHA512: 5c80638fe9a023e19d7c0d888a9348a2d3143ba020b856cc47a955aabb04fc5ccafa01da8a36497e752f6bad710770cb988549a9fb1d352d8eccbd7fbc6fdabc Homepage: https://cran.r-project.org/package=geodist Description: CRAN Package 'geodist' (Fast, Dependency-Free Geodesic Distance Calculations) Dependency-free, ultra fast calculation of geodesic distances. Includes the reference nanometre-accuracy geodesic distances of Karney (2013) , as used by the 'sf' package, as well as Haversine and Vincenty distances. Default distance measure is the "Mapbox cheap ruler" which is generally more accurate than Haversine or Vincenty for distances out to a few hundred kilometres, and is considerably faster. The main function accepts one or two inputs in almost any generic rectangular form, and returns either matrices of pairwise distances, or vectors of sequential distances. 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This package is an R implementation of methods provided by the open source software GeoFIS (Leroux et al. 2018) . The main functionalities are the management zone delineation (Pedroso et al. 2010) and data aggregation (Mora-Herrera et al. 2020) . Package: r-cran-geographiclib Architecture: arm64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2107 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-geographiclib_0.4.2-1.ca2604.1_arm64.deb Size: 672960 MD5sum: 33ceafd705db9f4464560f044d3d10f2 SHA1: 546f397f84d84e6f830e00aeeaa0cd458f866373 SHA256: de2f3db87bb64ebaf2a3664bd2d99311ad9cb0e8a403b13b9bf6be713c81f6b6 SHA512: 32150c3cfd692868b6491a9598ceb2da01749fe8f3b46293b3ea144c6ddde6e0bc9475538dc6bc79a81dee4f04fcd6795af0d77ccfe5e7b6c0444e71f3e14243 Homepage: https://cran.r-project.org/package=geographiclib Description: CRAN Package 'geographiclib' (Access to 'GeographicLib') Bindings to the 'GeographicLib' C++ library for precise geodetic calculations including geodesic computations (distance, bearing, paths, intersections), map projections (UTM/UPS, Transverse Mercator, Lambert Conformal Conic, and more), grid reference systems (MGRS, Geohash, GARS, Georef), coordinate conversions (geocentric, local Cartesian), and polygon area on the WGS84 ellipsoid. 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Package: r-cran-geohashtools Architecture: arm64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 263 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-sf, r-cran-sp, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-geohashtools_0.3.3-1.ca2604.1_arm64.deb Size: 104296 MD5sum: e4d368f237af63299b0d72e711863687 SHA1: 186e5e7c5d59722b78a320d91e65d105fb24d484 SHA256: f3b47ce621be801c9e08aec74b5063ccbb32510a3f27f041988153c1403f09d3 SHA512: a2288ef216e9ad9526e02735b3deb5e9b8cf82546ac475ac57090adae339538bf81a5824c8a1a007d1eb305e3bdddde8c0d11196ba90bf95c1da465d51b24ce1 Homepage: https://cran.r-project.org/package=geohashTools Description: CRAN Package 'geohashTools' (Tools for Working with Geohashes) Tools for working with Gustavo Niemeyer's geohash coordinate system, including API for interacting with other common R GIS libraries. 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Package: r-cran-geommc Architecture: arm64 Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1591 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 979210 MD5sum: e44e9659ceb9effeff850e17c5417c5f SHA1: e1a901f0145cef3f0ca82079d8c00e14b52b6485 SHA256: fd7f31d3a706bfe18cf4d29b24f08a629e1648a8f5bce065bcf9d001e602cf96 SHA512: 3bd7b9349fb20c52e87832e4a7f17e77983e597282e10d222d69c0ebab9185a988626528da3028b038c5ab9957396bf9e008e5e272d2f37af5d2e22277f1007d 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: arm64 Version: 2.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4047 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_arm64.deb Size: 3700986 MD5sum: 24731b9b4835ab1d3ef95ee7c9954f38 SHA1: c0290a8f130e6e7f95d66f569592b82476660f37 SHA256: 948e9bcea051ec5af8d5fe76f75de0c4f57a4149f1ad931c35a610c0356f2eb2 SHA512: 52665259441a0aef46ed03554ada681224fe4812964d76f40b73bd6e3e3b60c039049561db69d75536abfcde74465de0406e51c7919bd39351e08cc1ea57c98a Homepage: https://cran.r-project.org/package=GeoModels Description: CRAN Package 'GeoModels' (Procedures for Gaussian and Non Gaussian Geostatistical (Large)Data Analysis) Functions for Gaussian and Non Gaussian (bivariate) spatial and spatio-temporal data analysis are provided for a) (fast) simulation of random fields, b) inference for random fields using standard likelihood and a likelihood approximation method called weighted composite likelihood based on pairs and b) prediction using (local) best linear unbiased prediction. Weighted composite likelihood can be very efficient for estimating massive datasets. Both regression and spatial (temporal) dependence analysis can be jointly performed. Flexible covariance models for spatial and spatial-temporal data on Euclidean domains and spheres are provided. There are also many useful functions for plotting and performing diagnostic analysis. Different non Gaussian random fields can be considered in the analysis. Among them, random fields with marginal distributions such as Skew-Gaussian, Student-t, Tukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian, Binomial, Negative Binomial and Poisson. See the URL for the papers associated with this package, as for instance, Bevilacqua and Gaetan (2015) , Bevilacqua et al. (2016) , Vallejos et al. (2020) , Bevilacqua et. al (2020) , Bevilacqua et. al (2021) , Bevilacqua et al. (2022) , Morales-Navarrete et al. (2023) , and a large class of examples and tutorials. Package: r-cran-geor Architecture: arm64 Version: 1.9-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1700 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1498942 MD5sum: 7630a6b34ff0282f25c1d869e0b1fa58 SHA1: 3b6e9218706b51b0b81ee859abf7f2bf36fc3928 SHA256: e8c27f1eebe48674221b14358a4d14e22bd6082e9c65a724d27d439d0f56a72b SHA512: b582b1489f5836b8c447ba7aa8585a90b95b24b7abfcda5575b9113f94559f77ccddb6a007a8e0ca1d7c589d3e4985feb40c08ac1f0386e0cded91bccac6a0f9 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-geosphere Architecture: arm64 Version: 1.6-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1325 Depends: libc6 (>= 2.43), libgcc-s1 (>= 4.5), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-terra, r-cran-sf, r-cran-sp Filename: pool/dists/resolute/main/r-cran-geosphere_1.6-8-1.ca2604.1_arm64.deb Size: 1034628 MD5sum: 34b8290f441a4160b2ab47b946d608c3 SHA1: 10cddd116f96eb66f13e863182dd47ca8fa92244 SHA256: 3408bfe19db86d9a01edcf6d08fb33b2da5c260725b70847b6e59f7a7efdf47c SHA512: ad1e15376211207fbaf1130d947f5db364d4d8935dce551413ed068318803e88c35691f8481c9471144b11000fb6d4a8c819c37fce4f126ab9af9268c5d18c23 Homepage: https://cran.r-project.org/package=geosphere Description: CRAN Package 'geosphere' (Spherical Trigonometry) Spherical trigonometry for geographic applications. That is, compute distances and related measures for angular (longitude/latitude) locations. Package: r-cran-geostan Architecture: arm64 Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5006 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 14), r-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spdep, r-cran-sf, r-cran-ggplot2, r-cran-spdata, r-cran-mass, r-cran-truncnorm, r-cran-signs, r-cran-gridextra, r-cran-matrix, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-bayesplot Filename: pool/dists/resolute/main/r-cran-geostan_0.8.2-1.ca2604.1_arm64.deb Size: 2490546 MD5sum: 8a925acc79aec519106655f7127cdeb8 SHA1: 547339bdce4b1ad850e822adf634a72e05206acf SHA256: 752ba86e7dbb6f47d5d66ffc94410f33a0ceddca09d68d4dda755255e13bcbbc SHA512: ba13735e64d9e8907ab913d1f60de8b627032a391f1b5d70cb6dc35bfd620b08a56865bb58977106c48fbbc9bc2c6e316460cda291fd84baa46f24c8b1eaf00d Homepage: https://cran.r-project.org/package=geostan Description: CRAN Package 'geostan' (Bayesian Spatial Analysis) For spatial data analysis; provides exploratory spatial analysis tools, spatial regression, spatial econometric, and disease mapping models, model diagnostics, and special methods for inference with small area survey data (e.g., the America Community Survey (ACS)) and censored population health monitoring data. 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Package: r-cran-ggdist Architecture: arm64 Version: 3.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3536 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-scales, r-cran-rlang, r-cran-cli, r-cran-tibble, r-cran-vctrs, r-cran-withr, r-cran-glue, r-cran-gtable, r-cran-distributional, r-cran-numderiv, r-cran-quadprog, r-cran-rcpp Suggests: r-cran-tidyselect, r-cran-dplyr, r-cran-fda, r-cran-posterior, r-cran-beeswarm, r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-vdiffr, r-cran-svglite, r-cran-fontquiver, r-cran-sysfonts, r-cran-showtext, r-cran-mvtnorm, r-cran-covr, r-cran-broom, r-cran-patchwork, r-cran-tidyr, r-cran-ragg, r-cran-pkgdown Filename: pool/dists/resolute/main/r-cran-ggdist_3.3.3-1.ca2604.1_arm64.deb Size: 2718840 MD5sum: 9d2b0b2f297befa86bec2d4ed2968964 SHA1: 06263b2222f756aed5bd6cb8e5aac6f990b555fd SHA256: a9535d329edc4dc43972fbc7f927137ba20f1c52d7e913a03a3bfcb8fb6ed403 SHA512: 48bb44f9fbb0607cc7e5763b8a2b1b2c5fdbf394d95b4858ff7dcdb87217491c451e4394d98e7dac73e7376d138c396affd516bda667480093079299b9ca741b Homepage: https://cran.r-project.org/package=ggdist Description: CRAN Package 'ggdist' (Visualizations of Distributions and Uncertainty) Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for visualizing uncertainty in either a frequentist or Bayesian mode. 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Package: r-cran-ggdmc Architecture: arm64 Version: 0.2.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 734 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_arm64.deb Size: 403230 MD5sum: c6134b0eac96701634b5de2f1f0f6217 SHA1: a2d238af42a331999b2a8e8cd389cce11c427262 SHA256: 10b403ecf1c454690c8235ac4b7605fb870b39161681ef82df15eeec612e19e3 SHA512: 42e6d3ac6615b05c0d2f96b204245effb64a8576b0ffcebb23bc949f5febd5defaab7a6b494c052e802f8a22907350d46de78d2111467ec614e5a9c79dee3f69 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: arm64 Version: 0.7.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6006 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_arm64.deb Size: 2319000 MD5sum: 3e07918c2d4c7d2a3b83944d446c268c SHA1: e8cbff77117c4f5c900bbd880af4730eedd51dd7 SHA256: 93f9620a4808fbb0530a539ebf8e662495611915d7d58c4e8c14232055b414f7 SHA512: b65874ce4528c20f1d31ded6c0dcb892c42557e2b688436f5f864349a0bcbda2979f501976f4c9661f4baa69bca3b16e369d855338dc1bf398bd9570d5094677 Homepage: https://cran.r-project.org/package=ggmlR Description: CRAN Package 'ggmlR' ('GGML' Tensor Operations for Machine Learning) Provides 'R' bindings to the 'GGML' tensor library for machine learning, optimized for 'Vulkan' GPU acceleration with a transparent CPU fallback. The package features a 'Keras'-like sequential API and a 'PyTorch'-style 'autograd' engine for building, training, and deploying neural networks. Key capabilities include high-performance 5D tensor operations, 'f16' precision, and efficient quantization. It supports native 'ONNX' model import (50+ operators) and 'GGUF' weight loading from the 'llama.cpp' and 'Hugging Face' ecosystems. Designed for zero-overhead inference via dedicated weight buffering, it integrates seamlessly as a 'parsnip' engine for 'tidymodels' and provides first-class learners for the 'mlr3' framework. See for more information about the underlying library. Package: r-cran-ggmncv Architecture: arm64 Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1711 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1237146 MD5sum: cc957e65782edbddbbee5c7ff6b52af9 SHA1: 183919be16fc32ac1c484acbbca0893b258f9205 SHA256: 8295d90c040d56d7d08199bd049d7c14e077e79d86f41df58094333b06f7dd97 SHA512: bc4afe5be20bf9d4308297167291de00f4aad95f229c3bd63ee382427d5def64a4830fb54e1687df36194a967123642c04e4ac114bd301ef7071c32162f0ec0c 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: arm64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5942 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_arm64.deb Size: 5884312 MD5sum: a739a71fa86508f48b630ed04ca49a25 SHA1: 135703a40dfd63ead06d775b7d1de8df88ac1b7d SHA256: 63f14d6d8f58364442dfc74e303a65a925fbb9620465052abb57e67b7c696082 SHA512: 0f5d18310a88edb349b99cd65bd6db3cd1ebfe816a4f191a59406002e1a917db3d239ccebf4042cfeb6e07e07a78e60a9c72a387fc7f5a5edf6760ce4ba401be Homepage: https://cran.r-project.org/package=ggpointdensity Description: CRAN Package 'ggpointdensity' (A Cross Between a 2D Density Plot and a Scatter Plot) A cross between a 2D density plot and a scatter plot, implemented as a 'ggplot2' geom. 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Package: r-cran-ggraph Architecture: arm64 Version: 2.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6071 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_arm64.deb Size: 4649394 MD5sum: 8794169f306c42e1a7869de6af55c604 SHA1: aafb2c8ab575c1adcb78c55898bf5ea81cd0c3c8 SHA256: de3c20862e4c5192ccbedaf8e6f97448e983cb6b85b66769e824c830ca460db8 SHA512: fcdfcc40e7cb7f60f2a9fe9e837a80ab7efc7088df0166ef6c9499468a7f9e29ec03b12dbb9de6de3772947fd71e9d2464b9770a77e63f7cd1e7c0a23d6c7daa 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. 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Package: r-cran-ghcm Architecture: arm64 Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3650 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 3079776 MD5sum: 6e15994aec2b000ed0b5a98e233f4287 SHA1: e94a74ff9230f068a91ce842955777bb673c2979 SHA256: 5a5b553c7632f51e68845967b3b802e3775aea8b5af074c0944fe20d4aa67f07 SHA512: d48e4a244cd78b8629a5b2192da288787c055b022bd8ab68903002bf78e79227188df07567269315010551d46552ed5413c3bc9b38e7d4da18ab1f9447144b4e 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: arm64 Version: 1.6.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1567 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_arm64.deb Size: 1338682 MD5sum: 6283e960619252d8cc3dff1dd8b9850e SHA1: 4b965bd0168102400bc8a1eac7b9b3427b128392 SHA256: 4b213e04e5b092f3ebfa2eb111011436896f12462a5b93d6c1ed032e679ac914 SHA512: 167867bb1e79822dcbbc9009d7a1da58800c6f30e7573b2b88e5295adfd89a8904098305165557ae296735bc770c5f1e8fa840a9b2d20b247ee991cd333e5701 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: arm64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 919 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_arm64.deb Size: 454622 MD5sum: 7f7b1064ab951d4a565e8eeafbf1bf15 SHA1: 8e46e90f860cb013c8aa58bef50cc00e5d7937fe SHA256: c50f10b1402e851cc48e7fb39cb34d4815bba59dad41c7127553b494101da0b3 SHA512: 2b3f1d22ce4d3ba811a687a8f6b3b93e893f0c95b7bd3537d50cb0a73972787af9475f39f81c2972e6d7de122b6111c40404bc88078895245a294bf1a1c906e6 Homepage: https://cran.r-project.org/package=gibasa Description: CRAN Package 'gibasa' (An Alternative 'Rcpp' Wrapper of 'MeCab') A plain 'Rcpp' wrapper for 'MeCab' that can segment Chinese, Japanese, and Korean text into tokens. 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Package: r-cran-gicf Architecture: arm64 Version: 1.0.1-1.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-rcpp, r-cran-rcpparmadillo Suggests: r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-gicf_1.0.1-1.ca2604.1_arm64.deb Size: 86144 MD5sum: bc5d069dadec99d7aafef86c2724ed2b SHA1: 998ca35c220354b26f2656cb7dbc85bae0b71ff8 SHA256: 7c527af2c12ae2a29a2dd465184035da4b5e855284809450e9c3039407fede3c SHA512: e3186fd7dd15dc473ed6f4bee3e3c77694dad33541ae33d93c00318f0b76c277fd3750e6bbc3fe6f3f4237d96149b944012b35165e1eddc38a47de31af2ebf4f 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: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1473 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1054980 MD5sum: c9fc6bf7ba0623b94aad36ff0e380c52 SHA1: 031933a4565a7e95581434ff8199c18ac2085f98 SHA256: 4222d067e747f77b33af7b162143f78f5e2e910fc2f8f0451e8b57160fb259eb SHA512: a79a6c34d90f4f95d3a69ca8a1370d50d326d4e8729ba58b48849b680052c9f4640d83e12f75d4179d317ed099795d56265f97e452f494fa25561f2b168fa234 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: arm64 Version: 1.0-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1511 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 951398 MD5sum: 0ee367a078ec7734b88523bd0b8b0721 SHA1: 9eea43120f875507012c85f80c92a55ae4586a54 SHA256: bfaea90c25aa2cff61383fc59e1dd3b506ff574e63fb036e6960bcc9599f49d8 SHA512: 85f04bd1da9be7f5a6374bf30df89b588dc1e64d0d57699c46cf787f7571195ce1fdd8d28999a2d1c1e0e956c3d453892f59b070878b529bc136bb608dcb9afa 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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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-glca Architecture: arm64 Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2029 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_arm64.deb Size: 989866 MD5sum: c40ded0268d0cf354a618eca675899d7 SHA1: 2a8e663b32d566e276d2d54e105c329b5ee2c1fd SHA256: 33f6455abfe798d9e5b21ab274660ddbfb80b68dbbc9e895c73f5f34b3f35d83 SHA512: 961cec0a308f0bfcff111f3d26f657df6c49334894f4abf8b8959d085be1c858f8da28fa66b1bc1a39c32ad15b8b309f3c684d33106310c9ea32cf761759835b Homepage: https://cran.r-project.org/package=glca Description: CRAN Package 'glca' (An R Package for Multiple-Group Latent Class Analysis) Fits multiple-group latent class analysis (LCA) for exploring differences between populations in the data with a multilevel structure. There are two approaches to reflect group differences in glca: fixed-effect LCA (Bandeen-Roche et al (1997) ; Clogg and Goodman (1985) ) and nonparametric random-effect LCA (Vermunt (2003) ). Package: r-cran-glcm Architecture: arm64 Version: 1.6.6-1.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-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_arm64.deb Size: 266960 MD5sum: 562b39068f216aaeab91a5653543b647 SHA1: 67fe0f8a9717149682ca9cfa25f6ccad77924fe8 SHA256: cc075228077243677ce59c591303912a5f0e8ba28306105c989cb1563c541c90 SHA512: 57c6b0473bf521dca3986a2ff2cefed6579bdf5160c827d128dc1b8ea0d17e09c8885b1f5e942f85b13736cae767ffe3a7ecd7b9d98d8a96ae003fa81ea5fc35 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: arm64 Version: 0.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 765 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_arm64.deb Size: 576056 MD5sum: 807960bb2dd4bcf49d0940baefabfef9 SHA1: 650d09ce92f740b874db2b6fa9ef5ba0f7667216 SHA256: 804c56e6aa3abd98fbdd4e5b9ccf460e012b7cdcab2068057ed0ec3f2d16b113 SHA512: b4f9f57333e82c69ee5c825f5ed664c2828213a56ff426033da08229cf044c2e81379bd490a59f2d7153df7c382594a98c7039b4a1d194393a3456c7cc2eb943 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: arm64 Version: 2.6.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-e1071, r-cran-lmom Filename: pool/dists/resolute/main/r-cran-gld_2.6.8-1.ca2604.1_arm64.deb Size: 235220 MD5sum: 3070312c9940f329708f0a26056741a8 SHA1: 71d7302968e6a65b16a554b27b77467e38cd7bde SHA256: 828e1d59d1c60cb0f97e7745b4e6c0a22b1a82fde0cab204d7662d6eca185fc9 SHA512: 75ce935ad81be887d27d96c638c94059845a17a14cf78ff174361ba73193cab134465c7c70d4cd922d1c0c7ddc3a120892323dfc20f18573c15cfda835b48f55 Homepage: https://cran.r-project.org/package=gld Description: CRAN Package 'gld' (Estimation and Use of the Generalised (Tukey) LambdaDistribution) The generalised lambda distribution, or Tukey lambda distribution, provides a wide variety of shapes with one functional form. This package provides random numbers, quantiles, probabilities, densities and density quantiles for four different types of the distribution, the FKML (Freimer et al 1988), RS (Ramberg and Schmeiser 1974), GPD (van Staden and Loots 2009) and FM5 - see documentation for details. It provides the density function, distribution function, and Quantile-Quantile plots. It implements a variety of estimation methods for the distribution, including diagnostic plots. Estimation methods include the starship (all 4 types), method of L-Moments for the GPD and FKML types, and a number of methods for only the FKML type. These include maximum likelihood, maximum product of spacings, Titterington's method, Moments, Trimmed L-Moments and Distributional Least Absolutes. Package: r-cran-gldex Architecture: arm64 Version: 2.0.0.9.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 612 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-spacefillr Filename: pool/dists/resolute/main/r-cran-gldex_2.0.0.9.4-1.ca2604.1_arm64.deb Size: 501860 MD5sum: 961a7afc683b16004ee627f1d658e176 SHA1: 036c3ff6f0ada7d6f54e0e1aa749c732b5c669d1 SHA256: d5672887cfe0969de89b1c5b7e5331e593208bfa591d1067ee12221a0e334940 SHA512: f6ad13320441381f1baf1bdb7fcd4f6fbb3ecced71e0c1b39fe9265e170f946f8e6d6be9fe9c8026099c16164e1302e6effd1a5375603c2b87c76bcabdede973 Homepage: https://cran.r-project.org/package=GLDEX Description: CRAN Package 'GLDEX' (Fitting Single and Mixture of Generalised Lambda Distributions) The fitting algorithms considered in this package have two major objectives. One is to provide a smoothing device to fit distributions to data using the weight and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution. References include the following: Karvanen and Nuutinen (2008) "Characterizing the generalized lambda distribution by L-moments" , King and MacGillivray (1999) "A starship method for fitting the generalised lambda distributions" , Su (2005) "A Discretized Approach to Flexibly Fit Generalized Lambda Distributions to Data" , Su (2007) "Nmerical Maximum Log Likelihood Estimation for Generalized Lambda Distributions" , Su (2007) "Fitting Single and Mixture of Generalized Lambda Distributions to Data via Discretized and Maximum Likelihood Methods: GLDEX in R" , Su (2009) "Confidence Intervals for Quantiles Using Generalized Lambda Distributions" , Su (2010) "Chapter 14: Fitting GLDs and Mixture of GLDs to Data using Quantile Matching Method" , Su (2010) "Chapter 15: Fitting GLD to data using GLDEX 1.0.4 in R" , Su (2015) "Flexible Parametric Quantile Regression Model" , Su (2021) "Flexible parametric accelerated failure time model". Package: r-cran-glinternet Architecture: arm64 Version: 1.0.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 107440 MD5sum: 046a4eee01a9a8e9df2a78978c99f8f9 SHA1: 5f945066c6903c578968eacf776bb418d42d4103 SHA256: 88d5e6dda62438b2771d0b2c6319eb67902a44e426898d9878ff9764b5d7b009 SHA512: 9158c4752a8f09092c03caa6659d544cac82c4e7380bafec6eda2c6bd423f80dee037aa52212ad97d3f5e6063f4bddff373be625da11d27c6003357c7433f18f Homepage: https://cran.r-project.org/package=glinternet Description: CRAN Package 'glinternet' (Learning Interactions via Hierarchical Group-LassoRegularization) Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015) . Package: r-cran-glinvci Architecture: arm64 Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1029 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_arm64.deb Size: 668356 MD5sum: 1bd7e72a43137277b64e5f5109d32e29 SHA1: 930637029b6d08838a32617bdd2b76550f91a506 SHA256: 3fafe754290fb3e9bfe405c513d81a1c39d589643c983597b0009a605b0ed34e SHA512: 1135d1fbb1c52d5742f57e22e0723b90fe69786a6f037cbd7717c488c3c1be315424ad1bfabca7e8b9c2c40e56fb17d668ef45bae4525bb9f68dce9dd929d249 Homepage: https://cran.r-project.org/package=glinvci Description: CRAN Package 'glinvci' (Phylogenetic Comparative Methods with Uncertainty Estimates) A framework for analytically computing the asymptotic confidence intervals and maximum-likelihood estimates of a class of continuous-time Gaussian branching processes defined by Mitov V, Bartoszek K, Asimomitis G, Stadler T (2019) . The class of model includes the widely used Ornstein-Uhlenbeck and Brownian motion branching processes. The framework is designed to be flexible enough so that the users can easily specify their own sub-models, or re-parameterizations, and obtain the maximum-likelihood estimates and confidence intervals of their own custom models. Package: r-cran-gllm Architecture: arm64 Version: 0.38-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-gllm_0.38-1.ca2604.1_arm64.deb Size: 79562 MD5sum: 58367c5c7bd5385b9390ec7033b4ee18 SHA1: 50e0c26858e3bd444a09ae28e4e2edede74d0dd7 SHA256: 0e9646a52ceb4a324230dc76a9fee201061cfd235a63f28ec5c55f1cc86fd760 SHA512: cfede7a5059ac558de3a3941bdfdb604efb186219f8c5be3341a0828a1019e00862764d2d8d87d16fd872c8303e2e563b873e4d150849e40f462dcf1524aeaa5 Homepage: https://cran.r-project.org/package=gllm Description: CRAN Package 'gllm' (Generalised log-Linear Model) Routines for log-linear models of incomplete contingency tables, including some latent class models, via EM and Fisher scoring approaches. Allows bootstrapping. See Espeland and Hui (1987) for general approach. Package: r-cran-gllvm Architecture: arm64 Version: 2.0.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8861 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_arm64.deb Size: 3766564 MD5sum: a70d537da1d045d017582d46b1870590 SHA1: 0be9137c8d5f133decd701bea6707a95afc22a49 SHA256: 9689fc9fdba84738cf09dfc85a07e81ffd8abda2b38678ed043d9ec6fac88d1c SHA512: cc99eed386a41ab7841a597550cbc39635af5cc0b869d23ce37facf8d93f85d703e02f6fbf55e2c1d365298c2502b4c1574bf7f981338afa2e2a69a9350bd14f Homepage: https://cran.r-project.org/package=gllvm Description: CRAN Package 'gllvm' (Generalized Linear Latent Variable Models) Analysis of multivariate data using generalized linear latent variable models (gllvm). Estimation is performed using either the Laplace method, variational approximations, or extended variational approximations, implemented via TMB (Kristensen et al. (2016), ). Package: r-cran-glm.deploy Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 76912 MD5sum: bea297345c8aead9a2addf12a1942fe3 SHA1: f346c1f36d3451c6058b48d52e7b3ac5ece96f76 SHA256: c1eecc79ac57b15edfdff937e907ff3465fec8521ebbacfffbf9c9fcdd7da919 SHA512: 632d504bee06ac2833fcce458d01f462dcbbe9ba79276dba364e4ef2e57711cb06c4d34acd91dd93119dd4e5cf1fca42fa53a16105d440527ab149e437834bdd Homepage: https://cran.r-project.org/package=glm.deploy Description: CRAN Package 'glm.deploy' ('C' and 'Java' Source Code Generator for Fitted Glm Objects) Provides two functions that generate source code implementing the predict function of fitted glm objects. In this version, code can be generated for either 'C' or 'Java'. The idea is to provide a tool for the easy and fast deployment of glm predictive models into production. The source code generated by this package implements two function/methods. One of such functions implements the equivalent to predict(type="response"), while the second implements predict(type="link"). Source code is written to disk as a .c or .java file in the specified path. In the case of c, an .h file is also generated. Package: r-cran-glmaspu Architecture: arm64 Version: 1.0-1.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), 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_arm64.deb Size: 91384 MD5sum: eb498f751e43ad84d1ab28db29bbee57 SHA1: 32b2d15ac5f44c3e895ec9d470677a7283ca1295 SHA256: f106ba1873b5b904dda82711e452ed1e374049a37ed6469f4b40319ac1271cbe SHA512: 6193c203f4e166e1ab1ac8c7355bb5cf2eda426b0c4dbaa2030b56029bb7e99bc4bfe701ab9e623dfc855b71f412530706244c520d31b0501432a3c56ddb48e7 Homepage: https://cran.r-project.org/package=GLMaSPU Description: CRAN Package 'GLMaSPU' (An Adaptive Test on High Dimensional Parameters in GeneralizedLinear Models) Several tests for high dimensional generalized linear models have been proposed recently. In this package, we implemented a new test called adaptive sum of powered score (aSPU) for high dimensional generalized linear models, which is often more powerful than the existing methods in a wide scenarios. We also implemented permutation based version of several existing methods for research purpose. We recommend users use the aSPU test for their real testing problem. You can learn more about the tests implemented in the package via the following papers: 1. Pan, W., Kim, J., Zhang, Y., Shen, X. and Wei, P. (2014) A powerful and adaptive association test for rare variants, Genetics, 197(4). 2. Guo, B., and Chen, S. X. (2016) . Tests for high dimensional generalized linear models. Journal of the Royal Statistical Society: Series B. 3. Goeman, J. J., Van Houwelingen, H. C., and Finos, L. (2011) . Testing against a high-dimensional alternative in the generalized linear model: asymptotic type I error control. Biometrika, 98(2). Package: r-cran-glmbayes Architecture: arm64 Version: 0.9.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6411 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_arm64.deb Size: 2841262 MD5sum: 3ba976cc9bacade3fcdc3c6f3c1f9e86 SHA1: 13ddb6c8b0ee747ae7bd5a8d228b30f3fca54e10 SHA256: 307595729f32d2bd05bee1bc2a99bcd174e94a22ea01c62bd5fb274a747ebec1 SHA512: 81a2954eb18b2ffe9519b1780c4561d75a8c42578cc6448e3564a3736075cb749e269907d8dbbad1a499f0f94471cf8e327f971fde5ae5076d7eb1665525cceb Homepage: https://cran.r-project.org/package=glmbayes Description: CRAN Package 'glmbayes' (Bayesian Generalized Linear Models (IID Samples)) Provides Bayesian linear and generalized linear model fitting with independent and identically distributed (iid) posterior samples. The main functions mirror R's lm() and glm() interfaces while adding prior family specifications for Gaussian, Poisson, binomial, and Gamma models with log-concave likelihoods. Sampling for supported non-conjugate models uses accept-reject methods based on likelihood subgradients as in Nygren and Nygren (2006) . The package also includes tools for prior setup, posterior summaries, prediction, diagnostics, simulation, vignettes, and optional 'OpenCL' acceleration for larger models. Package: r-cran-glmcat Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1881 Depends: libc6 (>= 2.32), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 1087286 MD5sum: fe999bdcf97637282705ca2c9de5136e SHA1: 0116b08be412402f410e2bde974a1ff5f30ca8d4 SHA256: b872533950ac6aa9221cd00ab62425ecc2b4d2a95374c0b48b26ed566608982a SHA512: 5112accfcabfb222b5970001b24120ed581effd203880de674b79f8be69366535ed83c9f929063c38ce7d63bfe57c0b2053757c8e19ae67784cdd7e56993e51b Homepage: https://cran.r-project.org/package=GLMcat Description: CRAN Package 'GLMcat' (Generalized Linear Models for Categorical Responses) In statistical modeling, there is a wide variety of regression models for categorical dependent variables (nominal or ordinal data); yet, there is no software embracing all these models together in a uniform and generalized format. Following the methodology proposed by Peyhardi, Trottier, and Guédon (2015) , we introduce 'GLMcat', an R package to estimate generalized linear models implemented under the unified specification (r, F, Z). Where r represents the ratio of probabilities (reference, cumulative, adjacent, or sequential), F the cumulative cdf function for the linkage, and Z, the design matrix. The package accompanies the paper "GLMcat: An R Package for Generalized Linear Models for Categorical Responses" in the Journal of Statistical Software, Volume 114, Issue 9 (see ). Package: r-cran-glmlep Architecture: arm64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 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_arm64.deb Size: 41474 MD5sum: 578da3a5e18204892db30d71d24e0dc0 SHA1: fb3fad5e304ab94d168641f2b544e0f161e316a2 SHA256: 75d576a08d9f4d4b954838d6bc086a5be18773dca061993ef413c73e6d9da4a6 SHA512: fcf61f63f27ef17824789a3a447fe3204f4eeca416a90f321a576995a077b8519e4e547c542164e099f1a6f15e87220cd0d89f5d28163ae8aec25353da029fe9 Homepage: https://cran.r-project.org/package=glmlep Description: CRAN Package 'glmlep' (Fit GLM with LEP-Based Penalized Maximum Likelihood) Efficient algorithms for fitting regularization paths for linear or logistic regression models penalized by LEP. Package: r-cran-glmm Architecture: arm64 Version: 1.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), 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_arm64.deb Size: 370718 MD5sum: a272a3505de5603c42de29e278176e20 SHA1: c62d1342d2553059a7110d47c19c412a74866de7 SHA256: 914d3cc89f40ed2261a099d8a907fef2cee37cb601df8ed81cf2745be587c259 SHA512: ec1777e0588e1b69602cefbcc16a1df1c8528a5f2fc64e8b9db6f54e27a303b0018ca673b6ac07a772653843c818e5d62f2191e74b4661e35ac494b15a3f998b Homepage: https://cran.r-project.org/package=glmm Description: CRAN Package 'glmm' (Generalized Linear Mixed Models via Monte Carlo LikelihoodApproximation) Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information. Package: r-cran-glmmep Architecture: arm64 Version: 1.0-3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 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_arm64.deb Size: 229146 MD5sum: 5fe8bdd686d3bb1df3928ffee4c40c6b SHA1: 344a8cf9b7090ecacbdb1163efa708c7be6a3faf SHA256: 7f0a045530c8b01755f0f2dcbe835b7ea14109e1f5d9271799a64e493cd713b2 SHA512: bdc9cc963a895c67696fc5da4d238095004c8b08bb9827dd811872b473dbc40a3cf7e44b0e7876a0721a31b189c20f529b2b7c99fd400771f116d4cd00cf3a00 Homepage: https://cran.r-project.org/package=glmmEP Description: CRAN Package 'glmmEP' (Generalized Linear Mixed Model Analysis via ExpectationPropagation) Approximate frequentist inference for generalized linear mixed model analysis with expectation propagation used to circumvent the need for multivariate integration. In this version, the random effects can be any reasonable dimension. However, only probit mixed models with one level of nesting are supported. The methodology is described in Hall, Johnstone, Ormerod, Wand and Yu (2018) . Package: r-cran-glmmfields Architecture: arm64 Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2750 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 1125352 MD5sum: 5fe12cd0ef39a99de0b8a42115e4908e SHA1: 7af74af71523e96377975f104f9f87cf5c48b4cb SHA256: bccb362202078ea60f5fb7573df9a3bd3cbcfcceca5ac4f3fdbf1ae584425cd9 SHA512: 742c16d289d0893c26a55900bc32d4f1e6dc80da5a99598355709bd090379741c7443683f8531868bbbf4255f6fc875790988ca2bb5b96edd33367d520fb4863 Homepage: https://cran.r-project.org/package=glmmfields Description: CRAN Package 'glmmfields' (Generalized Linear Mixed Models with Robust Random Fields forSpatiotemporal Modeling) Implements Bayesian spatial and spatiotemporal models that optionally allow for extreme spatial deviations through time. 'glmmfields' uses a predictive process approach with random fields implemented through a multivariate-t distribution instead of the usual multivariate normal. Sampling is conducted with 'Stan'. References: Anderson and Ward (2019) . Package: r-cran-glmmlasso Architecture: arm64 Version: 1.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 748 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_arm64.deb Size: 539016 MD5sum: cbbd28a3eec0ddd26fe048ba73219f14 SHA1: 884a043bf90f4e9dd5dad36c780dcfa0e5de9003 SHA256: 4d8bef43cf7d1dc50cc9fa99f4608cd5ac9223f3fc3a6565fe5c0eb4f3088bff SHA512: 8e665ec9b0070a0d939a61d3049b6677f54e665910bf76964a6db510fa7a593b89d4ae00c511f822bf343788ca04239b1fe84b77964aab72219cca4aa6b3a7c8 Homepage: https://cran.r-project.org/package=glmmLasso Description: CRAN Package 'glmmLasso' (Variable Selection for Generalized Linear Mixed Models byL1-Penalized Estimation) A variable selection approach for generalized linear mixed models by L1-penalized estimation is provided, see Groll and Tutz (2014) . See also Groll and Tutz (2017) for discrete survival models including heterogeneity. Package: r-cran-glmmml Architecture: arm64 Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 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_arm64.deb Size: 255702 MD5sum: 3a2158751d023c401ba2d41ff89ab70c SHA1: be58ce1cb24f61ce1bbf71441bbe23da2d5f50fd SHA256: f2157824be564a22cccc60e6cf8a70ce30b0c8cac35a71e2c08e0b55ae0757d9 SHA512: bf413099c9ec0fcfe2f144d9c6161f06918027ff203ec105047c0621c0b60e7c086d7689ff7eeec44523a8e215d2d4919883226c367e2d28a3ec2eca89070679 Homepage: https://cran.r-project.org/package=glmmML Description: CRAN Package 'glmmML' (Generalized Linear Models with Clustering) Binomial and Poisson regression for clustered data, fixed and random effects with bootstrapping. Package: r-cran-glmmpen Architecture: arm64 Version: 1.5.4.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3860 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_arm64.deb Size: 1597520 MD5sum: a70befae09f5abef96bba9fbbf2cfa20 SHA1: a4116270f6a26292a6aebf18f758dd1b6ff7a0ef SHA256: f71b714016f1424dc24f022658464d7938845bff299bc5ffd4dbf6f8a1a0b02e SHA512: 4ee87e2b938743b0a1dd74b0efe48d8293adec9a9b59419a2f8ca904528ae4db706ae6d53e87cebf2837b364ab355513c58cd975ed66cc8241a12e37c7cdaad5 Homepage: https://cran.r-project.org/package=glmmPen Description: CRAN Package 'glmmPen' (High Dimensional Penalized Generalized Linear Mixed Models(pGLMM)) Fits high dimensional penalized generalized linear mixed models using the Monte Carlo Expectation Conditional Minimization (MCECM) algorithm. The purpose of the package is to perform variable selection on both the fixed and random effects simultaneously for generalized linear mixed models. The package supports fitting of Binomial, Gaussian, and Poisson data with canonical links, and supports penalization using the MCP, SCAD, or LASSO penalties. The MCECM algorithm is described in Rashid et al. (2020) . The techniques used in the minimization portion of the procedure (the M-step) are derived from the procedures of the 'ncvreg' package (Breheny and Huang (2011) ) and 'grpreg' package (Breheny and Huang (2015) ), with appropriate modifications to account for the estimation and penalization of the random effects. The 'ncvreg' and 'grpreg' packages also describe the MCP, SCAD, and LASSO penalties. Package: r-cran-glmmrbase Architecture: arm64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4036 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 1374296 MD5sum: 514c500cdcffa0546a5ad77b969ac1f0 SHA1: 1fe1b4869cb3dd4179c108dd68be7865b07bf2fd SHA256: 4abf34f46e6520d78ec11c7c07a44411d1e738f1d59875eb44911226e529bfcd SHA512: 6ae0bac50b2e88f746a204e80591c287b2ed372c3321439d506364f0388f39eee7fb60f5056d706a24c2ae2f39c801271e1835a6c1d3de236b179b2f325b316b Homepage: https://cran.r-project.org/package=glmmrBase Description: CRAN Package 'glmmrBase' (Generalised Linear Mixed Models in R) Specification, analysis, simulation, and fitting of generalised linear mixed models. Includes Markov Chain Monte Carlo Maximum likelihood model fitting for a range of models, non-linear fixed effect specifications, a wide range of flexible covariance functions that can be combined arbitrarily, robust and bias-corrected standard error estimation, power calculation, data simulation, and more. Package: r-cran-glmmroptim Architecture: arm64 Version: 0.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1012 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 418632 MD5sum: 3d72dd273689d69ac0b347ee8a265106 SHA1: 8ba409789b824eff240e15a6e92ea0ace477d6b0 SHA256: 08f31757ba5d01b99999352ff1fde1330c5249cb8c236dfff3b47400d1db0782 SHA512: 8c5481b9b43d01257495919ec9210c992512c955046f28219ae924c40e4ee8ee807949b423083c7c6f642b308150baf12c62b204d5a51edb03793721c5e55297 Homepage: https://cran.r-project.org/package=glmmrOptim Description: CRAN Package 'glmmrOptim' (Approximate Optimal Experimental Designs Using GeneralisedLinear Mixed Models) Optimal design analysis algorithms for any study design that can be represented or modelled as a generalised linear mixed model including cluster randomised trials, cohort studies, spatial and temporal epidemiological studies, and split-plot designs. See for a detailed manual on model specification. A detailed discussion of the methods in this package can be found in Watson, Hemming, and Girling (2023) . Package: r-cran-glmmsel Architecture: arm64 Version: 1.0.3-1.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), 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_arm64.deb Size: 162718 MD5sum: 0b36e48a80f0ac0e8b15a2580d95a9f0 SHA1: 4d2cc1dd4ddc50bd26ea17c13c9c16fc1946b294 SHA256: 9286b14507db6257fcfa489864ffd8cfefcfcec11c9ed816f00e1cde5e83e696 SHA512: 8b93f16c69ad77f4debd3c1312921247ed718f64301faea3428f5eab2e3a009992889da7aae59030f4fc20f8f358ec7800d48c9ef1625d54f6fdc75c801c2ad0 Homepage: https://cran.r-project.org/package=glmmsel Description: CRAN Package 'glmmsel' (Generalised Linear Mixed Model Selection) Provides tools for fitting sparse generalised linear mixed models with l0 regularisation. Selects fixed and random effects under the hierarchy constraint that fixed effects must precede random effects. Uses coordinate descent and local search algorithms to rapidly deliver near-optimal estimates. Gaussian and binomial response families are currently supported. For more details see Thompson, Wand, and Wang (2025) . Package: r-cran-glmmtmb Architecture: arm64 Version: 1.1.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11008 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_arm64.deb Size: 6273952 MD5sum: 77eb8ac5a1557ba44d15034057241c35 SHA1: 2ac86a69dc5cc0a53dbf3b4550f85b15ef19c3fd SHA256: b61065958fed0bfe7d48a4cba0d41ffe2f6251e2b6fc67cead3943338f979dc9 SHA512: 5079587e3723fe5ff6ffcd712a937910eedcd970f16386938c4b0a99a33d61bbf9d677eb868f520e6de781edcf3333621c43344a54d30f0f748f619339b5676e Homepage: https://cran.r-project.org/package=glmmTMB Description: CRAN Package 'glmmTMB' (Generalized Linear Mixed Models using Template Model Builder) Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via 'TMB' (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation. 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There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family (). This comes with a modest computational cost, so when the built-in families suffice, they should be used instead. The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers cited. Package: r-cran-glmpath Architecture: arm64 Version: 0.98-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-glmpath_0.98-1.ca2604.1_arm64.deb Size: 196930 MD5sum: d07159a99c8b9e5fae225f7ec205543a SHA1: c08761a0f3bfcf439f9132af1dc26742b7f89a6e SHA256: 645c35645bf727ae7e7bc803b34fdb8b446b45ce540a50d1e49dd6d56b9dd2e5 SHA512: 79ffbda4be5a25bcf0c3bb77c527fc779345d32361b62bc3a1aa98a9da5811e06ccd9581def1028d256136a27349196f758102df90f52b9719e971b1eedbac6a Homepage: https://cran.r-project.org/package=glmpath Description: CRAN Package 'glmpath' (L1 Regularization Path for Generalized Linear Models and CoxProportional Hazards Model) A path-following algorithm for L1 regularized generalized linear models and Cox proportional hazards model. 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This version is a completely new version compared with our previous version, which was mainly based on R. New core algorithms are developed and are now written in C++ and highly optimized. 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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) . 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Package: r-cran-golden Architecture: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1044 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, r-cran-sciviews, r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-golden_0.0.1-1.ca2604.1_arm64.deb Size: 612936 MD5sum: 457e115c1b2771a9709587a2ab7e7d6d SHA1: 0e2f731282148cc7c7396390a0b3a57b68042247 SHA256: a47ea27ed8dd6f7e3915f510225cf0c68e522772593f87d9f51bd853f2098fb7 SHA512: 975a31da3f441ff3a4426ed6f62bd036df8bd35532cdf0e1928f01e40cafe6803d44317a513e02930f9b478bdd39f0ff39333781a8a46abc569f5051c9024213 Homepage: https://cran.r-project.org/package=golden Description: CRAN Package 'golden' (Framework for Patient-Level Microsimulation of Risk FactorTrajectories & Hazard-Based Events) Fast, flexible, patient-level microsimulation. 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Package: r-cran-goldilocks Architecture: arm64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 604 Depends: libc6 (>= 2.17), libgcc-s1 (>= 4.5), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-dplyr, r-cran-pbmcapply, r-cran-pweall, r-cran-rcpp, r-cran-rlang, r-cran-bh Suggests: r-cran-covr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-goldilocks_0.4.0-1.ca2604.1_arm64.deb Size: 366674 MD5sum: f9293bb7c355458d5d8f291fdf09a6f2 SHA1: f85ac644b95a8ead648e97d520813b386b60c461 SHA256: 637872329004cf34082591f25287dcb1a635dcb2cb2482e13f4a47ea87843ca9 SHA512: 19fa279557b6d34504e6ff82beff8a1b3a6cfe7e55b67f7b549df2533840de1f2f2ed1de09c72cc99695861fb4a6803c1640d95147a115ec3976e6fca43a0d11 Homepage: https://cran.r-project.org/package=goldilocks Description: CRAN Package 'goldilocks' (Goldilocks Adaptive Trial Designs for Time-to-Event Endpoints) Implements the Goldilocks adaptive trial design for a time to event outcome using a piecewise exponential model and conjugate Gamma prior distributions. The method closely follows the article by Broglio and colleagues , which allows users to explore the operating characteristics of different trial designs. Package: r-cran-googlepolylines Architecture: arm64 Version: 0.8.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 40797 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 3072768 MD5sum: dcd76a0792fd480be0e297399e0c2c60 SHA1: e4267c73837be09b379a842806dee87c46e8997b SHA256: 36d32fd7816dfc382ce4f0b49aacaadca829af512fae2eb66ffcac2b59ad7ee8 SHA512: 274c4cfc1a540c810e1a8c3914ff6163d8fd95016b651b80272a2d25b3886acc9cac8f40340f04c55a63bd06a25dbc0f7dc1f7c24ed5f51b6338c97e2a029f06 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 (). 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Package: r-cran-gower Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 206676 MD5sum: 23b797a631f3cf1da803fa5da2b49f02 SHA1: a537f8184b1d7d14a2a3c860a81bd5d3d59ed4d7 SHA256: ca72b10fe74131ced70a14062e1355b824578ce22be82ff5199fd208a80c4a13 SHA512: 0b5f7705262d9560e9a04f03e4d5ade4f364a01331a3eadf8f1d04ae23023614a7e8bc4245cd5146bdabd297c7ea21122b09f4e7e818d7fc597c272f6b581a1d Homepage: https://cran.r-project.org/package=gower Description: CRAN Package 'gower' (Gower's Distance) Compute Gower's distance (or similarity) coefficient between records. Compute the top-n matches between records. Core algorithms are executed in parallel on systems supporting OpenMP. Package: r-cran-gowersom Architecture: arm64 Version: 0.1.0-1.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_arm64.deb Size: 40422 MD5sum: ab179a13c0477c50b72e2eddf5427b65 SHA1: 20dc57663d5be650596ee52c23a5fc454f377864 SHA256: 287a09623f4a8391b4cf4e4ee6adeacc9edc09d2f562e3c7fcf55c017a08d000 SHA512: f068857b18f67776504a16c3bdeb2078c9e7bad68bfc1a0e7781f17b9b8120710af7fec8c4a9b4d4644c219337150f33773be9685c34787d3b8d2510d82a1090 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. 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Package: r-cran-gpareto Architecture: arm64 Version: 1.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1566 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_arm64.deb Size: 1300024 MD5sum: fc8ab7d0516014a951de1afb43f3333f SHA1: 87ea297cf8740dcf449b628c3bacf2795096d395 SHA256: 9d6a66f176152127f69b766acfc8df3f42677a0f77d5371cc0ac594d6048d604 SHA512: 9582a5fc8fff36ba92f0fdbc38ee08c34c4ccf7b75e91bca3e441056481a3ad674b90611d384035ec80e901a431e4be58ed07248d09f9bd7b7ba4336a1b7c312 Homepage: https://cran.r-project.org/package=GPareto Description: CRAN Package 'GPareto' (Gaussian Processes for Pareto Front Estimation and Optimization) Gaussian process regression models, a.k.a. Kriging models, are applied to global multi-objective optimization of black-box functions. Multi-objective Expected Improvement and Step-wise Uncertainty Reduction sequential infill criteria are available. A quantification of uncertainty on Pareto fronts is provided using conditional simulations. Package: r-cran-gpbayes Architecture: arm64 Version: 0.1.0-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1540 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_arm64.deb Size: 725928 MD5sum: 4ac8e4811d865b383db689a21b7ed87e SHA1: cdd2293778569d8b01f72bf250537a4ac152a001 SHA256: 873aede4c328642c0f8ab0db8006dca499503c871a539d5c327b85c113eae036 SHA512: 728e959119deb83e4a39bc59796731ef3f93a56a29e4b74584127a4be7bbf4acb5720eee6662e5548be772d7b028d649a2db826f92c18aeb0217c5002d1bf89c 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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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: arm64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 207996 MD5sum: 5a4f518472988abfb60f463dbdaf6795 SHA1: d4da52ac078562140f8efe4451f7cb1c5d200267 SHA256: ac51177bf1758a0bb3b70c9f4bf31ca3ca743c451f5c9c2895e6c940805bd822 SHA512: 1835d6ed5e895f06bc75f72208bed70b9ffa0357e06246bd996c60c3bf330fdd93fe0b25b21dc801fc9f61d503ac8cab2e0b4219984d077458e32f03cdc7348e Homepage: https://cran.r-project.org/package=GPGame Description: CRAN Package 'GPGame' (Solving Complex Game Problems using Gaussian Processes) Sequential strategies for finding a game equilibrium are proposed in a black-box setting (expensive pay-off evaluations, no derivatives). 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Package: r-cran-gpgp Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2409 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1888538 MD5sum: 410ad068c10108196ab6debb7697b6de SHA1: f2b06cea142fa2c04a2250af0dbfcc45476d7750 SHA256: 4a916e84ec1ffbb75d3fc3f27356f1fbeb57c3f132726cf6d632a206ea389e1e SHA512: 1877283abb5f2206c6b140de4fdf5852976a39f57a4343783423e3e3153e1b1c8415ee408898eab83c0bee7b252bcc7d7fc40922dfc4a851e345062468001a4f 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: arm64 Version: 0.13.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3583 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_arm64.deb Size: 2185024 MD5sum: 9da12cea06c30ea4bcb0f7e1d026541e SHA1: 3a3dc190b7d04ce514e743d5cd081c6a507c272b SHA256: 2f3f4fe7b5408bc84297e66e84bfd0f9d11feb15f1677cd50ecb42e732af35cb SHA512: cdb64515ca33a93ab0ee7f5c9b532cd7f53097d4c93aa3e9cdc63381a3d411588dbbe86019c3c206c1897c6bfe38d1f9ac3fffc5e04aa27e24da8cc4c56d5e0b Homepage: https://cran.r-project.org/package=gplite Description: CRAN Package 'gplite' (General Purpose Gaussian Process Modelling) Implements the most common Gaussian process (GP) models using Laplace and expectation propagation (EP) approximations, maximum marginal likelihood (or posterior) inference for the hyperparameters, and sparse approximations for larger datasets. 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Package: r-cran-gppenalty Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 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_arm64.deb Size: 153888 MD5sum: d7a0ada96fc32bc57ae3b3a9094920b9 SHA1: 0439ebce9b9acfdc81bb4f05f870422772776c7f SHA256: a3f36848ca2f3468bf849191e0ccd8fb0edd8878b046619afccec030728c84b9 SHA512: 1d42b6e56f89abdb63077b998611c3b87d7ac35f72dc9c9a1fd1fac554e4af81602d73aa1d7982b74806a824f9b8cdce3e965ae4c3a86dd315a95360f5c8efe9 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. 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Package: r-cran-gps Architecture: arm64 Version: 1.2-1.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 (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-gps_1.2-1.ca2604.1_arm64.deb Size: 207254 MD5sum: 6f2d5c3d843a5722501655ef165074ad SHA1: b7d7ef626cabb7960b11dd651f8635f42fd9f4ec SHA256: cc720d4132b85a3750e85d2c5d4afbba0e692979588378757cce78a4f4b1549b SHA512: 6ae389199955714f422f16870db75b4521b1d9a7e4207f3a3b46b5c500637e1082c80762c427dacc1b65ea2617245e78a70c46c88e4c8dd5b2a53fee2490a261 Homepage: https://cran.r-project.org/package=gps Description: CRAN Package 'gps' (General P-Splines) General P-splines are non-uniform B-splines penalized by a general difference penalty, proposed by Li and Cao (2022) . Constructible on arbitrary knots, they extend the standard P-splines of Eilers and Marx (1996) . 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Package: r-cran-grainscape Architecture: arm64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2997 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_arm64.deb Size: 1623240 MD5sum: cb61f3be7939dc6ba88d61511b27ace5 SHA1: 4c8d1c1308d3cd536f19c3c6ac9f90bb70a6ef83 SHA256: d8d978ed9c965cb82b02bb77902dbe1a9668fd99d8a623d55003d3128a07fcbd SHA512: ee41f1ab7d8d7b45a06d09ab1090ee4ecc3c44c150768b5002992277840e46ac72911f2a149e03a04ede07bda3f89ffeb92485ae9c39748987227b7cfe0c7af9 Homepage: https://cran.r-project.org/package=grainscape Description: CRAN Package 'grainscape' (Landscape Connectivity, Habitat, and Protected Area Networks) Given a landscape resistance surface, creates minimum planar graph (Fall et al. (2007) ) and grains of connectivity (Galpern et al. (2012) ) models that can be used to calculate effective distances for landscape connectivity at multiple scales. Documentation is provided by several vignettes, and a paper (Chubaty, Galpern & Doctolero (2020) ). Package: r-cran-grandr Architecture: arm64 Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1654 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-rlang, r-cran-ggplot2, r-cran-patchwork, r-cran-rcurl, r-cran-plyr, r-cran-reshape2, r-cran-mass, r-cran-scales, r-cran-cowplot, r-cran-minpack.lm, r-cran-lfc, r-cran-labeling, r-cran-numderiv Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-circlize, r-cran-seurat, r-bioc-complexheatmap, r-cran-ggrepel, r-bioc-deseq2, r-bioc-s4vectors, r-cran-data.table, r-bioc-clusterprofiler, r-bioc-biomart, r-cran-msigdbr, r-bioc-fgsea, r-cran-rclipboard, r-cran-cubature, r-cran-dt, r-cran-shinyjs, r-cran-shinyjqui, r-cran-rcolorbrewer, r-cran-gsl, r-cran-htmltools, r-cran-matrixstats, r-cran-vgam, r-cran-quantreg, r-cran-shiny, r-cran-ggrastr, r-cran-viridislite, r-cran-desolve Filename: pool/dists/resolute/main/r-cran-grandr_0.2.7-1.ca2604.1_arm64.deb Size: 1487712 MD5sum: 41bbf99a4acd3f210bb573e05b15f819 SHA1: 572b697e114f13510ea5a708e129cd3d233cbb0a SHA256: 9e1f7f222a184aa37c946f78185b0d9e7ed6262cda8765c36d5bf91483b815bf SHA512: 3d94241c73d89d78306daf5eecb8799305497f2ab3260653a1f5aba4e741c9de8d9e5554310476664e6aa7ad257bc5efa61a18863658c42b7916de788a874504 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. Such experiments require specialized tools for primary processing such as GRAND-SLAM, (see 'Jürges et al' ) and specialized tools for downstream analyses. 'grandR' provides a comprehensive toolbox for quality control, kinetic modeling, differential gene expression analysis and visualization of such data. Fast Wilcoxon tests are supported via the 'presto' package (available at ). Package: r-cran-graphicalevidence Architecture: arm64 Version: 1.1-1.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-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_arm64.deb Size: 190530 MD5sum: 2dbe325b873216b4a0666b6874547176 SHA1: 95c487540c4c618867a419c0b63b7dfbe42767ba SHA256: ca5e41b306902f4217e57ef38ca46729cc37237a018b76600546f193b9e80cc4 SHA512: c999b5a3fc99bc900ecae9718898c541156b06b66386a702f9bfbc20145e08fead4f9763206a96207219d42129384baa64f75b1c9b23b08e72ca5499b11f0548 Homepage: https://cran.r-project.org/package=graphicalEvidence Description: CRAN Package 'graphicalEvidence' (Graphical Evidence) Computes marginal likelihood in Gaussian graphical models through a novel telescoping block decomposition of the precision matrix which allows estimation of model evidence. The top level function used to estimate marginal likelihood is called evidence(), which expects the prior name, data, and relevant prior specific parameters. This package also provides an MCMC prior sampler using the same underlying approach, implemented in prior_sampling(), which expects a prior name and prior specific parameters. Both functions also expect the number of burn-in iterations and the number of sampling iterations for the underlying MCMC sampler. Package: r-cran-graphicalvar Architecture: arm64 Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 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-glasso, r-cran-glmnet, r-cran-mvtnorm, r-cran-qgraph, r-cran-dplyr, r-cran-igraph, r-cran-rlang, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-graphicalvar_0.3.4-1.ca2604.1_arm64.deb Size: 139886 MD5sum: 8d2652a91ce236fc599c84adf47324bc SHA1: a72b5ef045ca3828f0d68d3266a0103f79d530c4 SHA256: d4b92bf9431b527ae56b8bf6de2becc06ef9db6273d7fd57fa3b13c5a5749893 SHA512: 9fd4775adab1df4983e4bfc14565fbff8f507047dda9ca832d2cf66e2ba7efc0f1f329d1a0e22a97a723ab824c4d1e518c2eb7a0814d453d8ac2d1c4ed771d83 Homepage: https://cran.r-project.org/package=graphicalVAR Description: CRAN Package 'graphicalVAR' (Graphical VAR for Experience Sampling Data) Estimates within and between time point interactions in experience sampling data, using the Graphical vector autoregression model in combination with regularization. See also Epskamp, Waldorp, Mottus & Borsboom (2018) . Package: r-cran-graphlayouts Architecture: arm64 Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2798 Depends: libc6 (>= 2.17), 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-rcpparmadillo Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-uwot, r-cran-oaqc Filename: pool/dists/resolute/main/r-cran-graphlayouts_1.2.3-1.ca2604.1_arm64.deb Size: 2353158 MD5sum: 298fe342b845ca57d7ec9ba97f4bada8 SHA1: 5ef6f04bbc82b017b1d9ff290e8074ecd412caa0 SHA256: 2d4f5d7f4dfab2a4fcecd963b84a9f3b782575d657136b8231847e6797720ab8 SHA512: ec6d29196fd8ec52036d54486e5f91e5b97f66e6fddc0bda7fdbbe9842f5c9e5c8ca711317bbd086d9d4f14e40b30b242d3d04d2def86932d553fd82ee3e777f Homepage: https://cran.r-project.org/package=graphlayouts Description: CRAN Package 'graphlayouts' (Additional Layout Algorithms for Network Visualizations) Several new layout algorithms to visualize networks are provided which are not part of 'igraph'. 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Package: r-cran-graphpcor Architecture: arm64 Version: 0.1.25-1.ca2604.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1372 Depends: r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-inlatools, r-cran-numderiv, r-cran-igraph Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-graphpcor_0.1.25-1.ca2604.2_arm64.deb Size: 697718 MD5sum: e8270a95f7e7c950b4e0c190dfe1bff8 SHA1: d930cb66fa8f6b937b45d14ac0e144d4dc866863 SHA256: 8e054aa50efce46346b9ebd2fc285f5d6c6a0442931a3842babb2d0e85746a06 SHA512: 256f5604a8478bdce481d66e601949e5ee9d214fb77f7a7f743483d99120107d4065261659cd26f3c368ed491d40730ab276b6f1280da030dee9adcdefbbd290 Homepage: https://cran.r-project.org/package=graphpcor Description: CRAN Package 'graphpcor' (Models for Correlation Matrices Based on Graphs) Implement some models for correlation/covariance matrices including two approaches to model correlation matrices from a graphical structure. 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Package: r-cran-graphql Architecture: arm64 Version: 1.5.3-1.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-jsonlite Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-graphql_1.5.3-1.ca2604.1_arm64.deb Size: 76816 MD5sum: 99110af60999f8eb5e55d6d3eccc2b60 SHA1: eca0eef71f8f64d5a215ab3f635935997a63b804 SHA256: dd93bc8a45ccd58cd228b53840c4847b8d0c0aa1aea1131f0d7b1a814da5d2c0 SHA512: fa8a4420b7af217006a7a62f4aaa7c6f25f54ceb85bad7a1fe32c109e0c686dd33c46205e975232a53b1be402189a228d540c59e5aa31b41d9f32c384dfe1af0 Homepage: https://cran.r-project.org/package=graphql Description: CRAN Package 'graphql' (A GraphQL Query Parser) Bindings to the 'libgraphqlparser' C++ library. Parses GraphQL syntax and exports the AST in JSON format. Package: r-cran-grasps Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2677 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-igraph, r-cran-ggforce, r-cran-ggplot2, r-cran-rcpp, r-cran-rdpack, r-cran-scales, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-mass, r-cran-quarto, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-grasps_0.1.1-1.ca2604.1_arm64.deb Size: 1605960 MD5sum: 0e44be03193e999791467177bcf2d769 SHA1: 12fbc8272f5a1fb58d2f40cb80da0f66b5ee8e8c SHA256: 1f04462720d070f9d7e6f0c21460cc32eace282d5440613e499f4d7a0c83030f SHA512: 3b01185a7fb7b15ea81c5ffee3058c71b778d06fb83004f22d1fa0cea074792306b70f408917ebe566fa0d5be429205eeb169af59ba7bbae3cec0087b001b460 Homepage: https://cran.r-project.org/package=grasps Description: CRAN Package 'grasps' (Groupwise Regularized Adaptive Sparse Precision Solution) Provides a unified framework for sparse-group regularization and precision matrix estimation in Gaussian graphical models. 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Package: r-cran-grattan Architecture: arm64 Version: 2026.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1117 Depends: libc6 (>= 2.17), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-data.table, r-cran-grattaninflators, r-cran-hutils, r-cran-hutilscpp, r-cran-ineq, r-cran-fastmatch, r-cran-forecast, r-cran-fy, r-cran-assertthat, r-cran-magrittr Suggests: r-cran-curl, r-cran-fst, r-cran-knitr, r-cran-rlang, r-cran-rmarkdown, r-cran-survey, r-cran-testthat, r-cran-tibble, r-cran-yaml, r-cran-withr, r-cran-covr Filename: pool/dists/resolute/main/r-cran-grattan_2026.1.1-1.ca2604.1_arm64.deb Size: 708070 MD5sum: 91b4e78c284dd22cc05ac501f782626b SHA1: 7e49ee69031419d843d252a5f3d88385e519d55b SHA256: bcee8d3fa2c81f18de32f4d95095016ddd50ef871ed7f548c5f01e97c81a7438 SHA512: b3eb048511001c7bc573b842da283295994d05656053d7d51c79c4795274265e9d320d796372acc709d4863835d593916bc8c0e2730cf5e51ad339032873385d Homepage: https://cran.r-project.org/package=grattan Description: CRAN Package 'grattan' (Australian Tax Policy Analysis) Utilities to cost and evaluate Australian tax policy, including fast projections of personal income tax collections, high-performance tax and transfer calculators, and an interface to common indices from the Australian Bureau of Statistics. 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Package: r-cran-grattaninflators Architecture: arm64 Version: 0.5.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: libc6 (>= 2.17), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-fy, r-cran-hutils Suggests: r-cran-distributional, r-cran-fable, r-cran-fabletools, r-cran-tinytest, r-cran-withr Filename: pool/dists/resolute/main/r-cran-grattaninflators_0.5.7-1.ca2604.1_arm64.deb Size: 112932 MD5sum: 2ac957175f4b06191142cf5ccce0113c SHA1: 4fcb60171e146f86072fb8cf48fa1974c2d38512 SHA256: cd4640ae0679193e7d8ccb0ab883bbed43b13ea179ef216b64d081ad21b0ff2b SHA512: b4355f3cc4569d6e475544fe22a67b6d475d1c5e5073f4c4501e20c19f4cff1ad42cf8ba4a54c8e3a4dd579696ae66af34ffe09b0b6b17026be6714165451aed Homepage: https://cran.r-project.org/package=grattanInflators Description: CRAN Package 'grattanInflators' (Inflators for Australian Policy Analysis) Using Australian Bureau of Statistics indices, provides functions that convert historical, nominal statistics to real, contemporary values without worrying about date input quality, performance, or the ABS catalogue. Package: r-cran-gravmagsubs Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 659 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-knitr, r-cran-rmarkdown, r-cran-fields, r-cran-ggplot2, r-cran-gridextra, r-cran-scales, r-cran-scatterplot3d Filename: pool/dists/resolute/main/r-cran-gravmagsubs_1.0.1-1.ca2604.1_arm64.deb Size: 311306 MD5sum: 5256afe12c7cd60d5cb70bb834694bb2 SHA1: b3cbef18dbfc32675bb720f3d43460293d3e5586 SHA256: 725b2f848a01476159024915dfcdbf0f650e46f270efd7c6d8ea470b84fd94f9 SHA512: 0523e79bfbc71a5c00964396bf7783b7b4d8dd4069bbf0dda89a8e439493f0b80eb1ea3c464582515b1db463aceb8fb3b7e3aeea8de18055192511e14be1aa8a Homepage: https://cran.r-project.org/package=gravmagsubs Description: CRAN Package 'gravmagsubs' (Gravitational and Magnetic Attraction of 3-D VerticalRectangular Prisms) Computes the gravitational and magnetic anomalies generated by 3-D vertical rectangular prisms at specific observation points using the method of Plouff (1976) . Package: r-cran-grbase Architecture: arm64 Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6141 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 5108844 MD5sum: 3f0623768b6ef16b60eb505c7e5ecb9f SHA1: 763abfc9fb8c04b6666d340049be53d33cb057d5 SHA256: 687a5b389802e48c95785c2c9b7624b5d069ef024993c1371ff8734e4edac57d SHA512: c03cafd3722bc00feca57f15008267e2a201a92254b106ba99ccbac9a62a7552eb5fb9635129b4c2448cd10c4b4025b69ebc8c51bad6a1baf12b61331c839ab0 Homepage: https://cran.r-project.org/package=gRbase Description: CRAN Package 'gRbase' (A Package for Graphical Modelling in R) The 'gRbase' package provides graphical modelling features used by e.g. the packages 'gRain', 'gRim' and 'gRc'. 'gRbase' implements graph algorithms including (i) maximum cardinality search (for marked and unmarked graphs). (ii) moralization, (iii) triangulation, (iv) creation of junction tree. 'gRbase' facilitates array operations, 'gRbase' implements functions for testing for conditional independence. 'gRbase' illustrates how hierarchical log-linear models may be implemented and describes concept of graphical meta data. The facilities of the package are documented in the book by Højsgaard, Edwards and Lauritzen (2012, ) and in the paper by Dethlefsen and Højsgaard, (2005, ). Please see 'citation("gRbase")' for citation details. Package: r-cran-grc Architecture: arm64 Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 231808 MD5sum: 28d9ba5922d958420abe9f26101f0f7d SHA1: 6c0698940e4298726db075236366e3b08c72eda1 SHA256: d313653d809d7d17d7214ed72e8aaec58c77135c9b46f1cae176c2d200e51284 SHA512: 9e10ee35d4a2c2869508d91b18e31d65d500b0b4d9308acb1efec7a4369f64f667de970cb82256582354eff24e4928154e6f7e9bcddbe22260f3aad1bd2dbad1 Homepage: https://cran.r-project.org/package=gRc Description: CRAN Package 'gRc' (Inference in Graphical Gaussian Models with Edge and VertexSymmetries) Estimation, model selection and other aspects of statistical inference in Graphical Gaussian models with edge and vertex symmetries (Graphical Gaussian models with colours). Documentation about 'gRc' is provided in the paper by Hojsgaard and Lauritzen (2007, ) and the paper by Hojsgaard and Lauritzen (2008, ). Package: r-cran-greed Architecture: arm64 Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3515 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 2474814 MD5sum: 031659cd6de0e0e549adff5fe335b6e8 SHA1: 5fee5fd1f941d5e7c5deaaebb6d295040da815b7 SHA256: add59308260b4668ea38cace02a0c75a1d01e62a0cb5bb21f76663252ff6a215 SHA512: c457127888b4384367c233d1a02f6bd89e9a286bb5036cf174e77f3f7079d08aa610d658a859f081ef63707d2715cbd8348d5b983ea813688ac510e4d3760a18 Homepage: https://cran.r-project.org/package=greed Description: CRAN Package 'greed' (Clustering and Model Selection with the IntegratedClassification Likelihood) An ensemble of algorithms that enable the clustering of networks and data matrices (such as counts, categorical or continuous) with different type of generative models. Model selection and clustering is performed in combination by optimizing the Integrated Classification Likelihood (which is equivalent to minimizing the description length). Several models are available such as: Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of Multinomial, Latent Block Model. The optimization is performed thanks to a combination of greedy local search and a genetic algorithm (see for more details). Package: r-cran-greedyepl Architecture: arm64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 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_arm64.deb Size: 65386 MD5sum: 8398996609677b3e0f11bbf4a1072de4 SHA1: 7527117702930e905708a671c094f6e0646a3fae SHA256: e94277ce93bc991e0df66a08475606ab5706f6e5eac9616bcacdcd1fc50b8f29 SHA512: c48e6859f349fe20de7f6f4c323e9473a52b58d626dea683d47574c9ba7da584c8cc60eb14aedb37d8c4d6b8c05b2709d7673117f42373bc92fcdcf2a50cd008 Homepage: https://cran.r-project.org/package=GreedyEPL Description: CRAN Package 'GreedyEPL' (Greedy Expected Posterior Loss) Summarises a collection of partitions into a single optimal partition. The objective function is the expected posterior loss, and the minimisation is performed through a greedy algorithm described in Rastelli, R. and Friel, N. (2017) "Optimal Bayesian estimators for latent variable cluster models" . Package: r-cran-greedyexperimentaldesign Architecture: arm64 Version: 1.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 777 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_arm64.deb Size: 482446 MD5sum: f3c6ccf34605ebc987608fc7fd68c555 SHA1: f7487f9dc18737fb3a7ff355a5608fc688bbd520 SHA256: b4eabf015b7f44f294b079db8a13518a63d1e0797da7488c1300bc2f7f7243c7 SHA512: f5b3acd74433d9f60f7cf3fd2c4f5e5e52fa2e9a5032daf817a3db85c60b7503bcc2e25f7b63c309db436d692824a29bf4d7dd844b1a8dee390b14c35a167f48 Homepage: https://cran.r-project.org/package=GreedyExperimentalDesign Description: CRAN Package 'GreedyExperimentalDesign' (Greedy Experimental Design Construction) Computes experimental designs for two-arm experiments with covariates using multiple methods, including: (0) complete randomization and randomization with forced-balance; (1) greedy optimization of a balance objective function via pairwise switching; (2) numerical optimization via 'gurobi'; (3) rerandomization; (4) Karp's method for one covariate; (5) exhaustive enumeration for small sample sizes; (6) binary pair matching using 'nbpMatching'; (7) binary pair matching plus method (1) to further optimize balance; (8) binary pair matching plus method (3) to further optimize balance; (9) Hadamard designs; and (10) simultaneous multiple kernels. For the greedy, rerandomization, and related methods, three objective functions are supported: Mahalanobis distance, standardized sums of absolute differences, and kernel distances via the 'kernlab' library. This package is the result of a stream of research that can be found in Krieger, A. M., Azriel, D. A., and Kapelner, A. (2019). "Nearly Random Designs with Greatly Improved Balance." Biometrika 106(3), 695-701 . Krieger, A. M., Azriel, D. A., and Kapelner, A. (2023). "Better experimental design by hybridizing binary matching with imbalance optimization." Canadian Journal of Statistics, 51(1), 275-292 . Package: r-cran-greeks Architecture: arm64 Version: 1.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 580 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_arm64.deb Size: 309436 MD5sum: 5b35c975bbe0b3f7f47b7c7e7f2fa10f SHA1: 928e9d19ed36eb51bd2017a2374e5d4ef5036929 SHA256: bc84b4692eb659b73d3bb261c650f24ca49abf0e9aaf160c3011877880e993bb SHA512: 0d71cd2e8b2cee4c8b31f6d819a05e30ae49251f49193342caa8d9d1a6ccf060e4550d37c14935492e63aaf430938c5228a02e61b9f8f5f170ce07fd6215b427 Homepage: https://cran.r-project.org/package=greeks Description: CRAN Package 'greeks' (Sensitivities of Prices of Financial Options and ImpliedVolatilities) Methods to calculate sensitivities of financial option prices for European, geometric and arithmetic Asian, and American options, with various payoff functions in the Black Scholes model, and in more general jump diffusion models. A shiny app to interactively plot the results is included. Furthermore, methods to compute implied volatilities are provided for a wide range of option types and custom payoff functions. Classical formulas are implemented for European options in the Black Scholes Model, as is presented in Hull, J. C. (2017), Options, Futures, and Other Derivatives. In the case of Asian options, Malliavin Monte Carlo Greeks are implemented, see Hudde, A. & Rüschendorf, L. (2023). European and Asian Greeks for exponential Lévy processes. . For American options, the Binomial Tree Method is implemented, as is presented in Hull, J. C. (2017). Package: r-cran-greencrab.toolkit Architecture: arm64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3070 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_arm64.deb Size: 684738 MD5sum: abf4b1272ef8f2ac7534cb4ad7c1bc57 SHA1: 9b7896b1c812e113a8882de42aed9643c65f88ac SHA256: 05490b5543a019858d3e27b48611a56207a15aa9d2298cf7127cc95797dfc987 SHA512: 0bbc467a1237f1de9724b3706a960819662cf326f85db5daac45bf2cfc9762efe321ceb7a03106cc7696d48751fc9e956fca86f82b7bda78c5c25b5e3f06b2eb Homepage: https://cran.r-project.org/package=greencrab.toolkit Description: CRAN Package 'greencrab.toolkit' (Run 'Stan' Models to Interpret Green Crab Monitoring Assessments) These Bayesian models written in the 'Stan' probabilistic language can be used to interpret green crab trapping and environmental DNA monitoring data, either independently or jointly. Detailed model information is found in Keller (2022) . Package: r-cran-gremlin Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 431 Depends: libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-nlme Filename: pool/dists/resolute/main/r-cran-gremlin_1.1.0-1.ca2604.1_arm64.deb Size: 272652 MD5sum: ee27919f2ffa2b8f7a2c414db98f9b5b SHA1: 940cca9894b94f37651d483a5429518b5dfaa584 SHA256: c928dad963fd8abb7604cbcf6ae0d53d40ed7c5b71a7dcb4dca65cfacbb69565 SHA512: d26e65258c4bf90afe79e1d5373ad773f0e23a4b532bda74d81e3d44f1786ddd06d2c9c7383418e52dc76fb201d9b8f5e34865c97a866d7f918260e8f3359734 Homepage: https://cran.r-project.org/package=gremlin Description: CRAN Package 'gremlin' (Mixed-Effects REML Incorporating Generalized Inverses) Fit linear mixed-effects models using restricted (or residual) maximum likelihood (REML) and with generalized inverse matrices to specify covariance structures for random effects. In particular, the package is suited to fit quantitative genetic mixed models, often referred to as 'animal models'. Implements the average information algorithm as the main tool to maximize the restricted log-likelihood, but with other algorithms available. Package: r-cran-gretel Architecture: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 99390 MD5sum: ca289eac1aff5455495586ff1ea070fa SHA1: 3d75e78151ac1efe378aea0133bda0b60d7302e3 SHA256: 808863955099176c76aad839a7d5b13f36e78b25a24c1dabf9a279a77bbcf772 SHA512: 8aabebf63a8373d498ca33ccbbade43018d6b2b5450d49cadf022c6412e4d9bd67e2a323b932204e4bdd505dba203b6f17b86ff67fab00668768fcd8a2030d68 Homepage: https://cran.r-project.org/package=gretel Description: CRAN Package 'gretel' (Generalized Path Analysis for Social Networks) The social network literature features numerous methods for assigning value to paths as a function of their ties. 'gretel' systemizes these approaches, casting them as instances of a generalized path value function indexed by a penalty parameter. The package also calculates probabilistic path value and identifies optimal paths in either value framework. Finally, proximity matrices can be generated in these frameworks that capture high-order connections overlooked in primitive adjacency sociomatrices. Novel methods are described in Buch (2019) . More traditional methods are also implemented, as described in Yang, Knoke (2001) . Package: r-cran-greybox Architecture: arm64 Version: 2.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4691 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-generics, r-cran-pracma, r-cran-nloptr, r-cran-statmod, r-cran-zoo, r-cran-texreg, r-cran-xtable, r-cran-rcpp Suggests: r-cran-smooth, r-cran-domc, r-cran-doparallel, r-cran-foreach, r-cran-testthat, r-cran-rmarkdown, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-greybox_2.0.8-1.ca2604.1_arm64.deb Size: 3212342 MD5sum: d24e2d44949a505e4561352f6d17c30c SHA1: 25058f93e7a41c3fa982f18a48693f1085afa389 SHA256: c594189d148b82ab7c6de0645c872533f878534826d705d0f7b9a3345e7ba11c SHA512: 1cb4f6698a468a8bba97304d4fd88e8537d0d4c730cf8c7018b5b737c55ee6b95318b220be0ff65e95b8bface4a5c87d8e725be0732ab5677eb508428520735e Homepage: https://cran.r-project.org/package=greybox Description: CRAN Package 'greybox' (Toolbox for Model Building and Forecasting) Implements functions and instruments for regression model building and its application to forecasting. 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Package: r-cran-grf Architecture: arm64 Version: 2.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1851 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dicekriging, r-cran-lmtest, r-cran-matrix, r-cran-rcpp, r-cran-sandwich, r-cran-rcppeigen Suggests: r-cran-diagrammer, r-cran-mass, r-cran-policytree, r-cran-rdrobust, r-cran-survival, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-grf_2.6.1-1.ca2604.1_arm64.deb Size: 1159934 MD5sum: 30e9fb4b544ba833fff62655bd7a9c11 SHA1: 78e8b9e1448f1d69e047250f248d3ea46b2d6844 SHA256: 71f33f29eb76d7921333bb93d7663dc92cf416d57ae08c0974c209ce935509e0 SHA512: 0bb7b3a7e1ccfe6959c9c9b78557267d7d1461d6911d4d594a32a691776cc4396ebc2461a80e6676fac350bee53f29bf07999610761e2c61094616ac3367b808 Homepage: https://cran.r-project.org/package=grf Description: CRAN Package 'grf' (Generalized Random Forests) Forest-based statistical estimation and inference. GRF provides non-parametric methods for heterogeneous treatment effects estimation (optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables), as well as least-squares regression, quantile regression, and survival regression, all with support for missing covariates. 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Package: r-cran-gridonclusters Architecture: arm64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3505 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ckmeans.1d.dp, r-cran-cluster, r-cran-fossil, r-cran-dqrng, r-cran-mclust, r-cran-rdpack, r-cran-plotrix, r-cran-bh Suggests: r-cran-funchisq, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-gridonclusters_0.3.2-1.ca2604.1_arm64.deb Size: 2263674 MD5sum: f0e923de3bec23841a5392431583a344 SHA1: bdbcf76e155cf6b8581b041dbd0d6914189cb29e SHA256: b38a53b10026efeb20b59d5447a00cf3d41321f9f10e5d21937437329324322b SHA512: eb9c00b16ebded659fb24ce24dfc077dd86cd899e1d8502f317687abac9411616f7dbcd35b9a0075074a2973af68dd151d24dfbafc8a9d36ebefd7d2607df7e3 Homepage: https://cran.r-project.org/package=GridOnClusters Description: CRAN Package 'GridOnClusters' (Multivariate Joint Grid Discretization) Discretize multivariate continuous data using a grid to capture the joint distribution that preserves clusters in original data. It can handle both labeled or unlabeled data. Both published methods (Wang et al 2020) and new methods are included. Joint grid discretization can prepare data for model-free inference of association, function, or causality. Package: r-cran-gridot Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-gridot_1.0.2-1.ca2604.1_arm64.deb Size: 185936 MD5sum: 7f66f5f63ff726ca1072b906f33283e4 SHA1: 06c01c416a4398ba61204ef62de4b8146be4521a SHA256: 8033955c37625f2f5a27790f711bb79c87e05b2f4e7a04442491dd35f422014e SHA512: b11c8dfcb870cf8d61aa46e8b11b61e2e56828e595c5d3b470accc81955d2d05c320476c4660ebe56ed909aee5c3bdf9152659bab910906e23ac0133c584acbb Homepage: https://cran.r-project.org/package=gridOT Description: CRAN Package 'gridOT' (Approximate Optimal Transport Between Two-Dimensional Grids) Can be used for optimal transport between two-dimensional grids with respect to separable cost functions of l^p form. It utilizes the Frank-Wolfe algorithm to approximate so-called pivot measures: One-dimensional transport plans that fully describe the full transport, see G. Auricchio (2023) . For these, it offers methods for visualization and to extract the corresponding transport plans and costs. Additionally, related functions for one-dimensional optimal transport are available. 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Package: r-cran-grim Architecture: arm64 Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4576 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_arm64.deb Size: 1351248 MD5sum: 975bf5b9fec0bb8e81f5f24af5800d20 SHA1: 51db2ac16bbb0d2aaddfa838ac25b2c6d1dfcd2e SHA256: 72192a70bfe8f2056f3f482f69fafeeebf7a4faf90561b6d3bc290b679a02ea8 SHA512: 17d3ebafe0ae05860dfd11e16837179d43e6319fb85d125ab073cddcb10035b45db3606608e50a5f187e243e0961727923f428ffe1bc49af761622c060f5b437 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: arm64 Version: 1.0.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 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_arm64.deb Size: 307874 MD5sum: c47624b279450ec6f4d591c0be18c35a SHA1: b09060e02b7e9b022e098d7b8578264811f9d56a SHA256: 0d3a4a762744c4809942a8eff54d6d6abad8d575aa52922de87c8349c907a8cc SHA512: 3d1c2d6f3e347bc17fd9dbeb9c13a3ef5cf3c702cacda5c0b71ed0a128384ae25bf2fd691b3b706d0ac04747c5da3adaa9a66b008b246ca4fb708195e0edea12 Homepage: https://cran.r-project.org/package=groc Description: CRAN Package 'groc' (Generalized Regression on Orthogonal Components) Robust multiple or multivariate linear regression, nonparametric regression on orthogonal components, classical or robust partial least squares models as described in Bilodeau, Lafaye De Micheaux and Mahdi (2015) . Package: r-cran-groupedsurv Architecture: arm64 Version: 1.0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-doparallel, r-cran-foreach, r-bioc-qvalue, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-knitr, r-cran-snplist, r-cran-bedmatrix Filename: pool/dists/resolute/main/r-cran-groupedsurv_1.0.5.1-1.ca2604.1_arm64.deb Size: 425714 MD5sum: c726cdf889658419a52b3ea201da5e3b SHA1: c9770c48ab0ae8a1273b37209685517c6f3886d2 SHA256: e535410e158b2e556e3a703bd0f3329ebb2be3ef913a1ed8381f7115bae4a164 SHA512: fc51e504041e20a3af8b5b233fea24491626b69be6b986e6086f7ffd8e2acaf43c69d48d4d0240d814dfac7d245f63cb5f3d3e05b80630ff6906e6a944ed569b Homepage: https://cran.r-project.org/package=groupedSurv Description: CRAN Package 'groupedSurv' (Efficient Estimation of Grouped Survival Models Using the ExactLikelihood Function) These 'Rcpp'-based functions compute the efficient score statistics for grouped time-to-event data (Prentice and Gloeckler, 1978), with the optional inclusion of baseline covariates. Functions for estimating the parameter of interest and nuisance parameters, including baseline hazards, using maximum likelihood are also provided. A parallel set of functions allow for the incorporation of family structure of related individuals (e.g., trios). Note that the current implementation of the frailty model (Ripatti and Palmgren, 2000) is sensitive to departures from model assumptions, and should be considered experimental. For these data, the exact proportional-hazards-model-based likelihood is computed by evaluating multiple variable integration. The integration is accomplished using the 'Cuba' library (Hahn, 2005), and the source files are included in this package. The maximization process is carried out using Brent's algorithm, with the C++ code file from John Burkardt and John Denker (Brent, 2002). Package: r-cran-grouprar Architecture: arm64 Version: 0.1.0-1.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_arm64.deb Size: 180996 MD5sum: b3b0b29c56a9ed5cc5c6a07785021c56 SHA1: 2eef859ddff91248633f1431c03ba026d5e1f6b9 SHA256: a3ee2fb21f3f44bb5ef5d6c38fe417472a0aa172538096eb2cc19758a2caa008 SHA512: 92a6a8b0fdc6bf66421a4807b706841b161faf40a8937410026704c66db776aa7d85ab66d51ac1a21ba63ae21e8958abcc9e59bb845f4013503ff591a7ceb00d 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: arm64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 347 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_arm64.deb Size: 147462 MD5sum: e09154f8c7359fdd9df0e2a667168c58 SHA1: c2ed9117bb3d534774e5950fd399b982c700820c SHA256: c47c086228e58190cdb48a77751399b8c90d6249bc7572fa4aa22e359b95655a SHA512: dc4dd245da57cc743faa361827cd917f09cde56dfa6b39c5c34aecba5366c33130d72857d1e937ed45d4b5cd54b2e092a6fd829d8b5b88f043cc4009e882cd50 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. Fore more details see: Ma L. and Soriano J. (2018) Efficient functional ANOVA through wavelet-domain Markov groves. . Package: r-cran-growth Architecture: arm64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 313 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rmutil Filename: pool/dists/resolute/main/r-cran-growth_1.1.2-1.ca2604.1_arm64.deb Size: 202630 MD5sum: 17704b949ea3e7a9022584c2b016ad79 SHA1: 04d88854a3f8b60b1d9fe09c08ff2e310511f8cc SHA256: 913397f9456ffc0d9891f5d2c94b887ca72dd3d33adf53dece6511ede4f0e449 SHA512: bbbfe03f41c3bf72b76d8cd96ae3b7f2a6cb4b348ae8ef905dcc3ac157900f458b69698c9c398e704104d97f9730fbd4da9db15f296115c5078fb4ba9c84b468 Homepage: https://cran.r-project.org/package=growth Description: CRAN Package 'growth' (Multivariate Normal and Elliptically-Contoured RepeatedMeasurements Models) Functions for fitting various normal theory (growth curve) and elliptically-contoured repeated measurements models with ARMA and random effects dependence. Package: r-cran-growthrates Architecture: arm64 Version: 0.8.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 792 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice, r-cran-desolve, r-cran-fme Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-growthrates_0.8.5-1.ca2604.1_arm64.deb Size: 463566 MD5sum: fdaa9d3f5a0e4e7d75b115e68109bfbb SHA1: 8a01fd3c143f2a0e7f635d064a03acca9cf7e2a9 SHA256: c47c4adf814055f2cdfd8660342d16c4314c3a35ce83a9bb15a5ee468a5e96d2 SHA512: 29c86ed3bb1ce76216b43794b8de891542df9b7c13684cbccaf9cd220bc2cbe28ebec0adae9c734c5c0e6a106f5902bcf48a3b88677ead202104ca0d86d253d4 Homepage: https://cran.r-project.org/package=growthrates Description: CRAN Package 'growthrates' (Estimate Growth Rates from Experimental Data) A collection of methods to determine growth rates from experimental data, in particular from batch experiments and plate reader trials. Package: r-cran-grpcox Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 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_arm64.deb Size: 139322 MD5sum: c78db091375af75b1c27a6f079f08141 SHA1: ae0e4347d56101266d862d26db8caa7e62d65253 SHA256: 98ae41ee59716fa68d69d2261299f58af51c5579cd1daace7eddaa6480d263c6 SHA512: 9dbd6192c82aa15a4915afedda6dbfe0dae0233a078755e07a3a98048d77554d1030fa89df03de9e14225cafabb2c8036c5236fd4adb5cd3a04ce44bef2cd450 Homepage: https://cran.r-project.org/package=grpCox Description: CRAN Package 'grpCox' (Penalized Cox Model for High-Dimensional Data with GroupedPredictors) Fit the penalized Cox models with both non-overlapping and overlapping grouped penalties including the group lasso, group smoothly clipped absolute deviation, and group minimax concave penalty. The algorithms combine the MM approach and group-wise descent with some computational tricks including the screening, active set, and warm-start. Different tuning regularization parameter methods are provided. Package: r-cran-grpnet Architecture: arm64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 798 Depends: libc6 (>= 2.38), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-grpnet_1.2-1.ca2604.1_arm64.deb Size: 605798 MD5sum: a0a9eb9e0fc790eafd43145c6c5734f5 SHA1: 61f9fd30f9e42b6283d76741e4a852ea80f2a47b SHA256: 7bc8c2562243b757c5a1cf8357161244bb4372b8752ea1c020a128f5e6e75699 SHA512: 624c98304902715235303ae871d45691ef0adea76c0c3d0ce27fcb896fa573ad9d9a676eddf71ba3e283eaf63df86d39567f323a3e9a4df01cfb889ef7adfbe0 Homepage: https://cran.r-project.org/package=grpnet Description: CRAN Package 'grpnet' (Group Elastic Net Regularized GLMs and GAMs) Efficient algorithms for fitting generalized linear and additive models with group elastic net penalties as described in Helwig (2025) . Implements group LASSO, group MCP, and group SCAD with an optional group ridge penalty. 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. Package: r-cran-grpreg Architecture: arm64 Version: 3.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 538 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-grpreg_3.6.0-1.ca2604.1_arm64.deb Size: 361306 MD5sum: 3da1756c09870d06eb10d0bf78925321 SHA1: 35682efc308df0cfadade52ebccd49717ade3ed5 SHA256: 5fb7f3370106c439002e54b6ee0d38cded960ff4fa2a6945e7f7e08a333e26bd SHA512: 12557ca0cb1816c3022aa76f14d66d17e563cdad90ebf287498ecc24ebf15c234c177e257c6d30dd09b79738044408f6e92390196a4ca1ef4e3d669f6af3b81d Homepage: https://cran.r-project.org/package=grpreg Description: CRAN Package 'grpreg' (Regularization Paths for Regression Models with GroupedCovariates) Efficient algorithms for fitting the regularization path of linear regression, GLM, and Cox regression models with grouped penalties. 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 . Package: r-cran-grpsel Architecture: arm64 Version: 1.3.2-1.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 (>= 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 Filename: pool/dists/resolute/main/r-cran-grpsel_1.3.2-1.ca2604.1_arm64.deb Size: 250264 MD5sum: 2c757ff35c3eaec6dacdd2cdc43e5d53 SHA1: e7293522dbfaa2fc24c13f923c14d6d334b4b2f8 SHA256: e2c35a256b3f2dbd729dcf88b2e482e3ffc5da3e3f9703b864f01304875de9f4 SHA512: 0dac1268a95358db2a4daddaeccc103fab3fc89aec4003ad9dbc0ca469daf7240f869389bad7085cb5c115221bd38a7db10335d4b6c632ae26f0f3b711481cf4 Homepage: https://cran.r-project.org/package=grpsel Description: CRAN Package 'grpsel' (Group Subset Selection) Provides tools for sparse regression modelling with grouped predictors using the group subset selection penalty. Uses coordinate descent and local search algorithms to rapidly deliver near optimal estimates. The group subset penalty can be combined with a group lasso or ridge penalty for added shrinkage. Linear and logistic regression are supported, as are overlapping groups. Package: r-cran-grpslope Architecture: arm64 Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 127392 MD5sum: 99b149925b807bf77ccc19d42ab66d04 SHA1: 533605808be9f7eaa61854df58abd95c7396513a SHA256: d8084dd5efffb4e77083ce320e84fab44ed1b0e7c85e230c21b2fb0ef8af534b SHA512: cf6b61e309887be660c79b2121cce92c5e813f639312d0f87df2f3cab6b4f4a79a97c7b42d1b27080a72b1f7fdc9b29a353a299f904aafc76401331cc947c71b 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) . Package: r-cran-grumpy Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-grumpy_0.1.1-1.ca2604.1_arm64.deb Size: 58602 MD5sum: f335296e18b1d426661955e1b93569be SHA1: 02a4e50778916b44bdf2bf1a248f11e959e9588f SHA256: b6ea8473d1594ebfe8679db3bccb6a033bff6405db15003c3c99d4e157559a76 SHA512: ff65354a28d1683192cb36d385750a0e552621667d81c5d1daadec4140499375364699bb6c72b8ab48ba9ab1d190491617ac44f7e4d1661e26c7c69ab47512fa Homepage: https://cran.r-project.org/package=grumpy Description: CRAN Package 'grumpy' (Read 'NumPy' '.npy' and '.npz' Files) Lightweight way to read 'NumPy' '.npy' and '.npz' files in R. 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Package: r-cran-gtfsrouter Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5114 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-data.table, r-cran-fs, r-cran-geodist, r-cran-rcpp Suggests: r-cran-digest, r-cran-dodgr, r-cran-dplyr, r-cran-ggplot2, r-cran-here, r-cran-hms, r-cran-knitr, r-cran-lubridate, r-cran-lwgeom, r-cran-markdown, r-cran-pbapply, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-gtfsrouter_0.1.4-1.ca2604.1_arm64.deb Size: 1349884 MD5sum: 36746caa5a781cc3c6cdfe393e57e45e SHA1: f422a1a7fca22705d5cc9b52863c88bf7d698758 SHA256: fd4fcff08f52f867302dc9532a78214a71a66be16aea245f216db0ba87f0eb56 SHA512: 73a826988a9b2e710e3cd67717595d36a922dee5df4eabb61b1ac6e76ab5e6f296e978d45ecaba959aa340e04614dbf1d9a0ee585a8397c39231ddc7a4704d7e Homepage: https://cran.r-project.org/package=gtfsrouter Description: CRAN Package 'gtfsrouter' (Routing with 'GTFS' (General Transit Feed Specification) Data) Use 'GTFS' (General Transit Feed Specification) data for routing from nominated start and end stations, for extracting 'isochrones', and travel times from any nominated start station to all other stations. Package: r-cran-gtfstools Architecture: arm64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1630 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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_arm64.deb Size: 1287838 MD5sum: 38b6c6ce2402fda04123acef19a5ce6b SHA1: d74c4bf1f95a110ed3c1361b49e30b1009693227 SHA256: 09006724615117544b9b6636ce9cf7a0f5706610ba270ef420dc27bd35b8e50c SHA512: a0c2d57f9c1cd3b8bb5dc9f19baecb8aac76570d78f32ff4429cc10340e9f67019205e4e05454572a87359724732eb1cd997f98f2fa972ab1c9644c612b1d2fd Homepage: https://cran.r-project.org/package=gtfstools Description: CRAN Package 'gtfstools' (General Transit Feed Specification (GTFS) Editing and AnalysingTools) Utility functions to read, manipulate, analyse and write transit feeds in the General Transit Feed Specification (GTFS) data format. Package: r-cran-gtools Architecture: arm64 Version: 3.9.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 356062 MD5sum: c89ff77909620fb8e0508e865327fe0d SHA1: 55eb5848945f5f449a6053cf9ce14af84efce2ca SHA256: 2d88387c6ef3b82674fb037626f4d247d7c00391a839be8289881f561203395c SHA512: bf0389244d86b8315d3262616167bd8a643200c1e106b1cf4e3b30b4ea63881fa0b083814b45e81e4e98be4c8e83e96d1144a7370e2fe4e4666019b37f527ba1 Homepage: https://cran.r-project.org/package=gtools Description: CRAN Package 'gtools' (Various R Programming Tools) Functions to assist in R programming, including: - assist in developing, updating, and maintaining R and R packages ('ask', 'checkRVersion', 'getDependencies', 'keywords', 'scat'), - calculate the logit and inverse logit transformations ('logit', 'inv.logit'), - test if a value is missing, empty or contains only NA and NULL values ('invalid'), - manipulate R's .Last function ('addLast'), - define macros ('defmacro'), - detect odd and even integers ('odd', 'even'), - convert strings containing non-ASCII characters (like single quotes) to plain ASCII ('ASCIIfy'), - perform a binary search ('binsearch'), - sort strings containing both numeric and character components ('mixedsort'), - create a factor variable from the quantiles of a continuous variable ('quantcut'), - enumerate permutations and combinations ('combinations', 'permutation'), - calculate and convert between fold-change and log-ratio ('foldchange', 'logratio2foldchange', 'foldchange2logratio'), - calculate probabilities and generate random numbers from Dirichlet distributions ('rdirichlet', 'ddirichlet'), - apply a function over adjacent subsets of a vector ('running'), - modify the TCP_NODELAY ('de-Nagle') flag for socket objects, - efficient 'rbind' of data frames, even if the column names don't match ('smartbind'), - generate significance stars from p-values ('stars.pval'), - convert characters to/from ASCII codes ('asc', 'chr'), - convert character vector to ASCII representation ('ASCIIfy'), - apply title capitalization rules to a character vector ('capwords'). Package: r-cran-gud Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3259 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_arm64.deb Size: 1180048 MD5sum: cb2ad1c3730fa225190f231238908e7a SHA1: c419d02cce07b42b050713d8cd0109ccdfdd3f4d SHA256: bb6ad6036650292e7d11ec6389c9cd1442934be295943cdbff3c23aa2c7000cd SHA512: 6b5d591f68f15931acc39c5c0c10743a54931a836f10932df95f75bbca506b23dd74781061ae179e41e06e856216b5fbd10d53465f5665bf14f250ca150115ce 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: arm64 Version: 1.4.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 394 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_arm64.deb Size: 186806 MD5sum: 1ae59e9284445740e4ecbd1657ee7082 SHA1: edc52fff1ee518202754f2bed5e41a42b025da69 SHA256: 71b863cdc00b5827420318d4a79d92397136db9b24dc85233623dc083ce58040 SHA512: 70f91ed14de92f3d550480414983d0924f113ae22fd4d9e9f1ec875abb167412ad3e105906e30a7d1401463ec463365871add5bffd3f53c331d6c0334d431811 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: arm64 Version: 1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1576 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_arm64.deb Size: 1007848 MD5sum: b090fdc936e3baff9e48202348dc7046 SHA1: e7f60cef4170565f629b6c2c9462daf8605a2a8c SHA256: c7cac3a478924d6c3a8309819bfd0b39b990962ac8c31f9911e7c44323bbc90a SHA512: 2689e4c81f905280a676827f9a6b2653bc691305e7be6a78783a59608170a07c0f99951431e2f6b38c3cf0e51a6999276ba94500551aa9779e6178fce2a319c5 Homepage: https://cran.r-project.org/package=GUniFrac Description: CRAN Package 'GUniFrac' (Generalized UniFrac Distances, Distance-Based MultivariateMethods and Feature-Based Univariate Methods for MicrobiomeData Analysis) A suite of methods for powerful and robust microbiome data analysis including data normalization, data simulation, community-level association testing and differential abundance analysis. 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Package: r-cran-guts Architecture: arm64 Version: 1.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3365 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-knitr, r-cran-rmarkdown, r-cran-adaptmcmc, r-cran-xlsx, r-cran-drc, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-guts_1.2.6-1.ca2604.1_arm64.deb Size: 2884100 MD5sum: 1816b73e2c075ef7f02e0a219c6f3697 SHA1: f5b2f0d0d0ae6cb8790668c090036a3c7c16c91d SHA256: 1131953ccefed824d161e9b1da14b8a2498080c52d0af378a33f5d215abd638f SHA512: f414fb6abf2ee51c4cf6ec57c912f73cedba0cfd7862b43bd4c685468d0fb685bb4af9e9509468595cfde8d74948fbc4a46b822a7f2f8efd3b0e5d718204fa51 Homepage: https://cran.r-project.org/package=GUTS Description: CRAN Package 'GUTS' (Fast Calculation of the Likelihood of a Stochastic SurvivalModel) Given exposure and survival time series as well as parameter values, GUTS allows for the fast calculation of the survival probabilities as well as the logarithm of the corresponding likelihood (see Albert, C., Vogel, S. and Ashauer, R. (2016) ). Package: r-cran-gwasexacthw Architecture: arm64 Version: 1.2-1.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 Filename: pool/dists/resolute/main/r-cran-gwasexacthw_1.2-1.ca2604.1_arm64.deb Size: 15040 MD5sum: 83ccf882ed66b58e1ee8ecc27eab10b4 SHA1: 194311899420717e165242f26222fab75e9525f0 SHA256: de9bc37a6a0e719301af14d4af13c4c69ef45f4aebf7b4588d0eb51ef1f287ac SHA512: 03e75854b6b24edb1cb3299ca70ba585a1ff67d0a4fc6fdef01955a9ec039e35a97c07170442d63d2c9e8c1fdb54f30f391a01670cd6d28a182abaf76e6a7f65 Homepage: https://cran.r-project.org/package=GWASExactHW Description: CRAN Package 'GWASExactHW' (Exact Hardy-Weinburg Testing for Genome Wide Association Studies) Exact Hardy-Weinburg testing (using Fisher's test) for SNP genotypes as typically obtained in a Genome Wide Association Study (GWAS). Package: r-cran-gwasinlps Architecture: arm64 Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 107208 MD5sum: e6792f2b6887651e2a4ff7297b95740e SHA1: d1ea70b30c6c3ef55f34d8bb948e9c32ce26dd29 SHA256: 86f722dd25a0d9b2438c92c18ef9f9fbb061df40d9581fdbaf13433794250766 SHA512: 9a125b342cad17cd8b93c3276975a16656e51ad48470e5fdfd4f62f03c0e4a8b096c4c9d9eb9cd2c6a50e8a6d97b0ba98c0403402af700f699752d00e8e7fba5 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: arm64 Version: 2.4-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2916 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 2508822 MD5sum: 647dadee9d51f5cbeba42e0c0e7f3a36 SHA1: 4a4ebe1f665f7627f87345591103564e81469013 SHA256: 6b139f90a5a37b62ce9c37015722dee957636a0df8510983bdbfee942d478e46 SHA512: f224a06a3ef7587301ac9670b7a4b55afc398bc3816eda2409d3a6241dd5033a9a817e99e72c1c2fbfb04f27399fe41a9d5d38c409710c775d22e3b80ca06509 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: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 108118 MD5sum: 7eb25d6033bc60f5e82a7e0b9a3721f3 SHA1: 17dbda1492b9330c24cd84f8379b5acbf1242c63 SHA256: 2749a57229e4b40f827546a3d139bd83eef5f546cabccf118e5e8696afa44d32 SHA512: 87201a05aa3bd59a9b7b3853cb1bfe70ae484e380002359dc65fba70eec82f6f7fe74047b5a92f9a537c8746758b9a100d8c9c42dfa032434951faa1cd51531b 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: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1909 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_arm64.deb Size: 1736116 MD5sum: 8a005794cdb9d92cc7e7a35cad1ff4bc SHA1: a2f9155a6993e9471d04a16756b7e78f36a994ff SHA256: cf13603d4e9fb746bde791ba1304bc63dcfef3e8bb16915593b22d97d425f49d SHA512: 03a58f03330975cd0f981d74d58096f62033a61e9a1d5868732bab07aabd6829db60eaa35d7d83ef40c44b36c12017eb4ca3aa73d74b90b472692dc3bec14049 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: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 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_arm64.deb Size: 59440 MD5sum: 541365c0440f13df7f87df1975a8ee84 SHA1: 03b18b064427a928f93c51076e610e7645dab9bf SHA256: 02a33b95411dbe737f2309102562d4f20e99231e38dcf76afeabfb8f446a6655 SHA512: cae72ca7be122abbe6d55d616fbbeed8f23363c072d6b038460f794a2dd4937603713e9bb619b875633154e9d4432e4c854d603e75786e7e3a51399273a10eb0 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: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1134 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_arm64.deb Size: 508156 MD5sum: f7b86ab79e4eff29bdce0d7a0582cfa9 SHA1: 870a472b71801c2626a24855435f0404cb5a9394 SHA256: 4a4c80e6010f64bc23e69efb523e0e20cb23c68b431913a3edce2ec2e9dd5651 SHA512: 84f8c7b22493cbd8a303117fe6691746bcbbd36715d6a7f3671fe132deae1c71d4cf461bb8ee537a692c0088bd33f39fabb543497d8204f1443e32b66bbb3b0c 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: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 126920 MD5sum: d09d149b1550196e57e0faf5cf177646 SHA1: 62eabf6dc9a95f6034448b5c6e1cdb00d57be37c SHA256: 448d32edd093660010c7e57b34396a991069b3b0f69f50bce44411b53f7325f7 SHA512: 3efaefb598d69982277671b7e5fb8abaf4a885206121923c7a14c167f1463b06145ce70046e8dab3bcd28511cc515a330207118c13727ea270e6c93bfb171cb2 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: arm64 Version: 2.1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6830 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_arm64.deb Size: 2331256 MD5sum: bd5cb9735e811b160ab3422d4628f8fc SHA1: e56c6ba133ec3ee20a1384bb766d9810c6be758d SHA256: 13f150f49646ec427459036b441717ebaf0ee022252255557184161da220e440 SHA512: 82f6a21461a58319111cda6f7d137d679e44e86d0ba37f37b521e3a3a94caaacfd0cfc4a8058881a1f7870f025f0fb79f71138de481cade1971af6e875137f42 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: arm64 Version: 1.0.7-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 102440 MD5sum: bd08c10828c53a147cadfa1247cc28ac SHA1: 290f744f6d4e87166eff35c157a4e5eda9c6e931 SHA256: 4b39a31053142d18cf37a8ccf0280a31dc43330e526e089ab2ca937efbc54bdc SHA512: 59fdde605dcbc4c21a2af7d43f10546a5c7126db1752608409826816c51fcbfd37f7c99fdee7d7bf2656caa74d45d7c21c8ec00263ed4364337924f3a50111ee Homepage: https://cran.r-project.org/package=HACSim Description: CRAN Package 'HACSim' (Iterative Extrapolation of Species' Haplotype AccumulationCurves for Genetic Diversity Assessment) Performs iterative extrapolation of species' haplotype accumulation curves using a nonparametric stochastic (Monte Carlo) optimization method for assessment of specimen sampling completeness based on the approach of Phillips et al. (2015) , Phillips et al. (2019) and Phillips et al. (2020) . 'HACSim' outputs a number of useful summary statistics of sampling coverage ("Measures of Sampling Closeness"), including an estimate of the likely required sample size (along with desired level confidence intervals) necessary to recover a given number/proportion of observed unique species' haplotypes. Any genomic marker can be targeted to assess likely required specimen sample sizes for genetic diversity assessment. The method is particularly well-suited to assess sampling sufficiency for DNA barcoding initiatives. Users can also simulate their own DNA sequences according to various models of nucleotide substitution. A Shiny app is also available. Package: r-cran-hahmmr Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3538 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_arm64.deb Size: 3343984 MD5sum: 20149f97e01e9e2fdb4e546603e83254 SHA1: 80280d79c3f8ce5a4bf218668fe0f7ada05ced00 SHA256: 7852be18c6a5306344837ebba59f7c980a87a8c1f09d3f7ac75fa892258e7e59 SHA512: ed3275b7085a2bbb8d2aa9bddaa6e016db318afcbb38a6d3e6aeef17d9080864650c93827b347fb6fdd35e57672a4c97bb1ab3225fc14d4b3631bd1abdc14b83 Homepage: https://cran.r-project.org/package=hahmmr Description: CRAN Package 'hahmmr' (Haplotype-Aware Hidden Markov Model for RNA) Haplotype-aware Hidden Markov Model for RNA (HaHMMR) is a method for detecting copy number variations (CNVs) from bulk RNA-seq data. Additional examples, documentations, and details on the method are available at . Package: r-cran-hal9001 Architecture: arm64 Version: 0.4.6-1.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-matrix, r-cran-assertthat, r-cran-origami, r-cran-glmnet, r-cran-data.table, r-cran-stringr, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark, r-cran-future, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-survival, r-cran-superlearner Filename: pool/dists/resolute/main/r-cran-hal9001_0.4.6-1.ca2604.1_arm64.deb Size: 308654 MD5sum: e963c04d502992bbd129693872ac25a1 SHA1: 3bed85fb366aec6d160ab1eacb1b443fc2525843 SHA256: 9568775071af974e6e2dd07c5957af357cb28a89da6f5a1d37ef49ffb023ad1e SHA512: d72b4a7be5aaef23bb8571822aabba2c052ffaf3ec6c63525715d9f05ad5e5317bb6fd20fa7195a6e82fc0b94d4569fd3c686755bfebefea1502de8a96234729 Homepage: https://cran.r-project.org/package=hal9001 Description: CRAN Package 'hal9001' (The Scalable Highly Adaptive Lasso) A scalable implementation of the highly adaptive lasso algorithm, including routines for constructing sparse matrices of basis functions of the observed data, as well as a custom implementation of Lasso regression tailored to enhance efficiency when the matrix of predictors is composed exclusively of indicator functions. For ease of use and increased flexibility, the Lasso fitting routines invoke code from the 'glmnet' package by default. The highly adaptive lasso was first formulated and described by MJ van der Laan (2017) , with practical demonstrations of its performance given by Benkeser and van der Laan (2016) . This implementation of the highly adaptive lasso algorithm was described by Hejazi, Coyle, and van der Laan (2020) . Package: r-cran-handwriter Architecture: arm64 Version: 3.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2749 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1853096 MD5sum: 9056f87893f7f275548eaa179b25f337 SHA1: 38a60bcf69eff309e137bdc26b5636ca3d369dce SHA256: 54ebb6e5911e6da5fd4c987c434260ee841c474112ffa092b9ba1ee0a7eed67c SHA512: b10662c3e509635527ca7f15cf474d92c085486091f5b504698a12c4f277d43c9c3a09cb6dfa0e7e927398a20cd0ff84b6ed5effca0e35ad33a95ad0d2dff9bc Homepage: https://cran.r-project.org/package=handwriter Description: CRAN Package 'handwriter' (Handwriting Analysis in R) Perform statistical writership analysis of scanned handwritten documents. Webpage provided at: . Package: r-cran-hann Architecture: arm64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 256 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-lattice Filename: pool/dists/resolute/main/r-cran-hann_1.2-1.ca2604.1_arm64.deb Size: 154418 MD5sum: b8171c4d04c255914824e94d1bc75444 SHA1: 80175552f9555901292454219fd67f3720d669b6 SHA256: 48b6369b1266e528770857bdb02bca098682e84bf1c52ea322c93a8b566840c3 SHA512: 07e800afdee5b3e9eef815da70df8225d94036be0c914467a0628a594849167369e78dab293568cb438f4fe1aefc8d32e87a5d4723454f1d9a1e6db0f6c54f22 Homepage: https://cran.r-project.org/package=hann Description: CRAN Package 'hann' (Hopfield Artificial Neural Networks) Builds and optimizes Hopfield artificial neural networks (Hopfield, 1982, ). One-layer and three-layer models are implemented. The energy of the Hopfield network is minimized with formula from Krotov and Hopfield (2016, ). Optimization (supervised learning) is done through a gradient-based method. Classification is done with S3 methods predict(). Parallelization with 'OpenMP' is used if available during compilation. Package: r-cran-hans Architecture: arm64 Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 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_arm64.deb Size: 36370 MD5sum: e027b7890a2e2a1e2744e1737f193c1c SHA1: 091958fff4baabc6b7b0228e7e19f2da002a867e SHA256: 8ebd611b96b46ce08aba323b8add816fad3e71caf83561300d74cc182e09670c SHA512: bb4e691e7c2d0f9e87761d3d8eaa3199aade0fc060de4fab4d898272f99fcc992ecc86a4b9f70dcec450b0c348d2716eb5e8dda6e912802981ea4675c0300af1 Homepage: https://cran.r-project.org/package=hans Description: CRAN Package 'hans' (Haversines are not Slow) The haversine is a function used to calculate the distance between a pair of latitude and longitude points while accounting for the assumption that the points are on a spherical globe. This package provides a fast, dataframe compatible, haversine function. For the first publication on the haversine calculation see Joseph de Mendoza y Ríos (1795) (In Spanish). Package: r-cran-hapassoc Architecture: arm64 Version: 1.2-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 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_arm64.deb Size: 279066 MD5sum: 851267a25415cf56a054c99decb8c550 SHA1: a8b22e6f7c1945948ba66073f17a0b1476cfc9fc SHA256: 9136953d421a6c492750a3525d128940bfcca4a98a89a2c91544172f19723459 SHA512: be0d83efcae62ed33625aafbcd8107bf7657cc144499d21ee71884865cc4441380e6a6368ae7e5880fd8e2f0c8d76300c0253e94245b22452286a43d3fcc717f Homepage: https://cran.r-project.org/package=hapassoc Description: CRAN Package 'hapassoc' (Inference of Trait Associations with SNP Haplotypes and OtherAttributes using the EM Algorithm) The following R functions are used for inference of trait associations with haplotypes and other covariates in generalized linear models. The functions are developed primarily for data collected in cohort or cross-sectional studies. They can accommodate uncertain haplotype phase and handle missing genotypes at some SNPs. Package: r-cran-haplin Architecture: arm64 Version: 7.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4540 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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_arm64.deb Size: 1420116 MD5sum: 14eb8c4bf0cda3ce590734978d2008cd SHA1: a81691dae0fc665b2ddeaf33bf17a1b152699a76 SHA256: 7e84d1fb8d6a15ad9326d21c86223bfdccd008c419a997b5344c55fa429a7d0a SHA512: 7b9731dce3bb55863be1772896605e897c9f91842f6547d466404219331a1903208ed23e59cf879898368d786f8d26545723da945bd0223acf52db857dd5c6f2 Homepage: https://cran.r-project.org/package=Haplin Description: CRAN Package 'Haplin' (Analyzing Case-Parent Triad and/or Case-Control Data with SNPHaplotypes) Performs genetic association analyses of case-parent triad (trio) data with multiple markers. It can also incorporate complete or incomplete control triads, for instance independent control children. Estimation is based on haplotypes, for instance SNP haplotypes, even though phase is not known from the genetic data. 'Haplin' estimates relative risk (RR + conf.int.) and p-value associated with each haplotype. It uses maximum likelihood estimation to make optimal use of data from triads with missing genotypic data, for instance if some SNPs has not been typed for some individuals. 'Haplin' also allows estimation of effects of maternal haplotypes and parent-of-origin effects, particularly appropriate in perinatal epidemiology. 'Haplin' allows special models, like X-inactivation, to be fitted on the X-chromosome. A GxE analysis allows testing interactions between environment and all estimated genetic effects. The models were originally described in "Gjessing HK and Lie RT. Case-parent triads: Estimating single- and double-dose effects of fetal and maternal disease gene haplotypes. Annals of Human Genetics (2006) 70, pp. 382-396". Package: r-cran-haplo.stats Architecture: arm64 Version: 1.9.8.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 847 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-arsenal, r-cran-mass Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-haplo.stats_1.9.8.7-1.ca2604.1_arm64.deb Size: 456182 MD5sum: 3a43c09a4c47244b845903715b3e7971 SHA1: 5c08e26bf05ab7b066de9587f7dfa0257d4a9c42 SHA256: 39de5467346a535981bc9d6e4f3c7dc28ff1471e1fdab6ede1d5f8cef9c5d144 SHA512: 582196670372e2d6dc7edb068c42a843c6d80da699457bf298ccdbe8fb9a64ac621625bc098752f6fbd0bd85cce0c405c12bf2048b4d28d26f70bf9bfe14be23 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: arm64 Version: 1.7.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1702 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_arm64.deb Size: 1268356 MD5sum: e51b1199d46fe6c0c26bd1d642e7cfa4 SHA1: 2c47d0857056c80136134457ee4d1f5ff19fd3ae SHA256: 6cecf5f1a4d9a1d6a4d90a1f8d31d6d3c4fa6067f51035ff0b6bea8310d908d3 SHA512: 64866f8199624d2d1de2ccc4c7331a64b2ab82cc6db6e40395ccad19c2317f05778412217de10c275028277e4c0be04ae07d6e086be6fd6afbb3a8fd087a3256 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: arm64 Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6287 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_arm64.deb Size: 4760382 MD5sum: c56afd25b2e449109b3734732c4ebb66 SHA1: 2ca6784eb3b7c7cce397f77899ff30b3f18a5b2b SHA256: c9da7e0b8c61be9d3b66d4062b4c33d9f2ad5570645aa5821cb1b7a14e20c9bb SHA512: ddc0e2ebd1df932ef9efb4bab5d2eec43ce02125ffd42616aaf07621fd436d38a8b2a7ef0bfeb139505b29a22df39859ab87f70b4b21fd0eac369b989f25e6e6 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. . 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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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Package: r-cran-hclust1d Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 136160 MD5sum: dc3a6601d3b3dc01e1feb993d4bdb0a9 SHA1: 87ff10c0f4899c67b69cc94b7666ef372c8a4bfa SHA256: 28b509fafdb703775620c160914a39a8949755c3489bd02551ffedcb8cdc02c2 SHA512: 66072e745aa7a78b956b1b784efee0b2f08c9e0673779f589c2bb5cc26ae027a706d7605b1bb4eff74cb5d9f231007eafad01284b02e2973170ae450ae2642d0 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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Package: r-cran-hdbayes Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1612 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-instantiate, r-cran-callr, r-cran-fs, r-cran-formula.tools, r-cran-posterior, r-cran-enrichwith, r-cran-bridgesampling, r-cran-mvtnorm, r-cran-loo Suggests: r-cran-ggplot2, r-cran-ggthemes, r-cran-knitr, r-cran-rmarkdown, r-cran-tibble, r-cran-dplyr, r-cran-survival Filename: pool/dists/resolute/main/r-cran-hdbayes_0.2.0-1.ca2604.1_arm64.deb Size: 961026 MD5sum: 79f156d213a25b111ae14c2a0331ba99 SHA1: 26d2e2212a9cf23819e2fba30f1dcdbbaeb150c9 SHA256: 74f67dffa70fbc577071f72cbbb4ce98f5eae9e8e7dad255bc858a3aa5720f3f SHA512: 7937489346d0839a5a2abe295a6193496448add44b65c14ca9b3c481ba4d77ff700d9818140e7742ee18fd7f1e29ac137e41369653ff61e575e560a3fc86d2fb Homepage: https://cran.r-project.org/package=hdbayes Description: CRAN Package 'hdbayes' (Bayesian Analysis of Generalized Linear Models with HistoricalData) User-friendly functions for leveraging (multiple) historical data set(s) in Bayesian analysis of generalized linear models (GLMs) and survival models, along with support for Bayesian model averaging (BMA). 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. In addition to GLMs, the package supports survival models including: (1) accelerated failure time (AFT) models, (2) piecewise exponential (PWE) models, i.e., proportional hazards models with piecewise constant baseline hazards, and (3) mixture cure rate models that assume a common probability of cure across subjects, paired with a PWE model for the non-cured population. Functions for computing marginal log-likelihoods under each implemented prior are also included. The package compiles all the 'CmdStan' models once during installation using the 'instantiate' package. Package: r-cran-hdbcp Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-hdbcp_1.0.0-1.ca2604.1_arm64.deb Size: 133902 MD5sum: a38ab36ce883a174637eee5f4c510584 SHA1: 3cd0e0b6fa1e85899c63ffbc6a6b0de2c59e282d SHA256: fbb601b05d6ed32e748c7c4f86d5bbe095896c0a71ae8230ef218a5ad6801c40 SHA512: e7cb0e5db5dce711e54cf5b609d36068301073a175d78d258cbaa8b0ec04a86a1ecdee900168d467a73bfcf66ef7a14ddba7ea2ab02747a4a8bcafa5bda74c6e Homepage: https://cran.r-project.org/package=hdbcp Description: CRAN Package 'hdbcp' (Bayesian Change Point Detection for High-Dimensional Data) Functions implementing change point detection methods using the maximum pairwise Bayes factor approach. Additionally, the package includes tools for generating simulated datasets for comparing and evaluating change point detection techniques. Package: r-cran-hdbinseg Architecture: arm64 Version: 1.0.3-1.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-foreach, r-cran-iterators, r-cran-doparallel, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-hdbinseg_1.0.3-1.ca2604.1_arm64.deb Size: 140862 MD5sum: 5e873975c304c225fd30486788246808 SHA1: 13439a6609174d868bd637a64f47335c5be423b3 SHA256: 84ffade597e1a4d582d268df42ce9fc1f5300478eab4c0ac402e3efc25da043a SHA512: 1d832d37326d07b53d577b4fe9b5143ebf51273e37bf2d4076088fdf2d2a75c458cfbd070b374cccf367fcabd57891e589984fcdefe95f29be34a44ba16cdac6 Homepage: https://cran.r-project.org/package=hdbinseg Description: CRAN Package 'hdbinseg' (Change-Point Analysis of High-Dimensional Time Series via BinarySegmentation) Binary segmentation methods for detecting and estimating multiple change-points in the mean or second-order structure of high-dimensional time series as described in Cho and Fryzlewicz (2014) and Cho (2016) . Package: r-cran-hdcd Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 492 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mclust, r-cran-rdpack Filename: pool/dists/resolute/main/r-cran-hdcd_1.1-1.ca2604.1_arm64.deb Size: 204362 MD5sum: 3c433c10d4cf7128c70ec33ac75f40be SHA1: 6fce2264db5586fd28c27ada76c5460c85eb1883 SHA256: d93f3f0fe29c5ce1be3af936e9f6dc41bcc752e1498827a3e396453ac18b7789 SHA512: 315bd840cd2157de7da4a3d2ad01bb475a460f8d52430ccbdc9ef0bffee5db78d1f2e4bda24e46c916a4d3e3d83222f2c3583c8c0c02197bbc9bc13e0e872e5a Homepage: https://cran.r-project.org/package=HDCD Description: CRAN Package 'HDCD' (High-Dimensional Changepoint Detection) Efficient implementations of the following multiple changepoint detection algorithms: Efficient Sparsity Adaptive Change-point estimator by Moen, Glad and Tveten (2023) , Informative Sparse Projection for Estimating Changepoints by Wang and Samworth (2017) , and the method of Pilliat et al (2023) . Package: r-cran-hdclust Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1630 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-rcppprogress, r-cran-rtsne Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hdclust_1.0.4-1.ca2604.1_arm64.deb Size: 1165808 MD5sum: 35cb3819c1d19f5318c3483bf6ab3c66 SHA1: d70a602a8db4299a2f27f9c2799b4f9711dee917 SHA256: f3a085e5788ba8e953215aaeaac222bddcac3877864a84b0697eaf412ff97887 SHA512: 96a5d4f59b7095e7182431c026ec561ac3f1e5eed120128828eeeedb9c0263867ce0e28f1919d0be1d13a39bb2c29dd8f4402ce0611507f1e3cba87b2d768c52 Homepage: https://cran.r-project.org/package=HDclust Description: CRAN Package 'HDclust' (Clustering High Dimensional Data with Hidden Markov Model onVariable Blocks) Clustering of high dimensional data with Hidden Markov Model on Variable Blocks (HMM-VB) fitted via Baum-Welch algorithm. Clustering is performed by the Modal Baum-Welch algorithm (MBW), which finds modes of the density function. Lin Lin and Jia Li (2017) . Package: r-cran-hdcpdetect Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2494 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_arm64.deb Size: 2422052 MD5sum: 4479243fc74754b15e963ae1745c1076 SHA1: 2dc22f5b00c55da69f3ff79daa6fd8439f88790e SHA256: 622cd2c3260332515134ad315d0d2c400f429a50e2bbb6aa58ddeedd3f18f63e SHA512: a4d6121a7c5d48d8c867000566e5ccc98b34c7c6d37cedebe29ff97b77caa17c5fbfb588fcebb4d5c9df64ee16570e544cfb230074c1baebde02d67901078c3c Homepage: https://cran.r-project.org/package=HDcpDetect Description: CRAN Package 'HDcpDetect' (Detect Change Points in Means of High Dimensional Data) Objective: Implement new methods for detecting change points in high-dimensional time series data. These new methods can be applied to non-Gaussian data, account for spatial and temporal dependence, and detect a wide variety of change-point configurations, including changes near the boundary and changes in close proximity. Additionally, this package helps address the “small n, large p” problem, which occurs in many research contexts. This problem arises when a dataset contains changes that are visually evident but do not rise to the level of statistical significance due to the small number of observations and large number of parameters. The problem is overcome by treating the dimensions as a whole and scaling the test statistics only by its standard deviation, rather than scaling each dimension individually. Due to the computational complexity of the functions, the package runs best on datasets with a relatively large number of attributes but no more than a few hundred observations. Package: r-cran-hdcurves Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hdcurves_0.1.2-1.ca2604.1_arm64.deb Size: 49854 MD5sum: 8bdb09f08f02e01447377d9ec85eab23 SHA1: 934a6dbd5be5411b37f76053fdaf013965832058 SHA256: 9a63fdcc7560c8738698a27f7ea9e0c26a21539809156a4c07a5f64271eb3e45 SHA512: e216d6afdb60d6b81425df2eaf9b17158ec9b0ba546a5babc0aafb78cc6de726fb6646d84a8d5c5cd195fde814ad6a950574e78e8365da3cd376280969d3c87d Homepage: https://cran.r-project.org/package=HDCurves Description: CRAN Package 'HDCurves' (Hierarchical Derivative Curve Estimation) A procedure that fits derivative curves based on a sequence of quotient differences. In a hierarchical setting the package produces estimates of subject-specific and group-specific derivative curves. In a non-hierarchical setting the package produces a single derivative curve. Package: r-cran-hdf5lib Architecture: arm64 Version: 2.1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 15537 Depends: r-base-core (>= 4.6.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hdf5lib_2.1.1.1-1.ca2604.1_arm64.deb Size: 2676130 MD5sum: 5ec056a0da70c61276fd9582e786d0b5 SHA1: 957366e22768221634427feed5fe5b256ed625b1 SHA256: 259bfb92fd07caa8a2faeb2448c756d1a25dbc4098baba621c50b2f716ab0a96 SHA512: 1c2050639e5a8a085d2249dae79e800393913fc8714c79ac633727f98b89d74d2cc8f7ba3ea9ebd156b1e1315df08244aab601362ec7f51ccd486e60189acdc5 Homepage: https://cran.r-project.org/package=hdf5lib Description: CRAN Package 'hdf5lib' (Headers and Static Libraries for 'HDF5') Provides a self-contained, static build of the 'HDF5' (Hierarchical Data Format 5) 'C' library (release 2.1.1) for R package developers. Designed for use in the 'LinkingTo' field, it enables zero-dependency integration by building the library entirely from source during installation. Additionally, it compiles and internally links a comprehensive suite of advanced compression filters and their 'HDF5' plugins (Zstd, LZ4, Blosc/Blosc2, Snappy, ZFP, Bzip2, LZF, Bitshuffle, szip, and gzip). These plugins are integrated out-of-the-box, allowing downstream packages to utilize high-performance compression directly through the standard 'HDF5' API while keeping the underlying third-party headers fully encapsulated. 'HDF5' is developed by The HDF Group . Package: r-cran-hdf5r Architecture: arm64 Version: 1.3.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3109 Depends: libc6 (>= 2.17), libhdf5-310 (>= 1.14.3), libhdf5-hl-310 (>= 1.14.3), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-bit64 Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-nycflights13, r-cran-reshape2, r-cran-formatr Filename: pool/dists/resolute/main/r-cran-hdf5r_1.3.12-1.ca2604.1_arm64.deb Size: 2013020 MD5sum: c2e75f6d583fff2ced43d1ec13b07283 SHA1: ff6ca040d995c12e0f6cf6ee1162f929beaebe25 SHA256: d9916a5ca8e79faea4bbb59402e82dc1382c94b5c9bf787248462241e1272fcd SHA512: 3bedd24fc6994c63231f05a654004b0ad2c4cf1ae51935dfa533a5364dac56f14247bdca3bf2d1a01bb935313001a432986004561d820b37ac325c650122d221 Homepage: https://cran.r-project.org/package=hdf5r Description: CRAN Package 'hdf5r' (Interface to the 'HDF5' Binary Data Format) 'HDF5' is a data model, library and file format for storing and managing large amounts of data. This package provides a nearly feature complete, object oriented wrapper for the 'HDF5' API using R6 classes. Additionally, functionality is added so that 'HDF5' objects behave very similar to their corresponding R counterparts. Package: r-cran-hdflex Architecture: arm64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1350 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-checkmate, r-cran-ggplot2, r-cran-rcpp, r-cran-reshape2, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-testthat, r-cran-cowplot Filename: pool/dists/resolute/main/r-cran-hdflex_0.3.2-1.ca2604.1_arm64.deb Size: 968696 MD5sum: 369c12392aa23d801faa10d5f91eeb00 SHA1: 8ec15cedccdaa8ee255672894cc8027978569dcd SHA256: c8434fe74efb394863d474cc405a1ada52eea04be23e3adef6023f593e731ebd SHA512: c126a8f76b2202016a644a6f4e243e7168a376d904d1f4532f24520eb1c97e62fd1c850e29945413a5ad7fd655068ebfa3650d140be40460ba60085f16cf2946 Homepage: https://cran.r-project.org/package=hdflex Description: CRAN Package 'hdflex' (High-Dimensional Aggregate Density Forecasts) Provides a forecasting method that efficiently maps vast numbers of (scalar-valued) signals into an aggregate density forecast in a time-varying and computationally fast manner. The method proceeds in two steps: First, it transforms a predictive signal into a density forecast and, second, it combines the resulting candidate density forecasts into an ultimate aggregate density forecast. For a detailed explanation of the method, please refer to Adaemmer et al. (2025) . Package: r-cran-hdglm Architecture: arm64 Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hdglm_0.1-1.ca2604.1_arm64.deb Size: 23854 MD5sum: 51d82b3cb2da69b144e1663cd3f03f8a SHA1: e4f6ddc22411afe4eb741d12e002695cbd44296e SHA256: 60fd8bc94850890c2988aec216063bf4cdd258230945cfd5795c2850571366ac SHA512: b1d1f6b62ba90fd9d6b15185b196f0379bf613c167f7c5df7d4158e76e02ba794174df34cd603ea2b5fc6f6c3a2bfa3744d497028fbd99e7b2cd0f0ec2664384 Homepage: https://cran.r-project.org/package=HDGLM Description: CRAN Package 'HDGLM' (Tests for High Dimensional Generalized Linear Models) Test the significance of coefficients in high dimensional generalized linear models. Package: r-cran-hdlsskst Architecture: arm64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 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_arm64.deb Size: 129708 MD5sum: 3c9519e635adb1766e2cfa6882d11b12 SHA1: 6a086c1c9122750a37816fedff0ea9f62e69e72f SHA256: ab90a57d1a014b7e8993be71284ae2108bdaa3cbe351edd974b6689b0aef05f5 SHA512: 627550233d490801c3ca5d88450f2d832ca26d7533019e7f06ba2c9e777207dbc2feee322d3d9280a1db05951e6356e32254d5a0ef561563f327ddf25fbbec58 Homepage: https://cran.r-project.org/package=HDLSSkST Description: CRAN Package 'HDLSSkST' (Distribution-Free Exact High Dimensional Low Sample Sizek-Sample Tests) Testing homogeneity of k multivariate distributions is a classical and challenging problem in statistics, and this becomes even more challenging when the dimension of the data exceeds the sample size. We construct some tests for this purpose which are exact level (size) alpha tests based on clustering. These tests are easy to implement and distribution-free in finite sample situations. Under appropriate regularity conditions, these tests have the consistency property in HDLSS asymptotic regime, where the dimension of data grows to infinity while the sample size remains fixed. We also consider a multiscale approach, where the results for different number of partitions are aggregated judiciously. Details are in Biplab Paul, Shyamal K De and Anil K Ghosh (2021) ; Soham Sarkar and Anil K Ghosh (2019) ; William M Rand (1971) ; Cyrus R Mehta and Nitin R Patel (1983) ; Joseph C Dunn (1973) ; Sture Holm (1979) ; Yoav Benjamini and Yosef Hochberg (1995) . Package: r-cran-hdmaadmm Architecture: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 137706 MD5sum: 24f852d88fbe79b60de0082f05aa1f23 SHA1: 124cbd32736b43e98a5a13f08d4a17616b493d98 SHA256: 1b937e3ffe62fec90f1dff66ee7e47df8da60cede492a97b1f068622b244d1dd SHA512: 3870a75fee3afb006def933baec62605dd94042d7eb1d986144aba5454829375a46c8054f17b61ed2496b0468f09a8d5b6d9d91f6c2f0d1d675e04c812876419 Homepage: https://cran.r-project.org/package=HDMAADMM Description: CRAN Package 'HDMAADMM' (ADMM for High-Dimensional Mediation Models) We use the Alternating Direction Method of Multipliers (ADMM) for parameter estimation in high-dimensional, single-modality mediation models. To improve the sensitivity and specificity of estimated mediation effects, we offer the sure independence screening (SIS) function for dimension reduction. The available penalty options include Lasso, Elastic Net, Pathway Lasso, and Network-constrained Penalty. The methods employed in the package are based on Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). , Fan, J., & Lv, J. (2008) , Li, C., & Li, H. (2008) , Tibshirani, R. (1996) , Zhao, Y., & Luo, X. (2022) , and Zou, H., & Hastie, T. (2005) . Package: r-cran-hdme Architecture: arm64 Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 797 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 448944 MD5sum: 246cc75b33035525e485c0a7f90ed799 SHA1: f56d9b8808f52b1b58f6968be67eb030573012f4 SHA256: 0ec5814cd72dbfc9cefdf8d29b8e9c148af861debf3cef1996522e7aab658a85 SHA512: dcd5228f8553a8fb39ed27591ce66a15a0ee023f5998e1ee51a0a6608a6204fb7a5ae1dc2a6d1d92f10007e75c74185fd1f330970b6681da31285ceebf484215 Homepage: https://cran.r-project.org/package=hdme Description: CRAN Package 'hdme' (High-Dimensional Regression with Measurement Error) Penalized regression for generalized linear models for measurement error problems (aka. errors-in-variables). The package contains a version of the lasso (L1-penalization) which corrects for measurement error (Sorensen et al. (2015) ). It also contains an implementation of the Generalized Matrix Uncertainty Selector, which is a version the (Generalized) Dantzig Selector for the case of measurement error (Sorensen et al. (2018) ). Package: r-cran-hdnom Architecture: arm64 Version: 6.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2063 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.6.0), r-api-4.0, r-cran-foreach, r-cran-ggplot2, r-cran-glmnet, r-cran-gridextra, r-cran-ncvreg, r-cran-penalized, r-cran-survival Suggests: r-cran-doparallel, r-cran-knitr, r-cran-ragg, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hdnom_6.2.0-1.ca2604.1_arm64.deb Size: 1174852 MD5sum: 9adb30c6a164443e44eed6535ccdf607 SHA1: 007f8ee21f93ebd45114880da761d2203534fe60 SHA256: 7075d826ec6f878cf79b30c96930c1c33806300fd03564e08b412ae7f7d0b0b6 SHA512: a08daa11fb907c61d763184c5e826ea3edbb795be8c734583c0e272967e79b86fc90d2746025a0a6a193c86a796ebc39aaed6d53ae6b62b5bdd9d7bf05d58913 Homepage: https://cran.r-project.org/package=hdnom Description: CRAN Package 'hdnom' (Benchmarking and Visualization Toolkit for Penalized Cox Models) Creates nomogram visualizations for penalized Cox regression models, with the support of reproducible survival model building, validation, calibration, and comparison for high-dimensional data. Package: r-cran-hdnra Architecture: arm64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5426 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 5232664 MD5sum: 19e7bce4bdf747d93fa240b378dda75d SHA1: f6f9db82b9e62092616abf6bfac40fe9443b1ac0 SHA256: 4965b399cc3c2d17e87e3e5dd48bf538545b86219c12b3fae36762e1368e8123 SHA512: 6936ecdf4eace318343a295f0ca16f7bc906305878bda29de736a274e26eac8bc508ee48282b0218bb10735ee155ed244674a8a561247d752e91a3b6632cdb24 Homepage: https://cran.r-project.org/package=HDNRA Description: CRAN Package 'HDNRA' (High-Dimensional Location Testing with Normal-ReferenceApproaches) Provides inverse-free high-dimensional location tests for two-sample and general linear hypothesis testing (GLHT) problems under equal or unequal covariance structures. The package implements classical normal-approximation procedures, scale-invariant procedures, normal-reference procedures based on covariance-matched Gaussian companions, and F-type normal-reference calibrations for heteroscedastic Behrens-Fisher and GLHT settings. Implemented two-sample normal-approximation and scale-invariant procedures include Bai and Saranadasa (1996) , Chen and Qin (2010) , Srivastava and Du (2008) , and Srivastava et al. (2013) . Implemented two-sample normal-reference procedures include Zhang, Guo, Zhou and Cheng (2020) , Zhang, Zhou, Guo and Zhu (2021) , Zhang, Zhu and Zhang (2020) , Zhang, Zhu and Zhang (2023) , Zhang and Zhu (2022) , Zhang and Zhu (2022) , and Zhu, Wang and Zhang (2023) . Implemented GLHT normal-approximation procedures include Fujikoshi et al. (2004) , Srivastava and Fujikoshi (2006) , Yamada and Srivastava (2012) , Schott (2007) , and Zhou, Guo and Zhang (2017) . Implemented GLHT normal-reference procedures include Zhang, Guo and Zhou (2017) , Zhang, Zhou and Guo (2022) , Zhu, Zhang and Zhang (2022) , Zhu and Zhang (2022) , Zhang and Zhu (2022) , and Cao et al. (2024) . The package also includes the random-integration normal-approximation GLHT procedure of Li et al. (2025) . A package-level overview is given in Wang, Zhu and Zhang (2026) . Package: r-cran-hdomdesign Architecture: arm64 Version: 1.0-2-1.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-hadamardr Filename: pool/dists/resolute/main/r-cran-hdomdesign_1.0-2-1.ca2604.1_arm64.deb Size: 64654 MD5sum: 509d0b7339795734ec5500f0d70cfafe SHA1: ff499d38bf548d63fd0f603c93af80207b20f81a SHA256: fefa64abb6c38c2b62d4210cb0ed3533f872ce2db83746ce5ef7305bd06634f0 SHA512: 55006fda5b519bebc36e819f5cc38552a5faf0074dc089187a9b2a26bde2935846fafe60d925acf775e7bf797c90c78258ed44865df94bbdf6d218d6fb27b2e1 Homepage: https://cran.r-project.org/package=HDOMDesign Description: CRAN Package 'HDOMDesign' (High-Dimensional Orthogonal Maximin Distance Designs) Contains functions to construct high-dimensional orthogonal maximin distance designs in two, four, eight, and sixteen levels from rotating the Kronecker product of sub-Hadamard matrices. 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Package: r-cran-hdqr Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hdqr_1.0.2-1.ca2604.1_arm64.deb Size: 112180 MD5sum: d0c99e9536ad1c1b960a65601b626d18 SHA1: 21dfcff5a18df36cbf06047b5a74f16a6390947a SHA256: 6061a046392d32fe5bc6f58b6ef4e082be43880a9b3b2c179dc7f17e43cf7c05 SHA512: 3ffed93553620604e0732d267ad91c58f190912ad8c3179053592b8632d342dfce0e03a10fe4da7f108a7c58e1840af2737f4ca7c9153cce1a6bb08f3901d816 Homepage: https://cran.r-project.org/package=hdqr Description: CRAN Package 'hdqr' (Fast Algorithm for Penalized Quantile Regression) Implements an efficient algorithm for fitting the entire regularization path of quantile regression models with elastic-net penalties using a generalized coordinate descent scheme. 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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) . Package: r-cran-hdsvm Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 220 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-hdsvm_1.0.2-1.ca2604.1_arm64.deb Size: 111192 MD5sum: 27ab60a0ba8edefb89445f913aa50b8a SHA1: 4cd4d8fe16d73d4d1101e0c11e91552cbd066838 SHA256: a6c8197215126d99fc53863b17b08cdc831fe11d9f6f1be35542479c399c6b54 SHA512: 3ad09aa980359bbb495cede65baadf1a68a18107c532d4e2f4279e564fcf50ed1fff119095156f898ba5634b8dfe80c495f90a53e3f3f375e939fb8d23a7a188 Homepage: https://cran.r-project.org/package=hdsvm Description: CRAN Package 'hdsvm' (Fast Algorithm for Support Vector Machine) Implements an efficient algorithm for fitting the entire regularization path of support vector machine models with elastic-net penalties using a generalized coordinate descent scheme. 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Package: r-cran-hdtsa Architecture: arm64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1394 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-clime, r-cran-sandwich, r-cran-mass, r-cran-geigen, r-cran-jointdiag, r-cran-vars, r-cran-forecast, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-hdtsa_1.0.6-1.ca2604.1_arm64.deb Size: 1081172 MD5sum: 275f18eeb4bcfb505338d84a12b419a8 SHA1: cf23ee9f5e479c75f5bd77ddc9fd16a381dc6c16 SHA256: ee097b6dc311d8434fc462995cce8599b23e1aa0cb04b57be214869580694359 SHA512: 77d26296f264335eed5fa466341818a48c41b08c9028470019e9fae851f222cd9d1cdb1077ecd49418818a2808467d0ca136926ecc7f554f74a18c8072227caa Homepage: https://cran.r-project.org/package=HDTSA Description: CRAN Package 'HDTSA' (High Dimensional Time Series Analysis Tools) An implementation for high-dimensional time series analysis methods, including factor model for vector time series proposed by Lam and Yao (2012) and Chang, Guo and Yao (2015) , martingale difference test proposed by Chang, Jiang and Shao (2023) , principal component analysis for vector time series proposed by Chang, Guo and Yao (2018) , cointegration analysis proposed by Zhang, Robinson and Yao (2019) , unit root test proposed by Chang, Cheng and Yao (2022) , white noise tests proposed by Chang, Yao and Zhou (2017) and Chang et al. (2026+), CP-decomposition for matrix time series proposed by Chang et al. (2023) and Chang et al. (2026+) , and statistical inference for spectral density matrix proposed by Chang et al. (2025) . Package: r-cran-hdtweedie Architecture: arm64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 414 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hdtweedie_1.2-1.ca2604.1_arm64.deb Size: 332226 MD5sum: af211e1bf7f397aac33bcc86aa32e01f SHA1: 5efea6ff0215cc2594f8d494885c32074497a8ca SHA256: 13790507cbf6c22d94dc1c7aa5e9f3f9e05ca1f546d0de40ae217b6d1ebfa019 SHA512: 308553373b9206b0757b99f6dc7561d17784e227712fa90e8f8aec66533780d0b05d9b63ff3179a6281991c3dd0040b9fc2a92af9e65c9857c990e1f7cf0be7e Homepage: https://cran.r-project.org/package=HDtweedie Description: CRAN Package 'HDtweedie' (The Lasso for Tweedie's Compound Poisson Model Using an IRLS-BMDAlgorithm) The Tweedie lasso model implements an iteratively reweighed least square (IRLS) strategy that incorporates a blockwise majorization decent (BMD) method, for efficiently computing solution paths of the (grouped) lasso and the (grouped) elastic net methods. Package: r-cran-healthbr Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1574 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-curl, r-cran-cli, r-cran-rlang, r-cran-stringr, r-cran-purrr, r-cran-readr, r-cran-jsonlite, r-cran-foreign Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-readxl, r-cran-haven, r-cran-furrr, r-cran-future, r-cran-arrow, r-cran-dbplyr, r-cran-duckdb, r-cran-piggyback, r-cran-survey, r-cran-srvyr Filename: pool/dists/resolute/main/r-cran-healthbr_0.2.0-1.ca2604.1_arm64.deb Size: 743532 MD5sum: de9289dff5773a956f9ee3f22081d24e SHA1: be94d28514f137a734b24461cbad44e2ac7b21df SHA256: 66b0551699194895a83af21f3ec6225244a1a1a4196dea3f1a777559e7c78f60 SHA512: 264d3bd9e431288642aa0320421bda4f2ec9e9989580062b93a634166a4d2e67ff52fdf253456fc8cacfe94dcaa46ed767e9bc1c00dbf1943e7fd145a8b6eb2c Homepage: https://cran.r-project.org/package=healthbR Description: CRAN Package 'healthbR' (Access Brazilian Public Health Data) Provides easy access to Brazilian public health data from multiple sources including VIGITEL (Surveillance of Risk Factors for Chronic Diseases by Telephone Survey), PNS (National Health Survey), 'PNAD' Continua (Continuous National Household Sample Survey), 'POF' (Household Budget Survey with food security and consumption data), 'Censo Demografico' (population denominators via 'SIDRA' API), SIM (Mortality Information System), SINASC (Live Birth Information System), 'SIH' (Hospital Information System), 'SIA' (Outpatient Information System), 'SINAN' (Notifiable Diseases Surveillance), 'CNES' (National Health Facility Registry), 'SI-PNI' (National Immunization Program - aggregated 1994-2019 via FTP, individual-level 'microdata' 2020+ via 'OpenDataSUS' API), 'SISAB' (Primary Care Health Information System - coverage indicators via REST API), ANS ('Agencia Nacional de Saude Suplementar' - supplementary health beneficiaries, consumer complaints, and financial statements), 'ANVISA' ('Agencia Nacional de Vigilancia Sanitaria' - product registrations, 'pharmacovigilance', 'hemovigilance', 'technovigilance', and controlled substance sales via 'SNGPC'), and other health information systems. Data is downloaded from the Brazilian Ministry of Health and 'IBGE' repositories. Data is returned in tidy format following tidyverse conventions. Package: r-cran-healthyaddress Architecture: arm64 Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4431 Depends: libc6 (>= 2.17), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-fastmatch, r-cran-fst, r-cran-hutils, r-cran-hutilscpp, r-cran-magrittr, r-cran-qs2 Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-healthyaddress_0.5.1-1.ca2604.1_arm64.deb Size: 4191242 MD5sum: 43a22fd9175907c7d5daf685ea20db1a SHA1: 42e4859c2f862cab718e2d1d1a8714595db8b90f SHA256: 9136cdd06dd8787b2b35d230615c8c5864420a32f31cf208b08048ea8a0a8c2f SHA512: e8514559b8369e70d24a3a5d01236c36e75e879069fa75d89597f278f69f1d6447563aa7364dc4579012cdd81a3bde6c7445db0ccdec467701838caf2de475f8 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-heatwaver Architecture: arm64 Version: 0.5.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3155 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-fasttime, r-cran-ggplot2, r-cran-rcpp, r-cran-rcpproll, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-doparallel, r-cran-dplyr, r-cran-ggpubr, r-cran-knitr, r-cran-lubridate, r-cran-ncdf4, r-cran-plyr, r-cran-rerddap, r-cran-rmarkdown, r-cran-stringr, r-cran-testthat, r-cran-tibble, r-cran-tidync, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-heatwaver_0.5.5-1.ca2604.1_arm64.deb Size: 1829702 MD5sum: 76f834f1eacc8d83cc1548d5b32586ff SHA1: 3d8f0830b885b724b2784c9c16105ef19765549b SHA256: e06b1fb929079ead75e9e30323022fd1a52cba2cb29fe311351bfaaaf70e1981 SHA512: f81fc24916c78591e575848d604d106ee4002054103ebd4cf0cffddc70349cdba3f3a6edc3f5685d04c43f2ce7f268b0a5cbc0f8438b89df16c1fb4461e77b05 Homepage: https://cran.r-project.org/package=heatwaveR Description: CRAN Package 'heatwaveR' (Detect Heatwaves and Cold-Spells) The different methods for defining, detecting, and categorising the extreme events known as heatwaves or cold-spells, as first proposed in Hobday et al. (2016) and Hobday et al. (2018) . The functions in this package work on both air and water temperature data of hourly and daily temporal resolution. These detection algorithms may be used on non-temperature data as well. Package: r-cran-heck Architecture: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1670 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_arm64.deb Size: 534960 MD5sum: 175dc7987d15d1b6ee60ede611ea599a SHA1: ea96f95e93ccca11b3bfe6be9fb5372b4d62a86c SHA256: 25d40e46aaed1016f59134ae39aa4e3035c72c59a167c519596743d63b74e5c6 SHA512: cc04ccc4e797ad4280537474257b15a8c04e0f69bf668ed323c1edd2c450774f01a8e83494d5de225d8c28d30247a55a60f286baff63b5011a9abe0f500188a4 Homepage: https://cran.r-project.org/package=heck Description: CRAN Package 'heck' (Highly Performant String Case Converter) Provides a case conversion between common cases like CamelCase and snake_case. Using the 'rust crate heck' as the backend for a highly performant case conversion for 'R'. Package: r-cran-hellcor Architecture: arm64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 169 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 75580 MD5sum: d652d18a0aa5a2350e1f533ccd57febf SHA1: 7ff291272ccbdb593bde789af35fc4c88e5b9bb5 SHA256: 58080ce56b8e953a687fa21b2ae6d4a482dd3104e87aea32a732e4aeb465755c SHA512: 3d276ea45f8248c94b054661fb49ff2885e7c81f385b3b73303a6cc70b3c1c798fefda113222f01c19ae5c7675b8b6997321acc2b96f6e0f102b84ac5cc5a78e Homepage: https://cran.r-project.org/package=HellCor Description: CRAN Package 'HellCor' (The Hellinger Correlation) Empirical value of the Hellinger correlation, a measure of dependence between two continuous random variables. More details can be found in Geenens and Lafaye De Micheaux (2019) . Package: r-cran-hellorust Architecture: arm64 Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1269 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_arm64.deb Size: 443330 MD5sum: 3dff68629a54f9b260a2d8898ec3bc80 SHA1: 6eb1050e28b1cc1df689809f54c61ea71d8076c7 SHA256: ba35b2d58998a66965202827f6da9a44061881a8e6cb9431d745b2b3e961948e SHA512: 47c9d91367471387704898b2d0aa1b4580f468289b5818acf0a015f9d6ebf8a9e5d87173de024dec92fce44891f3fe536d014af6ef1e5c0253cdf6bb603d7ce4 Homepage: https://cran.r-project.org/package=hellorust Description: CRAN Package 'hellorust' (Minimal Examples of Using Rust Code in R) Template R package with minimal setup to use Rust code in R without hacks or frameworks. Includes basic examples of importing cargo dependencies, spawning threads and passing numbers or strings from Rust to R. Cargo crates are automatically 'vendored' in the R source package to support offline installation. The GitHub repository for this package has more details and also explains how to set up CI. This project was first presented at 'Erum2018' to showcase R-Rust integration ; for a real world use-case, see the 'gifski' package on 'CRAN'. Package: r-cran-hermiter Architecture: arm64 Version: 2.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3999 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 2957882 MD5sum: ba1d2b1cc9a94661768bc212687ef1c2 SHA1: 908752b760dc7464414f510e48b142df93f82168 SHA256: 3af8c1e8f6b2ef92d656820cda55ae297837c165e8deeb60c325778d3b84c941 SHA512: a88e8a2614e950025260fe9cb7653f2fad27b6b66c583b0210985d2b0d7c02fd671cf2a7f0b8934f112a451ee759ac7f45274a564f86783b305ab3b755fc3ef0 Homepage: https://cran.r-project.org/package=hermiter Description: CRAN Package 'hermiter' (Efficient Sequential and Batch Estimation of Univariate andBivariate Probability Density Functions and CumulativeDistribution Functions along with Quantiles (Univariate) andNonparametric Correlation (Bivariate)) Facilitates estimation of full univariate and bivariate probability density functions and cumulative distribution functions along with full quantile functions (univariate) and nonparametric correlation (bivariate) using Hermite series based estimators. These estimators are particularly useful in the sequential setting (both stationary and non-stationary) and one-pass batch estimation setting for large data sets. Based on: Stephanou, Michael, Varughese, Melvin and Macdonald, Iain. "Sequential quantiles via Hermite series density estimation." Electronic Journal of Statistics 11.1 (2017): 570-607 , Stephanou, Michael and Varughese, Melvin. "On the properties of Hermite series based distribution function estimators." Metrika (2020) and Stephanou, Michael and Varughese, Melvin. "Sequential estimation of Spearman rank correlation using Hermite series estimators." Journal of Multivariate Analysis (2021) . Package: r-cran-hesim Architecture: arm64 Version: 0.5.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5751 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_arm64.deb Size: 3028134 MD5sum: 721f263b9d3021c80f10dbcdccb73a5b SHA1: e1cd97d631fde9b7b81b248b488ce78c3686cc79 SHA256: b40807dc4325d6d40ebf92f18ceae5772b6188bf107f8fb254aa3ed9d3c13b8d SHA512: 71163f1da1913d97f1f2cbd18705cdd5de1f6544c4821d75f20a3f737bf5dcc4e332c017f609a4a42ce91aca5b81c2fe6f30cde55bb4bbb17a649097abcf1664 Homepage: https://cran.r-project.org/package=hesim Description: CRAN Package 'hesim' (Health Economic Simulation Modeling and Decision Analysis) A modular and computationally efficient R package for parameterizing, simulating, and analyzing health economic simulation models. The package supports cohort discrete time state transition models (Briggs et al. 1998) , N-state partitioned survival models (Glasziou et al. 1990) , and individual-level continuous time state transition models (Siebert et al. 2012) , encompassing both Markov (time-homogeneous and time-inhomogeneous) and semi-Markov processes. Decision uncertainty from a cost-effectiveness analysis is quantified with standard graphical and tabular summaries of a probabilistic sensitivity analysis (Claxton et al. 2005, Barton et al. 2008) , . Use of C++ and data.table make individual-patient simulation, probabilistic sensitivity analysis, and incorporation of patient heterogeneity fast. Package: r-cran-heterogen Architecture: arm64 Version: 1.2.33-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2827 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_arm64.deb Size: 554484 MD5sum: be93d45ef20ae29d2ffec388484bdcb9 SHA1: f21f43e9d171425f404788cd49df011aa8b396d2 SHA256: 059fee5dce0bc9954b295c224c808ed252e6e0668480196654f3e36bb45c114d SHA512: a6e89824c1814e0af49fc25152ad6c211327097b2cce3e6e49d6213701e3e8d5ee5eff1ff428cf6f26723d70ce094a03692793c31c3c10c6302294c9169956ef Homepage: https://cran.r-project.org/package=heterogen Description: CRAN Package 'heterogen' (Spatial Functions for Heterogeneity and Climate Variability) A comprehensive suite of spatial functions created to analyze and assess data heterogeneity and climate variability in spatial datasets. 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Package: r-cran-hetgp Architecture: arm64 Version: 1.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2320 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-dicedesign, r-cran-mco, r-cran-quadprog Suggests: r-cran-knitr, r-cran-monomvn, r-cran-lhs, r-cran-colorspace Filename: pool/dists/resolute/main/r-cran-hetgp_1.1.9-1.ca2604.1_arm64.deb Size: 1923030 MD5sum: f662fade8310a302086f23c9ecc4c65e SHA1: fa8c411ee23e05ed8bb7b4964b0f7b2b974d548d SHA256: 91d5f3df8e9c532248a8b46b25fb83aa686809b28cb89f288b88c1b0c1226958 SHA512: 5452a5af52c22e434eeb84e2d5278d657481a4952bb12a61fb1ba44c530c38057ce871571f17dccfc85adbaaf578be1ac5c24bc0f46fe1510f4928f6ed7ca129 Homepage: https://cran.r-project.org/package=hetGP Description: CRAN Package 'hetGP' (Heteroskedastic Gaussian Process Modeling and Design underReplication) Performs Gaussian process regression with heteroskedastic noise following the model by Binois, M., Gramacy, R., Ludkovski, M. 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Package: r-cran-heumilkr Architecture: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 809 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang, r-cran-cli, r-cran-xml2, r-cran-ggplot2, r-cran-cpp11 Suggests: r-cran-testthat, r-cran-hedgehog, r-cran-curl, r-cran-ggextra, r-cran-scales, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-heumilkr_0.3.0-1.ca2604.1_arm64.deb Size: 572964 MD5sum: 9870bacfa6652c4f5be25ef7ae4ce011 SHA1: dbf5cabed30369ddec7f834ab65537dc6a118ed2 SHA256: 1da9fb825c0e79b5a627275d7d79d9d0a89c57ae3202c39a426303b894525116 SHA512: b3a83cd4bbdccce8da3b1562017d41272c73c8ea84103bedfc595788e3be73eb99acf7df01bd1c798f5d3c46b856651f91cad04432c958ea44ba72d8f26e35ff Homepage: https://cran.r-project.org/package=heumilkr Description: CRAN Package 'heumilkr' (Heuristic Capacitated Vehicle Routing Problem Solver) Implements the Clarke-Wright algorithm to find a quasi-optimal solution to the Capacitated Vehicle Routing Problem. 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: arm64 Version: 1.28.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1843 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1578418 MD5sum: 15fb2cd24872e3167ffb9480c03f7c96 SHA1: 2c29c2a61b7e30763724acc93d25fcd2ff373f84 SHA256: f991c7a9be0edafca471878704e8f74cb1c1eb50dd7c441a3ff45d8a337c9452 SHA512: 50e4d8cb4fee62afcb4de61b4a45c63a5c69100d43a61db3cfb7d2b8f3cb8d320d51f3741df754359aeed4cfd0c7f254c115ad5a3d94771655fd6167057e53a5 Homepage: https://cran.r-project.org/package=hexbin Description: CRAN Package 'hexbin' (Hexagonal Binning Routines) Binning and plotting functions for hexagonal bins. Package: r-cran-hexdensity Architecture: arm64 Version: 1.4.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-hexbin, r-cran-spatstat.geom Suggests: r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-hexdensity_1.4.10-1.ca2604.1_arm64.deb Size: 69726 MD5sum: 085cab724bc6d3cafdad0693e110b94d SHA1: 4198e8699f2e1896eb86fa9010758851ac990f54 SHA256: e11d7aa966a3f45c7e77b9310ac4d1d8fe9636b21d99219d643f8f5d8c57c1d6 SHA512: aab272211421af6f33745dcfbd7c54814d415c3ffb0d67cd9e26eb8110814aae101bb47d8f638ddec62aa3f7eaeb3c183ac37492de1e18673cf623ffe5764575 Homepage: https://cran.r-project.org/package=hexDensity Description: CRAN Package 'hexDensity' (Fast Kernel Density Estimation with Hexagonal Grid) Kernel density estimation with hexagonal grid for bivariate data. Hexagonal grid has many beneficial properties like equidistant neighbours and less edge bias, making it better for spatial analyses than the more commonly used rectangular grid. Carr, D. B. et al. (1987) . Diggle, P. J. (2010) . Hill, B. (2017) . Jones, M. C. (1993) . Package: r-cran-hexify Architecture: arm64 Version: 0.6.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2770 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_arm64.deb Size: 1834936 MD5sum: 1a5f98c572a05673238705250b831d9c SHA1: 8f4d9fc564250ff359aff73ad37be7c1fad89848 SHA256: 96d0f689944697c5c5f8b81fac29d6cad3d7d039d8ed213e02e7c9bc97fdc352 SHA512: b0ffc003aeba7cbdcf2bc4367dcc8d6e0f933b0f3ed6dbe00c1c8d54539ded3d3336a826aa2efffa3db9753775f892a6f71024978ce118a1ad137382645f35af 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). 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Package: r-cran-hgm Architecture: arm64 Version: 1.23-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 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_arm64.deb Size: 124102 MD5sum: 43762cf0ba9b6719301000f3b5ba192a SHA1: a7b322b94d040353dd9b9ec417f070e5fa54207c SHA256: c12c89be142e5d548c11ccc5f2b320cd5dfe9d1b68cbc8127410b4c4143a292f SHA512: e493330685a13226ecfb1a76f29d3fcf32aed2017afabe995d2c36a5a8d85c5eb4623780352dc6a296982d70dccd6c822fb56dc02091f2f4e72e0e1ddbf3ab8a 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. 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If data have spatial hierarchical structures (especially are overlapping on some locations), it is worth trying this model to reach better fitness. Package: r-cran-hhbayes Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2351 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_arm64.deb Size: 1026948 MD5sum: 9b1110cf96f6f293d2b0a6a56debb9dc SHA1: c803717edeb618ac7db1493be501bc02ca3f6a76 SHA256: 1890bd7745217411b8bb31a152359b71d58cedb23081667410fa05f4e7a66120 SHA512: 5bf1ea48d8782820276026d35c3eddd0f1aa03f5dfa98b093040b14d50ae980974c097c5a8504eea681493136441129eb4087a4fbf112cde8c9c579b536b18d4 Homepage: https://cran.r-project.org/package=HHBayes Description: CRAN Package 'HHBayes' (Bayesian Household Transmission Modeling with 'Stan') Provides a streamlined pipeline to simulate household infection dynamics, estimate transmission parameters, and visualize epidemic timelines. Uses a Bayesian approach with 'Stan' that models transmission probability as a function of viral load, seasonality and infectivity, multiple infection episodes (reinfections), and waning immunity modeling. Li et al. (2026) ). Package: r-cran-hhdynamics Architecture: arm64 Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 824 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 534382 MD5sum: 584e4454964228dcd2c0a5a628f3759a SHA1: 32189320e54c67808c78b110d986e0cf419ad133 SHA256: a6d7bb4572c829a2a1655ba8a7001ffe4a04deeb8cbf817e773071bed4ddd2e7 SHA512: 6068e8dac6dfb24013ab94c32826bf57fcc60ccffc2bc00997af6e585c2da00cb4bb24ce8374e11af89a8b77166b03dde6203a6fa3db72a3cf75c994149bfe28 Homepage: https://cran.r-project.org/package=hhdynamics Description: CRAN Package 'hhdynamics' (Fitting Household Transmission Model to Estimate HouseholdTransmission Dynamics of Influenza) A Bayesian household transmission model to estimate household transmission dynamics, with accounting for infection from community and tertiary cases. Package: r-cran-hhsmm Architecture: arm64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 438 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 346416 MD5sum: 0087913b98c330daa247cff161be2177 SHA1: e09f831936c19c7ef224456f577767081d52b92a SHA256: d30293565d0984d15d2c4eeee4aeb2eab036f89d23d8d958ee4b759e9f8aa1eb SHA512: 3f41a8b0fdc99a56bb893246edffb5b4a8dc454251977d96dda3c58bd1e024e201dedbaaa1d058f12220b7ee17d8e4ee92b9d40e80c9d572a7e741f39b34f93b Homepage: https://cran.r-project.org/package=hhsmm Description: CRAN Package 'hhsmm' (Hidden Hybrid Markov/Semi-Markov Model Fitting) Develops algorithms for fitting, prediction, simulation and initialization of the following models (1)- hidden hybrid Markov/semi-Markov model, introduced by Guedon (2005) , (2)- nonparametric mixture of B-splines emissions (Langrock et al., 2015 ), (3)- regime switching regression model (Kim et al., 2008 ) and auto-regressive hidden hybrid Markov/semi-Markov model, (4)- spline-based nonparametric estimation of additive state-switching models (Langrock et al., 2018 ) (5)- robust emission model proposed by Qin et al, 2024 (6)- several emission distributions, including mixture of multivariate normal (which can also handle missing data using EM algorithm) and multi-nomial emission (for modeling polymer or DNA sequences) (7)- tools for prediction of future state sequence, computing the score of a new sequence, splitting the samples and sequences to train and test sets, computing the information measures of the models, computing the residual useful lifetime (reliability) and many other useful tools ... (read for more description: Amini et al., 2022 and its arxiv version: ). Package: r-cran-hibayes Architecture: arm64 Version: 3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1383 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_arm64.deb Size: 517212 MD5sum: c61324a3e8f4058bf7301371adcb498e SHA1: 9f45e809d99a48c0aa57589147d4739a1e8a3319 SHA256: 5463e4ea2abc5504abdea0dd541adf7cd8b74246a15db357e1a91e63107a7540 SHA512: 26fa92fbf2993cfa0736966faa8e9b0e33ad33c804496f1c23097ade0330e33bef339154685fc3104bf89ff0795f706d25f7b7ebffbeee93279030cf69ba7238 Homepage: https://cran.r-project.org/package=hibayes Description: CRAN Package 'hibayes' (Individual-Level, Summary-Level and Single-Step BayesianRegression Model) A user-friendly tool to fit Bayesian regression models. It can fit 3 types of Bayesian models using individual-level, summary-level, and individual plus pedigree-level (single-step) data for both Genomic prediction/selection (GS) and Genome-Wide Association Study (GWAS), it was designed to estimate joint effects and genetic parameters for a complex trait, including: (1) fixed effects and coefficients of covariates, (2) environmental random effects, and its corresponding variance, (3) genetic variance, (4) residual variance, (5) heritability, (6) genomic estimated breeding values (GEBV) for both genotyped and non-genotyped individuals, (7) SNP effect size, (8) phenotype/genetic variance explained (PVE) for single or multiple SNPs, (9) posterior probability of association of the genomic window (WPPA), (10) posterior inclusive probability (PIP). The functions are not limited, we will keep on going in enriching it with more features. References: Lilin Yin et al. (2025) ; Meuwissen et al. (2001) ; Gustavo et al. (2013) ; Habier et al. (2011) ; Yi et al. (2008) ; Zhou et al. (2013) ; Moser et al. (2015) ; Lloyd-Jones et al. (2019) ; Henderson (1976) ; Fernando et al. (2014) . Package: r-cran-hiclimr Architecture: arm64 Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 714 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 573052 MD5sum: f711a86ab70854f81aaa97384d23f9d6 SHA1: 199533b155641ee7895ec6f0e82147768ae43137 SHA256: 4165d7bae63103d73eef12068cf2ba1aa44e72790e5e273c04b94f51f07db4a8 SHA512: 06556493bf1cfee3d4fcb364f3f5b2216cac5ef803cb3a233d09761f21f0b3548a8c9bba61f3cb4274b6ff617f832c90c9aa7d0b0ad153b20b7e1b44580177d3 Homepage: https://cran.r-project.org/package=HiClimR Description: CRAN Package 'HiClimR' (Hierarchical Climate Regionalization) A tool for Hierarchical Climate Regionalization applicable to any correlation-based clustering. It adds several features and a new clustering method (called, 'regional' linkage) to hierarchical clustering in R ('hclust' function in 'stats' library): data regridding, coarsening spatial resolution, geographic masking, contiguity-constrained clustering, data filtering by mean and/or variance thresholds, data preprocessing (detrending, standardization, and PCA), faster correlation function with preliminary big data support, different clustering methods, hybrid hierarchical clustering, multivariate clustering (MVC), cluster validation, visualization of regionalization results, and exporting region map and mean timeseries into NetCDF-4 file. The technical details are described in Badr et al. (2015) . 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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. 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The package provides penalized regression methods that decompose subgroup specific effects into shared global effects, Major subgroup specific effects, and Minor subgroup specific effects, enabling structured borrowing of information across related clinical subgroups. Both lasso and hierarchical overlapping group lasso penalties are supported to encourage sparsity while respecting the nested subgroup structure. Efficient computation is achieved through a modified design matrix representation and a custom algorithm for overlapping group penalties. 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For more details, see Bien, J., Taylor, J., Tibshirani, R., (2013) "A Lasso for Hierarchical Interactions." Annals of Statistics. 41(3). 1111-1141. 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The core function, HiGarrote(), offers an automated approach for analyzing experiments while respecting hierarchical structures among effects. For methodological details, refer to Yu and Joseph (2025) . This work is supported by U.S. National Science Foundation grant DMS-2310637. Package: r-cran-highfrequency Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3314 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-xts, r-cran-zoo, r-cran-rcpp, r-cran-robustbase, r-cran-data.table, r-cran-rcpproll, r-cran-quantmod, r-cran-sandwich, r-cran-numderiv, r-cran-rsolnp, r-cran-rcpparmadillo Suggests: r-cran-mvtnorm, r-cran-covr, r-cran-fkf, r-cran-rugarch, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-highfrequency_1.0.3-1.ca2604.1_arm64.deb Size: 3061212 MD5sum: e39c4c5b91e0663c1858300eda68b114 SHA1: 47995b8f5df8095ad0da2384940fd56297f6af05 SHA256: 56fe21f34b5d7cf7fb7944958c6c4dbc0b13b7c1c3a0ada030f21c11038c88b5 SHA512: 6776a7c9239244a09f95c798254a1ac5b12a7981b7ddb4b72e13746b0cefac10b52201f8a1df6245fc34db0deae2a1785e39c87bb7590e325793954ac7ccd368 Homepage: https://cran.r-project.org/package=highfrequency Description: CRAN Package 'highfrequency' (Tools for Highfrequency Data Analysis) Provide functionality to manage, clean and match highfrequency trades and quotes data, calculate various liquidity measures, estimate and forecast volatility, detect price jumps and investigate microstructure noise and intraday periodicity. A detailed vignette can be found in the open-access paper "Analyzing Intraday Financial Data in R: The highfrequency Package" by Boudt, Kleen, and Sjoerup (2022, ). Package: r-cran-highlight Architecture: arm64 Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1464 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-highlight_0.5.2-1.ca2604.1_arm64.deb Size: 494456 MD5sum: 77e0965f96d01766465eeb435adc88c4 SHA1: 05d70a731d590512b0d6e620a897a08e31890ef2 SHA256: 83c38ad5a5d1ebcc91aab19d2e88caee06fa76381ab99e37079ead8ffa7d79e1 SHA512: 5395936d65655b9eacc6d918adc0825e6fada14bc470dd71d0fbef12f7375340476cb18c62be25a096672caa976864ede59846f88397917f288861ba485d6f4d Homepage: https://cran.r-project.org/package=highlight Description: CRAN Package 'highlight' (Syntax Highlighter) Syntax highlighter for R code based on the results of the R parser. 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High-order portfolios use higher order moments to better characterize the return distribution. Different formulations and fast algorithms are proposed for high-order portfolios based on the mean, variance, skewness, and kurtosis. The package is based on the papers: R. Zhou and D. P. Palomar (2021). "Solving High-Order Portfolios via Successive Convex Approximation Algorithms." . X. Wang, R. Zhou, J. Ying, and D. P. Palomar (2022). "Efficient and Scalable High-Order Portfolios Design via Parametric Skew-t Distribution." . 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By transforming the high dimensional space into a high dimensional grid, the number of cells in each sub-space of the grid is characteristic of a given sample. Using a Hilbert curve each sample can be visualized as a simple density plot, and the distance between samples can be calculated from the distribution of cells using the Jensen-Shannon distance. Bins that correspond to significant differences between samples can identified using a simple bootstrap procedure. Package: r-cran-hint Architecture: arm64 Version: 0.1-3-1.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 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hint_0.1-3-1.ca2604.1_arm64.deb Size: 83340 MD5sum: 68fb556d556c202a73999c8844a44c85 SHA1: ce3f7a5a5c66c4d093d7f99c2ce2301900ad56fd SHA256: 784c39aa4d55e9925f845ae0ef2e4210683f28a3ed310d83edc7386d116b4687 SHA512: 8aa8b25ce2bb59eb04741f6cef88d1451e857ec2a9c523e97743209732fd0de78414170cda634c9027c7fff113331e9a845d7f0c75e19fb2b58eb37de5f8e15b Homepage: https://cran.r-project.org/package=hint Description: CRAN Package 'hint' (Tools for Hypothesis Testing Based on HypergeometricIntersection Distributions) Hypergeometric Intersection distributions are a broad group of distributions that describe the probability of picking intersections when drawing independently from two (or more) urns containing variable numbers of balls belonging to the same n categories. . 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Package: r-cran-hirestec Architecture: arm64 Version: 0.63.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3138 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-correctoverloadedpeaks, r-cran-plyr Suggests: r-cran-beeswarm, r-cran-cormid, r-cran-interpretmsspectrum, r-cran-knitr, r-cran-openxlsx, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-hirestec_0.63.1-1.ca2604.1_arm64.deb Size: 2885886 MD5sum: af728d41366afd5436127b9e8700db9f SHA1: 9382eaf502ccb0c2f0edff2fd79e4ac1b233ba04 SHA256: b45ad744765087ee86de9e6e9dbe18325039f14bd58d104bcff5e3675bc9dd68 SHA512: 7f790f415300e7f5da94d59a2216998cb73536208f9305e1b4d2ac3c8ffbcc3ab43b0a2b1fd30682fb9226ec0bc0afc76d034b328e54ef23bad1dd2bda44cdc3 Homepage: https://cran.r-project.org/package=HiResTEC Description: CRAN Package 'HiResTEC' (Non-Targeted Fluxomics on High-Resolution Mass-Spectrometry Data) Identifying labeled compounds in a 13C-tracer experiment in non-targeted fashion is a cumbersome process. 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Beaulieu and O'Meara (2016) . Package: r-cran-histdawass Architecture: arm64 Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2328 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-class, r-cran-factominer, r-cran-ggplot2, r-cran-ggridges, r-cran-histogram, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-histdawass_1.0.8-1.ca2604.1_arm64.deb Size: 1868328 MD5sum: ef13f9606198ab8157823e1e6ad938e0 SHA1: ee15e12a6dddca1904e9a96110e783e390607b94 SHA256: a4204a8188c470ed3064ec2958ebd80176d274331bbf5ac311e48241c04ef88c SHA512: ddd61d8cef0e1d2bd83295e350146ba8fda4321d8c84f5262fe25114b0c80f539430ae2e25cebbe818afd585951ec609d929b96bab962afd8e386121e1f50b6e Homepage: https://cran.r-project.org/package=HistDAWass Description: CRAN Package 'HistDAWass' (Histogram-Valued Data Analysis) In the framework of Symbolic Data Analysis, a relatively new approach to the statistical analysis of multi-valued data, we consider histogram-valued data, i.e., data described by univariate histograms. 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This package supports a longitudinal hierarchical model to borrow historical control data from other studies to better characterize the control response of the current study. It also quantifies the amount of borrowing through longitudinal benchmark models (independent and pooled). The hierarchical model approach to historical borrowing is discussed by Viele et al. (2013) . Package: r-cran-hitandrun Architecture: arm64 Version: 0.5-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 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_arm64.deb Size: 116344 MD5sum: 0f9f866118a9cc8ebdca28d79bd3206b SHA1: 37a191254b857a835485a785b5fa3539e27d19d5 SHA256: c7682e8fdfb622736ddde4f81b726ac8b9ae5123c08fd4ebfc80106195bd11a3 SHA512: 7b467604dc67d1d97a92c082c64c4edb269e14b871e8ecdac0d7e072150f9fb55a16571983cdeafa904b46a6c90cea27944818f875e137e3d183bb17d4d0fb67 Homepage: https://cran.r-project.org/package=hitandrun Description: CRAN Package 'hitandrun' ("Hit and Run" and "Shake and Bake" for Sampling Uniformly fromConvex Shapes) The "Hit and Run" Markov Chain Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints, and the "Shake and Bake" method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized weights with arbitrary linear constraints. Tervonen, T., van Valkenhoef, G., Basturk, N., and Postmus, D. (2012) . van Valkenhoef, G., Tervonen, T., and Postmus, D. (2014) . Package: r-cran-hkevp Architecture: arm64 Version: 1.1.6-1.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), 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_arm64.deb Size: 231100 MD5sum: 50fa31084f33581babf95e6e301dfe8e SHA1: 842504e75a9f90cb1ee3683d4673880608ee4c4f SHA256: b22eafa924008bb7cc77d66d5c540bc6e926e5c6442b4684acb09b08f23e9bcb SHA512: b8ee83d0bf7dbb0983fa497ce6d70d940ccb28d2aabfb21df226b65fb80f93e1615803ba60eee883b4c8a8e13c533f271680576c5350ac4d98c10da804086b51 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. 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The tools include not only leverage and traditional deletion diagnostics (Cook's distance, covratio, covtrace, and MDFFITS) but also convenience functions and graphics for residual analysis. Models can be fit using either lmer in the 'lme4' package or lme in the 'nlme' package. Package: r-cran-hlsm Architecture: arm64 Version: 0.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: libc6 (>= 2.29), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-coda, r-cran-igraph, r-cran-abind Filename: pool/dists/resolute/main/r-cran-hlsm_0.9.2-1.ca2604.1_arm64.deb Size: 212966 MD5sum: 827b429e0b3b772541ae93bb0e575e4f SHA1: 939ad3c6482c755b2666fc853ebc3186f6546561 SHA256: 9b4f4fa9ecd7f06cdf1a8ffea69bedc1fc08b26bf1c16170e68bd7b881b2f670 SHA512: d3f61f5c2915501a41ca2ba25ec9323e19d5e169b259fb029384da08dcc222043aacc21b8e5016d31e6339ffe2f195fa85a91826c580ca5993e0dd92db6f6c76 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). 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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: arm64 Version: 2.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1670 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_arm64.deb Size: 743372 MD5sum: abebe8d83d34d7e16622c5275c904be9 SHA1: 38d217da1140930568cc9bfcf31bd0d6aa0fb340 SHA256: 7c9990fe2a61027875dd0cfcc10ee71d629649a67c7eb891cbbdba48ff80bcce SHA512: 9ef4b22c594484a8a58e8c229b2e4e84c9f20cde817194808864685120c6ceb1f35af5251f8ff2601734e90b0f20e0b8ec6f2f523256e3831e00b29af5852033 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: arm64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10333 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_arm64.deb Size: 4742296 MD5sum: 9df7d06d185f5f7b74b6ee1a753f8424 SHA1: 7fda050fdf3a6121af11321ef21170600c1b8368 SHA256: 354b4fdd669e0c6340e4d9773553f0667d31f1f0faa322b6f890cfd62aaf85df SHA512: 0cea11e17d8218494238ce26bba0be21361c3a6daa1bc40fc66ee67212668ccce710d60954eca409bb685970fb525e4da13239af60609c5217ed9300a4d02c10 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: arm64 Version: 5.2-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3906 Depends: libc6 (>= 2.38), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-cluster, r-cran-rpart, r-cran-nnet, r-cran-foreign, r-cran-gtable, r-cran-gridextra, r-cran-data.table, r-cran-htmltable, r-cran-viridislite, r-cran-htmltools, r-cran-base64enc, r-cran-colorspace, r-cran-rmarkdown, r-cran-knitr, r-cran-formula Suggests: r-cran-survival, r-cran-qreport, r-cran-acepack, r-cran-chron, r-cran-rms, r-cran-mice, r-cran-rstudioapi, r-cran-tables, r-cran-plotly, r-cran-rlang, r-cran-vgam, r-cran-leaps, r-cran-pcapp, r-cran-digest, r-cran-polspline, r-cran-abind, r-cran-kableextra, r-cran-rio, r-cran-lattice, r-cran-latticeextra, r-cran-gt, r-cran-sparkline, r-cran-jsonlite, r-cran-htmlwidgets, r-cran-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_arm64.deb Size: 3605536 MD5sum: 95d9580f650e80cd77b856cd4814a4b5 SHA1: f01d316342c43cc956d2c3eaab047922b70f0e0a SHA256: 71b7229fcea7ff20e61e3f2552f9eb096061fa610036896415c65ed30059c73d SHA512: 22495a7c9cc0139c41db531ceda2625409f47b6628fafa53987efc70555a82c881ff6c6a941d08a4980d788a1397811ca7e6701520bae7bd9d27879bdabf6c28 Homepage: https://cran.r-project.org/package=Hmisc Description: CRAN Package 'Hmisc' (Harrell Miscellaneous) Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, simulation, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, recoding variables, caching, simplified parallel computing, encrypting and decrypting data using a safe workflow, general moving window statistical estimation, and assistance in interpreting principal component analysis. Package: r-cran-hmm.discnp Architecture: arm64 Version: 3.0-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 859 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_arm64.deb Size: 685912 MD5sum: fa93d81deafdb6afb6496abbd051aab6 SHA1: 663b8e1a787c3a83851a834d92d8c8ea2da91975 SHA256: 49a7f49171103e65f3c271c79d51908f3650070922a0b2bb1954aeb43db7051e SHA512: 39021944e0b2c6dd63d2b83cabe0b778d2a9714f21e8a600e44efc5e8e37d58d7237c45586245f58f6481977ca438d09011d6e0f11abdb68610e18f91c3ef9ae 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: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 211 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_arm64.deb Size: 117710 MD5sum: b80acff3d7fa0b12b1a1020564eb2f6b SHA1: 32fc4f63995ad6a5299d1857fe90ba7d046c5ae1 SHA256: 4d9bb7f37601670ad656656b14c0117d8e168d4a795aea21beec9c45372b1feb SHA512: 1491fb1ef62de02d07f4e41f10ece2644881d5104c5b517a86329c62904ac52cb4353a45b5d78dd238ad245a38db3a334c5e6bfe36b997a9d84a2a5365cc2c09 Homepage: https://cran.r-project.org/package=HMMextra0s Description: CRAN Package 'HMMextra0s' (Hidden Markov Models with Extra Zeros) Contains functions for hidden Markov models with observations having extra zeros as defined in the following two publications, Wang, T., Zhuang, J., Obara, K. and Tsuruoka, H. (2016) ; Wang, T., Zhuang, J., Buckby, J., Obara, K. and Tsuruoka, H. (2018) . The observed response variable is either univariate or bivariate Gaussian conditioning on presence of events, and extra zeros mean that the response variable takes on the value zero if nothing is happening. Hence the response is modelled as a mixture distribution of a Bernoulli variable and a continuous variable. That is, if the Bernoulli variable takes on the value 1, then the response variable is Gaussian, and if the Bernoulli variable takes on the value 0, then the response is zero too. This package includes functions for simulation, parameter estimation, goodness-of-fit, the Viterbi algorithm, and plotting the classified 2-D data. Some of the functions in the package are based on those of the R package 'HiddenMarkov' by David Harte. This updated version has included an example dataset and R code examples to show how to transform the data into the objects needed in the main functions. We have also made changes to increase the speed of some of the functions. Package: r-cran-hmmhsmm Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 619 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 495884 MD5sum: d268b9328bf11f428e1ad98dc872bda0 SHA1: 60d6e22c6690af015f50d3cf1130577f3276a5b6 SHA256: ba8a4025c0cb52a1ed474b7d496b5b64d13dc276c01815cd468ce7c3cf64df58 SHA512: b92999b724a98266b623a6bf822a6f4fc1565c236a923c44c60e064575e141f4a2714d7c771abf9fe672231a642a16e7fd11c0c08050d026ecbf391e31a0a65b 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: arm64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3419 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_arm64.deb Size: 1568648 MD5sum: 51260522d7c594e222d4e5e66ebc8162 SHA1: 84ea91422a57b6a310bc6f43df5a1e5e642b1733 SHA256: 85aac7cea6d4d75b05c5cd8e2c6b70d0cadb79a81bd819782b080db1f3c1a7fb SHA512: 00a58d487ff8ff6b0c6bd12b4f181ec121def7521ce4fc31e66b88345b0a5cd76a7c7739702ca00d7b4e8b5fc088cd96c14b61b2f44425b32463b675ad8607c4 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: arm64 Version: 1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 364 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 209890 MD5sum: 6f4ab6d1f3d5c0575e82e4ae7291b8a7 SHA1: 55f438f4565950c6904846a99f7326c0cc560c25 SHA256: dd058b5057cddf7d9dcacaa59776cf7849b97e55e533df176362bc0e16869ee7 SHA512: db3a724dcf9ff5defc7bc1e6989d7fd0c7bdebdf03e9bc42b4718eaf0344fc75be7aa6ca29b0513643a2ba3c4e267f29af6487a35cca60062bdd7bb74a2e1538 Homepage: https://cran.r-project.org/package=hommel Description: CRAN Package 'hommel' (Methods for Closed Testing with Simes Inequality, in ParticularHommel's Method) Provides methods for closed testing using Simes local tests. In particular, calculates adjusted p-values for Hommel's multiple testing method, and provides lower confidence bounds for true discovery proportions. A robust but more conservative variant of the closed testing procedure that does not require the assumption of Simes inequality is also implemented. The methods have been described in detail in Goeman et al (Biometrika 106, 841-856, 2019). Package: r-cran-hopit Architecture: arm64 Version: 0.11.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 845 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_arm64.deb Size: 639276 MD5sum: 892a6fd8b963c9b82b461cb778b753aa SHA1: 7e7a3e8e2c2d2fc1734f15676b91a5293360b2e4 SHA256: 214c73bf343c7e5fe0301c545866da27fc7f6db80c9252ffcb3797763d8b6fa5 SHA512: 5b0d580c84a7c4254698c3466af34df8e46e50f867fa2fa9db3e3c49c8a8f781d1c74da7383e9eac4a44633244d0ef7e191c3907e700759566d34a46efd71658 Homepage: https://cran.r-project.org/package=hopit Description: CRAN Package 'hopit' (Hierarchical Ordered Probit Models with Application to ReportingHeterogeneity) Self-reported health, happiness, attitudes, and other statuses or perceptions are often the subject of biases that may come from different sources. For example, the evaluation of an individual’s own health may depend on previous medical diagnoses, functional status, and symptoms and signs of illness; as on well as life-style behaviors, including contextual social, gender, age-specific, linguistic and other cultural factors (Jylha 2009 ; Oksuzyan et al. 2019 ). The hopit package offers versatile functions for analyzing different self-reported ordinal variables, and for helping to estimate their biases. Specifically, the package provides the function to fit a generalized ordered probit model that regresses original self-reported status measures on two sets of independent variables (King et al. 2004 ; Jurges 2007 ; Oksuzyan et al. 2019 ). The first set of variables (e.g., health variables) included in the regression are individual statuses and characteristics that are directly related to the self-reported variable. In the case of self-reported health, these could be chronic conditions, mobility level, difficulties with daily activities, performance on grip strength tests, anthropometric measures, and lifestyle behaviors. The second set of independent variables (threshold variables) is used to model cut-points between adjacent self-reported response categories as functions of individual characteristics, such as gender, age group, education, and country (Oksuzyan et al. 2019 ). The model helps to adjust for specific socio-demographic and cultural differences in how the continuous latent health is projected onto the ordinal self-rated measure. The fitted model can be used to calculate an individual predicted latent status variable, a latent index, and standardized latent coefficients; and makes it possible to reclassify a categorical status measure that has been adjusted for inter-individual differences in reporting behavior. Package: r-cran-houba Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1327 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_arm64.deb Size: 528028 MD5sum: 48eac26b1b5c992eb9751bf379849528 SHA1: f28cbb5be8adb024134484ed061b683b0bad6349 SHA256: 8c738471370ededab27e6a60c24776723b4c96170b2b9f480b2e63927c0b1ace SHA512: 85f584591ec079dd7d934b4bd9bd0ae206eacae939296a8c8506dc7a77c05bfafaa963d503b6303e2fe4ca03f5f79876d01f6cf61fde79b11454386e9fe8249b Homepage: https://cran.r-project.org/package=houba Description: CRAN Package 'houba' (Manipulation of (Large) Memory-Mapped Objects (Vectors, Matricesand Arrays)) Manipulate data through memory-mapped files, as vectors, matrices or arrays. Basic arithmetic functions are implemented, but currently no matrix arithmetic. Can write and read descriptor files for compatibility with the 'bigmemory' package. Package: r-cran-hpa Architecture: arm64 Version: 1.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1611 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-ggplot2, r-cran-mvtnorm, r-cran-titanic, r-cran-sampleselection, r-cran-ga Filename: pool/dists/resolute/main/r-cran-hpa_1.3.4-1.ca2604.1_arm64.deb Size: 671162 MD5sum: 46303e08a46891cac359ce242302bf7b SHA1: fa3d17d5505d32e6892f84df88292c227a1cfdc3 SHA256: 095918eaaa2d1b9ca7088710acc75adca230e6f8acee0f615df025956dd88194 SHA512: 1cbf45fe82516aec835f62e0910465baed49fe6fd945256697e84509cf032e92f3f92a7e1ad622910019c822e769376bca7b3cdb8994cd65fc8cba63840a93d4 Homepage: https://cran.r-project.org/package=hpa Description: CRAN Package 'hpa' (Distributions Hermite Polynomial Approximation) Multivariate conditional and marginal densities, moments, cumulative distribution functions as well as binary choice and sample selection models based on the Hermite polynomial approximation which was proposed and described by A. Gallant and D. W. Nychka (1987) . Package: r-cran-hqreg Architecture: arm64 Version: 1.4-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-hqreg_1.4-1-1.ca2604.1_arm64.deb Size: 99902 MD5sum: 645dd51812079a114072ae211095017d SHA1: 819830fe54e225a1046dd1f0fa2d9e15bac61a50 SHA256: 086a832e7cfaec64624c1cdc956bfd81fca7b48eb8c0c32f69af49e330d808cc SHA512: 59eabe3c41972b5515e62965492db11ee988bd1675fd06e971e9feb7e2d7e89d43c6bc2effc1ead5184e2cbf5c3d4defc4c82b941bdb9f391df9272c252f77d6 Homepage: https://cran.r-project.org/package=hqreg Description: CRAN Package 'hqreg' (Regularization Paths for Lasso or Elastic-Net Penalized HuberLoss Regression and Quantile Regression) Offers efficient algorithms for fitting regularization paths for lasso or elastic-net penalized regression models with Huber loss, quantile loss or squared loss. Reference: Congrui Yi and Jian Huang (2017) . Package: r-cran-hrqglas Architecture: arm64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-hrqglas_1.1.2-1.ca2604.1_arm64.deb Size: 65904 MD5sum: 77893edc263813c46dc325aae45c7335 SHA1: 378362617b2d8c6c807aaee7ea9c3305a42ab85f SHA256: a6dca14a59d063c4b802921a8ba67ba87a501560c0e628e478c67a1acb015893 SHA512: 137f8b2bffc0ae52590a06351ba1d97239290f4fa9c22402a8936a6479a067846a403cccad3aa546fa3501ac04499ebe17aa6d54a4c48a2d8b873fdf18cb1b07 Homepage: https://cran.r-project.org/package=hrqglas Description: CRAN Package 'hrqglas' (Group Variable Selection for Quantile and Robust Mean Regression) A program that conducts group variable selection for quantile and robust mean regression (Sherwood and Li, 2022). The group lasso penalty (Yuan and Lin, 2006) is used for group-wise variable selection. Both of the quantile and mean regression models are based on the Huber loss. Specifically, with the tuning parameter in the Huber loss approaching to 0, the quantile check function can be approximated by the Huber loss for the median and the tilted version of Huber loss at other quantiles. Such approximation provides computational efficiency and stability, and has also been shown to be statistical consistent. Package: r-cran-hrt Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: libc6 (>= 2.17), 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-compquadform, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-hrt_1.0.2-1.ca2604.1_arm64.deb Size: 158388 MD5sum: e057ea97c66830461a3e22c096b483c4 SHA1: 728ab48775500cb87cb60e878ea445d9efa1feab SHA256: f34fb55dbeefa6e510893beea8d7a41f2448673bac65f3fbaef4e35233478158 SHA512: 3f170fb4fa300f2f7975e0187f941629fcc44ef0a1655c6c52f1e7693f7ba1c4139601d3aa495635bb4741a05beb2f984106a440ef877a00ce1621c11cbb759f Homepage: https://cran.r-project.org/package=hrt Description: CRAN Package 'hrt' (Heteroskedasticity Robust Testing) Functions for testing affine hypotheses on the regression coefficient vector in regression models with heteroskedastic errors: (i) a function for computing various test statistics (in particular using HC0-HC4 covariance estimators based on unrestricted or restricted residuals); (ii) a function for numerically approximating the size of a test based on such test statistics and a user-supplied critical value; and, most importantly, (iii) a function for determining size-controlling critical values for such test statistics and a user-supplied significance level (also incorporating a check of conditions under which such a size-controlling critical value exists). The three functions are based on results in Poetscher and Preinerstorfer (2021) "Valid Heteroskedasticity Robust Testing" , which will appear as . Package: r-cran-hrtnomaly Architecture: arm64 Version: 25.11.22-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 743 Depends: libc6 (>= 2.17), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tidyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-cellwise Filename: pool/dists/resolute/main/r-cran-hrtnomaly_25.11.22-1.ca2604.1_arm64.deb Size: 579966 MD5sum: 2e9d924b6dcdc032d112385a2d37309a SHA1: 9ebdcc4629c8352478c6caf9e7ed1475ac3cb90a SHA256: b63d7e7687422452ddb9a2a7effb2da2d9b5ab8c047f4a058728dd1b6e94d894 SHA512: 2f6e068048760d4853f8f3cdd6aa18b3a2c690b78fe200353df0912d84f138c6ea1afc1cd1296a22d63162b992e59f565b8413298eb703f652f9fc8251b92c5f Homepage: https://cran.r-project.org/package=HRTnomaly Description: CRAN Package 'HRTnomaly' (Historical, Relational, and Tail Anomaly-Detection Algorithms) The presence of outliers in a dataset can substantially bias the results of statistical analyses. To correct for outliers, micro edits are manually performed on all records. A set of constraints and decision rules is typically used to aid the editing process. However, straightforward decision rules might overlook anomalies arising from disruption of linear relationships. Computationally efficient methods are provided to identify historical, tail, and relational anomalies at the data-entry level (Sartore et al., 2024; ). A score statistic is developed for each anomaly type, using a distribution-free approach motivated by the Bienaymé-Chebyshev's inequality, and fuzzy logic is used to detect cellwise outliers resulting from different types of anomalies. Each data entry is individually scored and individual scores are combined into a final score to determine anomalous entries. In contrast to fuzzy logic, Bayesian bootstrap and a Bayesian test based on empirical likelihoods are also provided as studied by Sartore et al. (2024; ). These algorithms allow for a more nuanced approach to outlier detection, as it can identify outliers at data-entry level which are not obviously distinct from the rest of the data. --- This research was supported in part by the U.S. Department of Agriculture, National Agriculture Statistics Service. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA, or US Government determination or policy. Package: r-cran-hsar Architecture: arm64 Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1020 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-spdep, r-cran-spatialreg, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-matrix, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-sdsfun, r-cran-sf, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-hsar_0.6.0-1.ca2604.1_arm64.deb Size: 531822 MD5sum: 7d36909189c5e25f08869f969bdf5c40 SHA1: 478d7f02022958d86d87c4d5e67c216a8cfde092 SHA256: 6aee3836975eafe5c687d98ffa8b517abe975e9a312dc8aa88442c810ff5b50e SHA512: 0a254904f38e5575f11108a6c5f0cdca9435518057e831685b423f1470170e89abf4efde3103352707c8ffeb83a4b00e433e9cbef8b7ba27845e308122c7b58e Homepage: https://cran.r-project.org/package=HSAR Description: CRAN Package 'HSAR' (Hierarchical Spatial Autoregressive Model) A Hierarchical Spatial Autoregressive Model (HSAR), based on a Bayesian Markov Chain Monte Carlo (MCMC) algorithm (Dong and Harris (2014) ). The creation of this package was supported by the Economic and Social Research Council (ESRC) through the Applied Quantitative Methods Network: Phase II, grant number ES/K006460/1. Package: r-cran-hsdm Architecture: arm64 Version: 1.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2093 Depends: libc6 (>= 2.29), libgsl28 (>= 2.8+dfsg), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda Suggests: r-cran-knitr, r-cran-raster, r-cran-sp, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/resolute/main/r-cran-hsdm_1.4.4-1.ca2604.1_arm64.deb Size: 1308236 MD5sum: 9c19e10963e8d705f885ce132f496326 SHA1: f5bd7b57828e3ad46e1160c067d7b4f400981e72 SHA256: 0109cf6961ab386f6f734ffcc8002423459722855eaf408247e652b4b283b088 SHA512: 074e446a98959cc9e6530167174f552ebe27147c88d85c5e46b3676d8ae983662487ca6ec744e052ee112520b941e97362a836e85cbfb2e6c8aad537d1f65ca0 Homepage: https://cran.r-project.org/package=hSDM Description: CRAN Package 'hSDM' (Hierarchical Bayesian Species Distribution Models) User-friendly and fast set of functions for estimating parameters of hierarchical Bayesian species distribution models (Latimer and others 2006 ). Such models allow interpreting the observations (occurrence and abundance of a species) as a result of several hierarchical processes including ecological processes (habitat suitability, spatial dependence and anthropogenic disturbance) and observation processes (species detectability). Hierarchical species distribution models are essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results. Package: r-cran-hsphase Architecture: arm64 Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1864 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-snowfall, r-cran-rcpp, r-cran-gdata, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-hsphase_3.0.0-1.ca2604.1_arm64.deb Size: 1097160 MD5sum: c43dc9893a183b32e54a97509f245d6c SHA1: c99162054fb522bd801d57289c87ff12e35de1d5 SHA256: d62880a5850796a5c7f48a0ce1a21c301d1d619eb8b20023b1dcb3a04ca6f601 SHA512: 19b084ff3ce400fd7eff4cce3e70ba847064ec18ea517616903bf62471f57d5e3b0c7d9e7eff574e8ebea19bb8b8215e3526550bb7211a52796c8d49ef70ae17 Homepage: https://cran.r-project.org/package=hsphase Description: CRAN Package 'hsphase' (Phasing, Pedigree Reconstruction, Sire Imputation andRecombination Events Identification of Half-sib Families UsingSNP Data) Identification of recombination events, haplotype reconstruction, sire imputation and pedigree reconstruction using half-sib family SNP data. Package: r-cran-hsrecombi Architecture: arm64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 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-hsphase, r-cran-dplyr, r-cran-data.table, r-cran-rlist, r-cran-quadprog, r-cran-curl, r-cran-matrix, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-hsrecombi_1.1.1-1.ca2604.1_arm64.deb Size: 152326 MD5sum: 2a3a76f630181b311e762259c06c1aa1 SHA1: 67c6c1d4d520f3da73d15838836c45b47bc7f11a SHA256: b601524478c9df160833d756115db61761766b98c544b2c012e377eccd2f0d0e SHA512: 618d7b83347367ecca4b5ca33f3db4d75967229e016d0cf3060470bdc89552c48bbc744f4de814fb172d02b0d764685988ff754daf8bac27b7d58577185a736b Homepage: https://cran.r-project.org/package=hsrecombi Description: CRAN Package 'hsrecombi' (Estimation of Recombination Rate and Maternal LD in Half-Sibs) Paternal recombination rate and maternal linkage disequilibrium (LD) are estimated for pairs of biallelic markers such as single nucleotide polymorphisms (SNPs) from progeny genotypes and sire haplotypes. The implementation relies on paternal half-sib families. If maternal half-sib families are used, the roles of sire/dam are swapped. Multiple families can be considered. For parameter estimation, at least one sire has to be double heterozygous at the investigated pairs of SNPs. Based on recombination rates, genetic distances between markers can be estimated. Markers with unusually large recombination rate to markers in close proximity (i.e. putatively misplaced markers) shall be discarded in this derivation. *A pipeline is available at GitHub* Hampel, Teuscher, Gomez-Raya, Doschoris, Wittenburg (2018) "Estimation of recombination rate and maternal linkage disequilibrium in half-sibs" . Gomez-Raya (2012) "Maximum likelihood estimation of linkage disequilibrium in half-sib families" . Package: r-cran-hsstan Architecture: arm64 Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3583 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-proc, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-hsstan_0.8.2-1.ca2604.1_arm64.deb Size: 1070626 MD5sum: 0b6cebca17d5d88cf931cf97954951db SHA1: cfefac83554763620fd034206317dc3c78fa194a SHA256: 654e656fbefc6dbae6070b610b58dc66bacb07e599c51e7c699af3387ef210a0 SHA512: 70633ac6a7b9c30420e653c277cb8765e61feb0e19009133efa16df2c1423a7a048399e542a06e5287a270729868c1fdd5d25340241cb8b434d0dd57ccba250a Homepage: https://cran.r-project.org/package=hsstan Description: CRAN Package 'hsstan' (Hierarchical Shrinkage Stan Models for Biomarker Selection) Linear and logistic regression models penalized with hierarchical shrinkage priors for selection of biomarkers (or more general variable selection), which can be fitted using Stan (Carpenter et al. (2017) ). It implements the horseshoe and regularized horseshoe priors (Piironen and Vehtari (2017) ), as well as the projection predictive selection approach to recover a sparse set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2020) ). 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For more information, see Brand, Xu, Koch, and Geraldo (2021) . Our current package first started as a fork of the 'causalTree' package on 'GitHub' and we greatly appreciate the authors for their extremely useful and free package. Package: r-cran-htlr Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2497 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-bcbcsf, r-cran-glmnet, r-cran-magrittr, r-cran-rcpparmadillo Suggests: r-cran-ggplot2, r-cran-corrplot, r-cran-testthat, r-cran-bayesplot, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-htlr_1.0-1.ca2604.1_arm64.deb Size: 1717746 MD5sum: 0d5262503da62d3d2a42b2f1ae9634fe SHA1: 04b4c222d8747111c9c0a1aa030dbf876c3caaf1 SHA256: 6758a74f53f52e7714ad1625c8d0668d15e35f175c7ae212b0bc0daf4c4e45a9 SHA512: cf21ef4a0ee584172aae55492463e7638a0bbc675e8bafcb28ec12f9b6da8c5dc8e72e8b199422167fdacc6eaf0757688401a75b4616c5ce4eb9f3254df8c906 Homepage: https://cran.r-project.org/package=HTLR Description: CRAN Package 'HTLR' (Bayesian Logistic Regression with Heavy-Tailed Priors) Efficient Bayesian multinomial logistic regression based on heavy-tailed (hyper-LASSO, non-convex) priors. 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The available forecast methods include bottom-up, top-down, optimal combination reconciliation (Hyndman et al. 2011) , and trace minimization reconciliation (Wickramasuriya et al. 2018) . 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Package: r-cran-htt Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 854 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_arm64.deb Size: 416240 MD5sum: f73301fe8cd0f217fdf20af2b61e4bf5 SHA1: f382e396084b9db0212972259853c5b9d933965e SHA256: dbe93538762fc91bb8ff28b54ee59e5a185c924e076b921b92b19d91fca179e1 SHA512: 35aec50671d102241477c49bd54c7d7fe3295d49caf38061b284eee36ee542add1f5c4b6f4bbd15a793a3303e4dda79d687806684e2cb95ed7244349eb421e02 Homepage: https://cran.r-project.org/package=HTT Description: CRAN Package 'HTT' (Hypothesis Testing Tree) A novel decision tree algorithm in the hypothesis testing framework. The algorithm examines the distribution difference between two child nodes over all possible binary partitions. The test statistic of the hypothesis testing is equivalent to the generalized energy distance, which enables the algorithm to be more powerful in detecting the complex structure, not only the mean difference. It is applicable for numeric, nominal, ordinal explanatory variables and the response in general metric space of strong negative type. The algorithm has superior performance compared to other tree models in type I error, power, prediction accuracy, and complexity. Package: r-cran-httk Architecture: arm64 Version: 2.7.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4945 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-msm, r-cran-data.table, r-cran-survey, r-cran-mvtnorm, r-cran-truncnorm, r-cran-magrittr, r-cran-purrr, r-cran-rdpack, r-cran-ggplot2, r-cran-dplyr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-gplots, r-cran-scales, r-cran-envstats, r-cran-mass, r-cran-rcolorbrewer, r-cran-stringr, r-cran-reshape, r-cran-viridis, r-cran-gmodels, r-cran-colorspace, r-cran-cowplot, r-cran-ggrepel, r-cran-forcats, r-cran-smatr, r-cran-gridextra, r-cran-readxl, r-cran-ks, r-cran-testthat, r-cran-ggpubr, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-httk_2.7.4-1.ca2604.1_arm64.deb Size: 4618838 MD5sum: 8134eb8adcd517e03887117ecb1e9ec7 SHA1: 1a95e4d8d248017e435a8a70ab7e03ff7e336cca SHA256: 510283cc22b730e9799f5df300c936283050561869e7ca3c3f12711c0f58fab7 SHA512: dac32e3235185595d112c19cbafdcf599bf18803128d210022aa78b4ff552b4f486c6ae4559e55c72b3abe76ddab750fdb5287ed9e64af480f55537b758f5247 Homepage: https://cran.r-project.org/package=httk Description: CRAN Package 'httk' (High-Throughput Toxicokinetics) Pre-made models that can be rapidly tailored to various chemicals and species using chemical-specific in vitro data and physiological information. These tools allow incorporation of chemical toxicokinetics ("TK") and in vitro-in vivo extrapolation ("IVIVE") into bioinformatics, as described by Pearce et al. (2017) (). Chemical-specific in vitro data characterizing toxicokinetics have been obtained from relatively high-throughput experiments. The chemical-independent ("generic") physiologically-based ("PBTK") and empirical (for example, one compartment) "TK" models included here can be parameterized with in vitro data or in silico predictions which are provided for thousands of chemicals, multiple exposure routes, and various species. High throughput toxicokinetics ("HTTK") is the combination of in vitro data and generic models. We establish the expected accuracy of HTTK for chemicals without in vivo data through statistical evaluation of HTTK predictions for chemicals where in vivo data do exist. The models are systems of ordinary differential equations that are developed in MCSim and solved using compiled (C-based) code for speed. A Monte Carlo sampler is included for simulating human biological variability (Ring et al., 2017 ) and propagating parameter uncertainty (Wambaugh et al., 2019 ). Empirically calibrated methods are included for predicting tissue:plasma partition coefficients and volume of distribution (Pearce et al., 2017 ). These functions and data provide a set of tools for using IVIVE to convert concentrations from high-throughput screening experiments (for example, Tox21, ToxCast) to real-world exposures via reverse dosimetry (also known as "RTK") (Wetmore et al., 2015 ). Package: r-cran-httpgd Architecture: arm64 Version: 2.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1583 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-unigd, r-cran-cpp11, r-cran-asioheaders Suggests: r-cran-testthat, r-cran-xml2, r-cran-knitr, r-cran-rmarkdown, r-cran-covr, r-cran-future, r-cran-httr, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-httpgd_2.1.4-1.ca2604.1_arm64.deb Size: 548326 MD5sum: 937fc50698f7c1b9750aa9c182eaede9 SHA1: 2678a6f33a667632c424f078539f933adc6bb2b7 SHA256: 774bb79c3684f090fc0632de756ebcdcc9effae042fc37bd405a204782ead035 SHA512: 8cfdda56fcfe8281c587f335b63aa9c0f62146f88f12a99855a3b193c056d37455e12d67e551c3c3340dec43751a7541a5a242f245baf7ad7506471b1799bba0 Homepage: https://cran.r-project.org/package=httpgd Description: CRAN Package 'httpgd' (A 'HTTP' Server Graphics Device) A graphics device for R that is accessible via network protocols. 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Package: r-cran-huge Architecture: arm64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2101 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libstdc++6 (>= 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_arm64.deb Size: 1684656 MD5sum: 7ccfc3b0762d8e9618dbd8b261de2846 SHA1: 8f5ed58c208bbd971c620e62b5ce5175fb61d26c SHA256: fde3dd8ad8f038e5a4caa00b9774dfc6cd323f4b84d5e9ccd10af7165be6bbdd SHA512: 8158c3d1d0e63e967606cd2d016ac1177e874d1bca3a797c61bf3d03115971dd76fc341ccc53710772f795204fe44f602ac34473874e792fc61e1c3c33f6438b 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: arm64 Version: 3.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3139 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_arm64.deb Size: 998164 MD5sum: 651ebde1da8142434750a931e6fe5fbe SHA1: edcfd9bd38f195124eb2d2d2af40b317d749f571 SHA256: a1fec37c187ce3e249ca6b02d2114927a3405d06f8f03ebb78bda34d63a21a6b SHA512: 919ff250f3833927d0e8c0abe069c7ddd82d9ff98d49086f9c50490b92bdba83693a2f31097ce97f6af08e563ba269524cb0e97927f2259df6ee1e25379a929a 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: arm64 Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2458 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_arm64.deb Size: 948222 MD5sum: de3c584808daad4178115c97b8d5fda6 SHA1: a71f39039e5bb1dc1cc5ef5f4797b818e49a7a06 SHA256: d37afa869171689f69432113e7b8879139ebfe6dfc6e5a588cc53983121831b5 SHA512: 71c8c73c9358045479fee57c4d12af1141841b27aa1c278e07114475ad9336106e92db96a216159ce837285c1fd404ef74af9fa1e2925c62ca1e131e4bfef90f Homepage: https://cran.r-project.org/package=hwep Description: CRAN Package 'hwep' (Hardy-Weinberg Equilibrium in Polyploids) Inference concerning equilibrium and random mating in autopolyploids. Methods are available to test for equilibrium and random mating at any even ploidy level (>2) in the presence of double reduction at biallelic loci. For autopolyploid populations in equilibrium, methods are available to estimate the degree of double reduction. We also provide functions to calculate genotype frequencies at equilibrium, or after one or several rounds of random mating, given rates of double reduction. The main function is hwefit(). This material is based upon work supported by the National Science Foundation under Grant No. 2132247. The opinions, findings, and conclusions or recommendations expressed are those of the author and do not necessarily reflect the views of the National Science Foundation. For details of these methods, see Gerard (2023a) and Gerard (2023b) . 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To cite in publications please use Hankin 2017 , and for Generalized Plackett-Luce likelihoods use Hankin 2024 . 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Uses stochastic geometry approach to high-dimensional kernel density estimation, support vector machine delineation, and convex hull generation. Applications include modeling trait and niche hypervolumes and species distribution modeling. Package: r-cran-hystar Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 138146 MD5sum: cb44e647bee66126ff4be3e9d052cfb1 SHA1: 185ffbccfec5a7f65aea3ea5f56853284c967536 SHA256: b2e5231d1e87102329aacb5c9d514c83a7605a04aaeef2ab5bf734005bffb98d SHA512: 5f9d03429cd4041f58d143aa20b786e207ca15ce4dfd880ee8a0326083c127f2dbf0e311046a6bda6677664d91c445ebfa5404e4c94a5a7836e330ab7461f997 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: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 294 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_arm64.deb Size: 113964 MD5sum: d925484d8bbe4c4e01710577321a0663 SHA1: 07c309355274ea6bded13a82127c64532aa2a603 SHA256: fec312dac590980b5d45f25f71f76561d7475ca0ab0cd73c4a2fe1708446a0cb SHA512: 94d5ea3c95e073ac8df9640eea75fe1d87cb75fe232408d8b574d32e6ee1027d7c6bb430bc02a300880efa5bdc3686e7c7432daf3a96329d96ec889a1ad2393f 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: arm64 Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 640 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-pander, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-iapws95_1.2.5-1.ca2604.1_arm64.deb Size: 343480 MD5sum: 9f498fe923e6acf8cf827e482ca8643d SHA1: e4b3e8e14bf450bf56eb0c9e0d145ecfb38594e8 SHA256: 44f7fe8e02b241ef902da863ea0f4b10bd5204cc10a5059adfbcbfc02601394f SHA512: 30175dc5c100baefa05980d1308807cdfdb03b31ebf982c99e9a427a5d171ea2ca5e1e6c67e172614d15afc51184643b755f52bbc0f998d7c0964e8b2ecc07ac Homepage: https://cran.r-project.org/package=IAPWS95 Description: CRAN Package 'IAPWS95' (Thermophysical Properties of Water and Steam) An implementation of the International Association for the Properties of Water (IAPWS) Formulation 1995 for the Thermodynamic Properties of Ordinary Water Substance for General and Scientific Use and on the releases for viscosity, conductivity, surface tension and melting pressure. Package: r-cran-iapws Architecture: arm64 Version: 1.2-1.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_arm64.deb Size: 195552 MD5sum: c35d7f0db21d8902eef1ceaae134a290 SHA1: 37a9cae831b8ef957599e1d9b8e7c0ff1501237c SHA256: d3cd38fbaf5d971eca935602d72e3144a2e99bcbc107960bd0cc8589eb1a4320 SHA512: 9747b4ffdeb166834e41fac6a01d6b2cf282eff0c94ec76ad85e7cc07e2b7be438e134ed874321701ed2b9bb5005d2f9336242d966982b4b42c4d182325ad3bf Homepage: https://cran.r-project.org/package=iapws Description: CRAN Package 'iapws' (Formulations of the International Association for the Propertiesof Water and Steam) Implementation of some of the formulations for the thermodynamic and transport properties released by the International Association for the Properties of Water and Steam (IAPWS). More specifically, the releases R1-76(2014), R5-85(1994), R6-95(2018), R7-97(2012), R8-97, R9-97, R10-06(2009), R11-24, R12-08, R15-11, R16-17(2018), R17-20 and R18-21 at . Package: r-cran-iar Architecture: arm64 Version: 1.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1177 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 689402 MD5sum: 88ca5d0ce0f451d79b84cc18c0561a8d SHA1: 50ff6146bf674f5eb47d4776fe4e75476ada40d6 SHA256: d58164b8b9f4339830324c7e9d39106ad899fe075328b7f870cd0fc390cb1496 SHA512: caf882034e7782a525e40a8f113e47a0ffbabb836077e627c62ead8603b470787e1aeb1966f94e95f033a8a0af3e4701f890040f5e66ed769bf9ee24641b2fc3 Homepage: https://cran.r-project.org/package=iAR Description: CRAN Package 'iAR' (Irregularly Observed Autoregressive Models) Data sets, functions and scripts with examples to implement autoregressive models for irregularly observed time series. The models available in this package are the irregular autoregressive model (Eyheramendy et al.(2018) ), the complex irregular autoregressive model (Elorrieta et al.(2019) ) and the bivariate irregular autoregressive model (Elorrieta et al.(2021) ). Package: r-cran-ibclust Architecture: arm64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 551 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_arm64.deb Size: 339504 MD5sum: df11a898ae4c17107b753e72590e2901 SHA1: 514790e09e22b3d032125705b28ae887f2dc8bfb SHA256: 6687c6d06e9ffff2b2aeb7bfb243b82281863cda982aba50ddd450ccfc2c1c85 SHA512: 04bb07974656b67b161e5e7b967563776552ceddf0f4320941d7f129359823c65f4516152bcece10defa82871c5850b51ead71db8071a379bfc648f4d575e245 Homepage: https://cran.r-project.org/package=IBclust Description: CRAN Package 'IBclust' (Information Bottleneck Methods for Clustering Mixed-Type Data) Implements multiple variants of the Information Bottleneck ('IB') method for clustering datasets containing continuous, categorical (nominal/ordinal) and mixed-type variables. The package provides deterministic, agglomerative, generalized, and standard 'IB' clustering algorithms that preserve relevant information while forming interpretable clusters. The Deterministic Information Bottleneck is described in Costa et al. (2026) . The standard 'IB' method originates from Tishby et al. (2000) , the agglomerative variant from Slonim and Tishby (1999) , and the generalized 'IB' from Strouse and Schwab (2017) . Package: r-cran-ibdsegments Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 668 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-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_arm64.deb Size: 340842 MD5sum: cabba79ef07c1ea946756009580c6f70 SHA1: c0030404001647d5ccb13f8e627ed1884c39152b SHA256: b0845da1d7afb093b20978e532f16551b82414200ea8b544861999acf3bbad5a SHA512: 0e72d70ac1b3dbae27c44a97b4bffb20fdbbd6df6b5dab59708633ac9b5ed48fcb4f0520480b134a2667e3ed778102360882d9e82daa52477059663a5f672ea3 Homepage: https://cran.r-project.org/package=ibdsegments Description: CRAN Package 'ibdsegments' (Identity by Descent Probability in Pedigrees) Identity by Descent (IBD) distributions in pedigrees. A Hidden Markov Model is used to compute identity coefficients, simulate IBD segments and to derive the distribution of total IBD sharing and segment count across chromosomes. The methods are applied in Kruijver (2025) . The probability that the total IBD sharing is zero can be computed using the method of Donnelly (1983) . Package: r-cran-ibdsim2 Architecture: arm64 Version: 2.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1747 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1583444 MD5sum: 1908b05612b00168a4b4b029658b2b4f SHA1: 81d3abb1a62dc287c08afb18e3a4f7e44c55d1f2 SHA256: e97d8fbec93a39a93bea3ccc751b88b5d6d5c04839320a9b1b849da724da9f21 SHA512: b364dffa4bb7a680c15789e775072850ec243bd792e2ddfe6300aa4071141b86a5cc07929c1659edd5862f4c5359d4a12d2221ee2c8abbec4429e5936b1f3cf3 Homepage: https://cran.r-project.org/package=ibdsim2 Description: CRAN Package 'ibdsim2' (Simulation of Chromosomal Regions Shared by Family Members) Simulation of segments shared identical-by-descent (IBD) by pedigree members. Using sex specific recombination rates along the human genome (Halldorsson et al. (2019) ), phased chromosomes are simulated for all pedigree members. Applications include calculation of realised relatedness coefficients and IBD segment distributions. 'ibdsim2' is part of the 'pedsuite' collection of packages for pedigree analysis. A detailed presentation of the 'pedsuite', including a separate chapter on 'ibdsim2', is available in the book 'Pedigree analysis in R' (Vigeland, 2021, ISBN:9780128244302). A 'Shiny' app for visualising and comparing IBD distributions is available at . Package: r-cran-ibm Architecture: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 145 Depends: libc6 (>= 2.17), 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-ibm_0.3.0-1.ca2604.1_arm64.deb Size: 65792 MD5sum: 4b86b2ace9f4148cbbf1d2fbfdffca8e SHA1: d4ca27f7de81fd10e2b465cbd91086ad610d7b37 SHA256: c9dcff2dd38152e561dd077f70fc2efa080ad100688d127b7e80cc4062f43844 SHA512: fb86a652f89bf36cb03ad0d91157ea23939ea9a378be8008119d2d120d6119852d77b72fb2ff6576e1737fc2cf9246da21dd3df930846787804d748b82137051 Homepage: https://cran.r-project.org/package=ibm Description: CRAN Package 'ibm' (Individual Based Models in R) Implementation of some Individual Based Models (IBMs, sensu Grimm and Railsback 2005) and methods to create new ones, particularly for population dynamics models (reproduction, mortality and movement). The basic operations for the simulations are implemented in Rcpp for speed. Package: r-cran-ibmcraftr Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 55260 MD5sum: 98dc741bcadba3fd2792899b7e462b17 SHA1: d492332d1737974d9429bbd0118b9a508ea30ea3 SHA256: fd89af051b4d78dadc0faa93d5326119207f4448ad34d77b4c6a7922eafc360e SHA512: df1be274a43236c8156f8b68af9801c1f087e9cdf64046ee53ea8ff4b32e9bea37ce394cc87316265bc94b5c28fd0706fb28efac49987f4f9a45a1b3583b7133 Homepage: https://cran.r-project.org/package=ibmcraftr Description: CRAN Package 'ibmcraftr' (Toolkits to Develop Individual-Based Models in InfectiousDisease) It provides a generic set of tools for initializing a synthetic population with each individual in specific disease states, and making transitions between those disease states according to the rates calculated on each timestep. The new version 1.0.0 has C++ code integration to make the functions run faster. It has also a higher level function to actually run the transitions for the number of timesteps that users specify. Additional functions will follow for changing attributes on demographic, health belief and movement. Package: r-cran-ibmpopsim Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4152 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 3621292 MD5sum: 61e4b15a029966416fcbe59e700a1e45 SHA1: bc2fff4aa74fda358dd7c1d1a8a6073a7407d4c5 SHA256: 355986574beab7cd25fca7ca2942d3c081d8084514d146a95e17769ce80f97de SHA512: 8b3be3ae96efe07562b3b719fb15dc0b1c1132c24d905a78e4795d7458027c281bb739157ca690db01ac22119424f789dc21afcf174b2a4b2ff84a23d0270176 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: arm64 Version: 2.4-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 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_arm64.deb Size: 400068 MD5sum: e49e44de6227dd1bb815b8c944bbeab8 SHA1: 323df6c9ee94d2f9f38d7b218f209130c3a8b816 SHA256: 6430c6ba53bf1dcac596130199a65e075c05960b2c0bfab0d18ea0cfa4becef1 SHA512: c82b96b4a433c27c8c03e7b11330ec61892253e9bb3d12c4baeed9ad88494f3fca792d2424d43218c7a978a6931657e5e46ff47a8194b5dce91db0b180ae1a33 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: arm64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 48240 MD5sum: 109889d26331152d8d2fedeed4e8a5a1 SHA1: baaa1a34bb6ea69fed1183a20b4ba5c313ade0a0 SHA256: 98fb0235cb80657b23bf2b7e10e254e465783cbeac889cfe209c120b1f1a04eb SHA512: 9f06aa1639b2c80b9f66bfea20093a190331980466e0a5b8dde4627bb3a709c156c52e99f69cbb9a7b6a3ef81c3aca4d841fdddc23c211fc8f4bdf5fb6f6a1ca 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: arm64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 120160 MD5sum: b4fbca1664a8cdc2b0334713364a4401 SHA1: 8aaa339882abd790193f74b7c4667f647356df2c SHA256: 2cac4ec52741c9e0fbfec6eae1a9a010c83589adef64746c75b08e26c057a41c SHA512: a4d411cb32ac0f7bb376e50379d8fcfdc37a1288bdbbf7d3fa2e9a7c8f8b81b5503c0d19c85887ee6a84fc3ef8187563870254356890a74429b94528084a4203 Homepage: https://cran.r-project.org/package=iBST Description: CRAN Package 'iBST' (Improper Bagging Survival Tree) Fit a full or subsampling bagging survival tree on a mixture of population (susceptible and nonsusceptible) using either a pseudo R2 criterion or an adjusted Logrank criterion. The predictor is evaluated using the Out Of Bag Integrated Brier Score (IBS) and several scores of importance are computed for variable selection. The thresholds values for variable selection are computed using a nonparametric permutation test. See 'Cyprien Mbogning' and 'Philippe Broet' (2016) for an overview about the methods implemented in this package. 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See, for details, Masoudi et al. (2022) , Masoudi et al. (2017) and Masoudi et al. (2019) . Package: r-cran-iccalib Architecture: arm64 Version: 1.0.8-1.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 (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-survival, r-cran-fitdistrplus, r-cran-icenreg, r-cran-numderiv, r-cran-icsurv, r-cran-msm, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-iccalib_1.0.8-1.ca2604.1_arm64.deb Size: 249346 MD5sum: a08bbb33cdbf933b6be0512190be39db SHA1: 82cc28d356ed2ce4d38c8e643246389cb467adae SHA256: 7fba66cb3e6bea698a5fbf22851641ea5f57ab9a7298c4ebed597241c7989ee9 SHA512: 89ace794fe25493b00d18a3f9fef0ed18a4b528cbaad4a3d3a4c726bae81c78e0ba69507574356212780b80e56239365f50d9a63214aead73133633ef2041970 Homepage: https://cran.r-project.org/package=ICcalib Description: CRAN Package 'ICcalib' (Cox Model with Interval-Censored Starting Time of a Covariate) Calibration and risk-set calibration methods for fitting Cox proportional hazard model when a binary covariate is measured intermittently. Methods include functions to fit calibration models from interval-censored data and modified partial likelihood for the proportional hazard model, Nevo et al. (2018+) . Package: r-cran-iccbeta Architecture: arm64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 232 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 96318 MD5sum: f6b4fa50b25c83aeb67a733926ab06ef SHA1: 5a211713f450d9b59b9b6a3c73dccf9912a7cfa2 SHA256: e551734912052a33aa7ae90e91229dd2d002a1497b323dfbedf21a378e7169cf SHA512: 5fb9624b4d30d296b0ef7fc6491c54c6d46feab245bf4f10c839791b3fee53a5bbce78fe0e798b1557569596616c7ea4354f664cec1210aa3d15739a329deea7 Homepage: https://cran.r-project.org/package=iccbeta Description: CRAN Package 'iccbeta' (Multilevel Model Intraclass Correlation for Slope Heterogeneity) A function and vignettes for computing an intraclass correlation described in Aguinis & Culpepper (2015) . This package quantifies the share of variance in a dependent variable that is attributed to group heterogeneity in slopes. Package: r-cran-icebox Architecture: arm64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 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-ggplot2, r-cran-checkmate, r-cran-data.table, r-cran-rcpp Suggests: r-cran-randomforest, r-cran-mass, r-cran-testthat, r-cran-rpart Filename: pool/dists/resolute/main/r-cran-icebox_1.2-1.ca2604.1_arm64.deb Size: 248158 MD5sum: 4629bbbf1533bf23209a05db55110735 SHA1: 3bcd3e105b1a94cd9125be6f143378af7b3326ad SHA256: 3038800daeefdc9852f26d9167ba63a22e23b624011c71ccfbddafa838379e5e SHA512: 2ea166124f7cb75d1db8ac182048b426dd88e0bb2481fa25545882c6fb8dff44eed05b84aada3dd4d498e3f341edb86547b976f5d8f9d995bfcbf58868b55d0a Homepage: https://cran.r-project.org/package=ICEbox Description: CRAN Package 'ICEbox' (Individual Conditional Expectation Plot Toolbox) Implements Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. ICE plots refine Friedman's partial dependence plot by graphing the functional relationship between the predicted response and a covariate of interest for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate of interest, suggesting where and to what extent they may exist. Package: r-cran-icellr Architecture: arm64 Version: 1.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1120 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-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_arm64.deb Size: 725964 MD5sum: d344ab16e5bdd79d86f0751aaa3db59b SHA1: ecab729229225047846284f260c6775e92272f0f SHA256: cdcfea3f5ac7fa3d27aa4b8ef5efb34a2c45937b661e0ed5752acdfd558a43ef SHA512: 58a3504ebeb1f6fa839323a010df90f46188ce2c3ec9fb3ac0672d334f21425f21a5395ff170a5e143a95167f7da5b82df7f2fec68737fa52851a4b8dbfff2a7 Homepage: https://cran.r-project.org/package=iCellR Description: CRAN Package 'iCellR' (Analyzing High-Throughput Single Cell Sequencing Data) A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) and Khodadadi-Jamayran, et al (2020) for more details. Package: r-cran-icenreg Architecture: arm64 Version: 2.0.16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1966 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_arm64.deb Size: 1336582 MD5sum: 6b89d936dbd1d6dcbbd1f777b54ee627 SHA1: b7147eb0e5725b3a8d5d2159fcfed782dad798e2 SHA256: de8c26bf31979f5dc4f28e3bb93b82581a87a3246400df38d7f4fe72dda9e4b2 SHA512: e91edf3f7fda3cc988108b64c4b7362abae42f39eb4b3dfe730afe4e6eaeec9b9fcfaa07d5d8ccdf0bac2a07680f28ad60ad36d2a8fd20b92a73882c6a3fb841 Homepage: https://cran.r-project.org/package=icenReg Description: CRAN Package 'icenReg' (Regression Models for Interval Censored Data) Regression models for interval censored data. Currently supports Cox-PH, proportional odds, and accelerated failure time models. Allows for semi and fully parametric models (parametric only for accelerated failure time models) and Bayesian parametric models. Includes functions for easy visual diagnostics of model fits and imputation of censored data. Package: r-cran-icensmis Architecture: arm64 Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 540 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_arm64.deb Size: 232512 MD5sum: d946c06377d0753b76da2d07eacc915d SHA1: 0510faf0cccf537f74562d7e97524b5eda6860f8 SHA256: 370d43fb1ca8b39255f800dcda69a1043a35387ce57e5be1d8a832b9e357d66e SHA512: 29c6b8ce099d14ac4ac46323d0e236ec907787d18008864b81f9ed77fe1c445eeb05ab63c36392e26f3eb8679b819bf024b5d45b664da07a02013d8d6a04f130 Homepage: https://cran.r-project.org/package=icensmis Description: CRAN Package 'icensmis' (Study Design and Data Analysis in the Presence of Error-ProneDiagnostic Tests and Self-Reported Outcomes) We consider studies in which information from error-prone diagnostic tests or self-reports are gathered sequentially to determine the occurrence of a silent event. Using a likelihood-based approach incorporating the proportional hazards assumption, we provide functions to estimate the survival distribution and covariate effects. We also provide functions for power and sample size calculations for this setting. Please refer to Xiangdong Gu, Yunsheng Ma, and Raji Balasubramanian (2015) , Xiangdong Gu and Raji Balasubramanian (2016) , Xiangdong Gu, Mahlet G Tadesse, Andrea S Foulkes, Yunsheng Ma, and Raji Balasubramanian (2020) . Package: r-cran-iclogcondist Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 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-flexsurv, r-cran-ggplot2, r-cran-icenreg, r-cran-monotone, r-cran-fdrtool, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-iclogcondist_1.0.1-1.ca2604.1_arm64.deb Size: 158892 MD5sum: a00a2457e45cb18a923a904f860620f2 SHA1: e7efebbec84a5dff64fc3c71f4a7aa71136c3b2e SHA256: 8ded74f93abcbdb80a3ef5f331d3725f9e5af128ef726fdbc7f29579c8550f89 SHA512: 48d3ff7b6883bae245f012a2a43e094a02769687df17b1dfa493f61cc6e9cf2b692fd0e825bf7c6205b174e54047044a136427f5a44f1aa3981ee66921c33155 Homepage: https://cran.r-project.org/package=iclogcondist Description: CRAN Package 'iclogcondist' (Log-Concave Distribution Estimation with Interval-Censored Data) We consider the non-parametric maximum likelihood estimation of the underlying distribution function, assuming log-concavity, based on mixed-case interval-censored data. The algorithm implemented is base on Chi Wing Chu, Hok Kan Ling and Chaoyu Yuan (2024, ). 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(2023) . Package: r-cran-icosa Architecture: arm64 Version: 0.12.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1289 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 756216 MD5sum: 899fdf7d773e5dde16ac44e3330a11db SHA1: 47633ff6d343073e080d41a17afdc939bf90902d SHA256: 70c2285e90b864324e4d10ca42d94b706d73e8a9acdb3a0c1296fa3f00efe0ef SHA512: e361865e27cbc827f89cf0eadbc40fd48d10978d0e59ab67b33bf2f4904511cd5600602db59f7a45fa607b6ae51153b2b05726922a3d39f8e0800013c75a6dd6 Homepage: https://cran.r-project.org/package=icosa Description: CRAN Package 'icosa' (Global Triangular and Penta-Hexagonal Grids Based on TessellatedIcosahedra) Implementation of icosahedral grids in three dimensions. The spherical-triangular tessellation can be set to create grids with custom resolutions. Both the primary triangular and their inverted penta-hexagonal grids can be calculated. Additional functions are provided that allow plotting of the grids and associated data, the interaction of the grids with other raster and vector objects, and treating the grids as a graphs. Package: r-cran-icr Architecture: arm64 Version: 0.6.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 214196 MD5sum: ed2d401114d8ed378a65e3853bb6c6a9 SHA1: 23120aa9c937f77ee9c46326cae665632ffacc78 SHA256: 4508310c8103b11083c452b7f908414be651de1a82d074925e5a638fb898b08e SHA512: c8aceb1fd565eab538e06d36afa82c0e12d110eaf39249ceaad6684390da8f3f703620260013787f4c2fb449552284473074cea25d62c7a5eb522b3c845c299c 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: arm64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 379 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_arm64.deb Size: 211414 MD5sum: d56120e7db14daf2f5fe60fc765615e8 SHA1: 78c712f5c907a99476dbf691d99f8824ada21c7d SHA256: 2d47f1aa2609234e9e87d4d1cce37f57d0d1170f11c569e9370ae9768ec729c5 SHA512: 5fe4cb7aa83fb53476d7d875eee4dd066736852c3f34035906e24b5d73bcce9efd96e9258685678782924243f8ec5d15a31c172a6a57e01126a908850e3588e7 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: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 322 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 193472 MD5sum: 579f28297787d7ed49064f1659380b3d SHA1: 9444efd981cc9f64d940c41d0df7aef2361fa9ea SHA256: fb5cdd9c31a8a3b00519d9d08f239e73a2472c54d836340145c13bb120eb0b99 SHA512: b75c4a02decef25a8c6de30ce02e870e41dbea428f0f4aa128034b46ed15ff5f8fd0eaa3cea45ea7e317689cb4f729531749cc297a654b935cf4f15d9fd9f4c4 Homepage: https://cran.r-project.org/package=ICSClust Description: CRAN Package 'ICSClust' (Tandem Clustering with Invariant Coordinate Selection) Implementation of tandem clustering with invariant coordinate selection with different scatter matrices and several choices for the selection of components as described in Alfons, A., Archimbaud, A., Nordhausen, K.and Ruiz-Gazen, A. (2024) . Package: r-cran-icskat Architecture: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 177746 MD5sum: f3750e2a2199f7266dee66923645c9e7 SHA1: 914995e1d66472518a7625ac39d901b6d20ceb95 SHA256: 07d213d543ce42612f16a4582e4c13fe141e7028982e6cdcaf353e49c87767fc SHA512: d6e3301a43a487d7d210292db27d4a4091d3702bc70bf26fc348a455ea2bfe6d063df47127d6b0f543fc62fcf8448c960bc24d0eaf07f2287ab53a4766e1cd03 Homepage: https://cran.r-project.org/package=ICSKAT Description: CRAN Package 'ICSKAT' (Interval-Censored Sequence Kernel Association Test) Implements the Interval-Censored Sequence Kernel Association (ICSKAT) test for testing the association between interval-censored time-to-event outcomes and groups of single nucleotide polymorphisms (SNPs). Interval-censored time-to-event data occur when the event time is not known exactly but can be deduced to fall within a given interval. For example, some medical conditions like bone mineral density deficiency are generally only diagnosed at clinical visits. If a patient goes for clinical checkups yearly and is diagnosed at, say, age 30, then the onset of the deficiency is only known to fall between the date of their age 29 checkup and the date of the age 30 checkup. Interval-censored data include right- and left-censored data as special cases. This package also implements the interval-censored Burden test and the ICSKATO test, which is the optimal combination of the ICSKAT and Burden tests. Please see the vignette for a quickstart guide. The paper describing these methods is " Inference for Set-Based Effects in Genetic Association Studies with Interval-Censored Outcomes" by Sun R, Zhu L, Li Y, Yasui Y, & Robison L (Biometrics 2023, ). Package: r-cran-icsnp Architecture: arm64 Version: 1.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 296 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 203062 MD5sum: b7bc0169b083b522dd5a0069694c11dc SHA1: b0dc0b4a24dc0553d0e6d7ae71d81ca09116e792 SHA256: 0c482c8f68bbc73b8190500186070801007c5ca1ec79b63ae28a80d0ee3f4f36 SHA512: e6bf176b9fba1cc7c903aa78e3023cf83ef9ba68330203abf59a07177e05bc02d7a72d6a88f75bcf2c20ac7bae6f7e796962f761e113ce3a973af63a1af235fe Homepage: https://cran.r-project.org/package=ICSNP Description: CRAN Package 'ICSNP' (Tools for Multivariate Nonparametrics) Tools for multivariate nonparametrics, as location tests based on marginal ranks, spatial median and spatial signs computation, Hotelling's T-test, estimates of shape are implemented. 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Package: r-cran-idar Architecture: arm64 Version: 1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 342 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_arm64.deb Size: 240036 MD5sum: 02ee3e1d653517246494f82345ef4874 SHA1: e6b73409013ea3267b9ee9a62d7a63746da4217b SHA256: 3546faba57b116d8ebb1864f7b49fee07e63c3138be607c63f85955a49ab984c SHA512: 3154d2b63d16e67a1eac35c85eb454ae5e2f43af0cbf88fe98ced5a8bcf915b23c793dfe07300afed213f0592820382afc1b213fc4db784aa1cb5621a5c1b3a8 Homepage: https://cran.r-project.org/package=idar Description: CRAN Package 'idar' (Individual Diversity-Area Relationships) Computes and tests individual (species, phylogenetic and functional) diversity-area relationships, i.e., how species-, phylogenetic- and functional-diversity varies with spatial scale around the individuals of some species in a community. 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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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See Robitzsch and Steinfeld (2018) for a description of the functionality of the package. See Wang, Su and Qiu (2014; ) for an overview of modeling alternatives. Package: r-cran-immigrate Architecture: arm64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 343 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_arm64.deb Size: 160178 MD5sum: d547767c9d7282c13b9ac83cb7d8b5ca SHA1: 91629fa11dc671c76c80526ffbf491656240b66c SHA256: a9f6f0d98fc630063337b901dff063bd499e19f5335e647b73eb445e7772d935 SHA512: a46851f6c1bdab7bd825c521b27e58ef94a48ed3f5c5be1ec50acb895889b4c4197f284a80667b2b65ac60b5c6bdf0f91fb2317454dae99785c98bd4b86a1800 Homepage: https://cran.r-project.org/package=Immigrate Description: CRAN Package 'Immigrate' (Iterative Max-Min Entropy Margin-Maximization with InteractionTerms for Feature Selection) Based on large margin principle, this package performs feature selection methods: "IM4E"(Iterative Margin-Maximization under Max-Min Entropy Algorithm); "Immigrate"(Iterative Max-Min Entropy Margin-Maximization with Interaction Terms Algorithm); "BIM"(Boosted version of IMMIGRATE algorithm); "Simba"(Iterative Search Margin Based Algorithm); "LFE"(Local Feature Extraction Algorithm). This package also performs prediction for the above feature selection methods. 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Delivers multi-modal immune profiling (bulk, single-cell, CITE-seq/AbSeq, spatial, immunogenicity data), feature engineering (ML-ready feature tables and matrices), and biomarker discovery workflows (cohort comparisons, longitudinal tracking, repertoire similarity, enrichment). Provides a user-friendly interface to widely used AIRR methods — clonality/diversity, V(D)J usage, similarity, annotation, tracking, and many more. Think Scanpy or Seurat, but for AIRR data, a.k.a. Adaptive Immune Receptor Repertoire, VDJ-seq, RepSeq, or VDJ sequencing data. A successor to our previously published "tcR" R package (Nazarov 2015). 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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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Package: r-cran-inca Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 815 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 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_arm64.deb Size: 481126 MD5sum: 784187d50608a78033577237307dd6c5 SHA1: 9e70b00b128ec9e115955473132a5f3aaf55f61a SHA256: c1431d882d32fd225e05521f1188be073cfd50cd20f10a096acb189778856d88 SHA512: 9fdc7b9aba2e85ed19d726fe7375e980abcca40eca4d8adfb2d71a6a4d762f16c947b102653677d5102688f904eba9e657ffae1e0375aaaadd148abf0e128477 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. 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'iNEXT.3D' extends 'iNEXT' to include three dimensions (3D) of biodiversity, i.e., taxonomic diversity (TD), phylogenetic diversity (PD) and functional diversity (FD). This package provides functions to compute standardized 3D diversity estimates with a common sample size or sample coverage. A unified framework based on Hill numbers and their generalizations (Hill-Chao numbers) are used to quantify 3D. All 3D estimates are in the same units of species/lineage equivalents and can be meaningfully compared. The package features size- and coverage-based rarefaction and extrapolation sampling curves to facilitate rigorous comparison of 3D diversity across individual assemblages. Asymptotic 3D diversity estimates are also provided. See Chao et al. (2021) for more details. Package: r-cran-inext Architecture: arm64 Version: 3.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1622 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_arm64.deb Size: 1162540 MD5sum: 9445bcee8dc0a0dc140f857a4cfb5305 SHA1: badbb52bd3d6ae4625b9cea96898de7be62b8358 SHA256: eb3b87e38c7b2bd8cbcc5d29ef59c01a4eea081ee5fc2d38391307ed1f220c13 SHA512: 0993a3f6a7927b21b9a4561ecf9b348e281a44c9069a4b26eb0366c203edd931a2929133f3f0e2bafff9a931ffe395fbd4370b399cfb3d961aa18cf9715978b0 Homepage: https://cran.r-project.org/package=iNEXT Description: CRAN Package 'iNEXT' (Interpolation and Extrapolation for Species Diversity) Provides simple functions to compute and plot two types (sample-size- and coverage-based) rarefaction and extrapolation curves for species diversity (Hill numbers) based on individual-based abundance data or sampling-unit- based incidence data; see Chao and others (2014, Ecological Monographs) for pertinent theory and methodologies, and Hsieh, Ma and Chao (2016, Methods in Ecology and Evolution) for an introduction of the R package. Package: r-cran-infercsn Architecture: arm64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1473 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-dplyr, r-cran-ggnetwork, r-cran-ggplot2, r-cran-ggraph, r-cran-l0learn, r-cran-matrix, r-cran-purrr, r-cran-rcpp, r-cran-thisutils Suggests: r-bioc-complexheatmap, r-cran-circlize, r-cran-gtools, r-cran-gganimate, r-cran-ggextra, r-cran-ggpointdensity, r-cran-ggpubr, r-cran-igraph, r-cran-irlba, r-cran-network, r-cran-patchwork, r-cran-plotly, r-cran-precrec, r-cran-proc, r-cran-proxy, r-cran-tidygraph, r-cran-rann, r-cran-rcolorbrewer, r-cran-rtsne, r-cran-rtransferentropy, r-cran-uwot, r-cran-viridis Filename: pool/dists/resolute/main/r-cran-infercsn_1.2.0-1.ca2604.1_arm64.deb Size: 1062672 MD5sum: 565521b31eb14ec4816191a420a7339b SHA1: 274ab2f89797729a08f19fcdc54cd47533806e66 SHA256: 279c3134b6735d0fb2dd0242ac17328204c45188aef227b73c8c61abf3667543 SHA512: 1cf78ded0c1a633a6e7686780e51537c4c96e9f11684b192e93b6d94ffd35a7a19a3bc675b7d97c23d9705d2a7fdbd7c0346d61463acccf61302385c94625ddd Homepage: https://cran.r-project.org/package=inferCSN Description: CRAN Package 'inferCSN' (Inferring Cell-Specific Gene Regulatory Network) An R package for inferring cell-type specific gene regulatory network from single-cell RNA-seq data. Package: r-cran-inferr Architecture: arm64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-magrittr, r-cran-rcpp Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-xplorerr Filename: pool/dists/resolute/main/r-cran-inferr_0.3.2-1.ca2604.1_arm64.deb Size: 279614 MD5sum: c001f2addba3f6c8928301ddce0d7d06 SHA1: bf1e8a06ed4ffa94ee976ee3c048530c4d10cb6a SHA256: bb6c2fe728467f67286e1cecff986cc97874aef0e93a6d13e441c30873aed216 SHA512: e2f3ce05c4ce3cac5269f3cc3517f6ad8281069056b711134f05750e75bd538729750dee0d6ee3da35dd07d573d37b44850aeca045a912cb9e021826a927aa49 Homepage: https://cran.r-project.org/package=inferr Description: CRAN Package 'inferr' (Inferential Statistics) Select set of parametric and non-parametric statistical tests. 'inferr' builds upon the solid set of statistical tests provided in 'stats' package by including additional data types as inputs, expanding and restructuring the test results. The tests included are t tests, variance tests, proportion tests, chi square tests, Levene's test, McNemar Test, Cochran's Q test and Runs test. Package: r-cran-influencer Architecture: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: libc6 (>= 2.17), libgomp1 (>= 6), r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-matrix Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-influencer_0.1.5-1.ca2604.1_arm64.deb Size: 45496 MD5sum: faab165801801fbb733c0bb4401157f3 SHA1: 7f0e2a78e0a46e93b30d120f25b0d70102980607 SHA256: 20ea1ce2a2fef5efdbbfa9986f6aee2d8f0ee608c0d43ee876310c7c431b1b27 SHA512: cd189b1cad2276e0e3660144454f61d6e3bb3365d5f29be40db1d728b5d30f776a76ded158e0ceae228421ecce770f2cf6019399f91588792049424a47e81386 Homepage: https://cran.r-project.org/package=influenceR Description: CRAN Package 'influenceR' (Software Tools to Quantify Structural Importance of Nodes in aNetwork) Provides functionality to compute various node centrality measures on networks. Included are functions to compute betweenness centrality (by utilizing Madduri and Bader's SNAP library), implementations of constraint and effective network size by Burt (2000) ; algorithm to identify key players by Borgatti (2006) ; and the bridging algorithm by Valente and Fujimoto (2010) . On Unix systems, the betweenness, Key Players, and bridging implementations are parallelized with OpenMP, which may run faster on systems which have OpenMP configured. Package: r-cran-infocausality Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 531 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 226894 MD5sum: 1d53dd85053d8d68c6ef0cc0221bb073 SHA1: 2bbe0049d5c6bcc6fd90b7bcb59e50f40f5e89ee SHA256: eb1169031f5e44b22d0da76b69f9628e9a12f4d64f72d0f9cd41be24932ac89c SHA512: 08db1fea69ae341aa63678beee30685169f0532c6523c607261f2d9587974d5cd6265d470f569009b863c663a358094e81a6a3b48466af9a1d9585302c72681c Homepage: https://cran.r-project.org/package=infocausality Description: CRAN Package 'infocausality' (Information-Theoretic Measure of Causality) Methods for quantifying temporal and spatial causality through information flow, and decomposing it into unique, redundant, and synergistic components, following the framework described in Martinez-Sanchez et al. (2024) . Package: r-cran-infotheo Architecture: arm64 Version: 1.2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-infotheo_1.2.0.1-1.ca2604.1_arm64.deb Size: 49446 MD5sum: eca0d33f5746d7dae8cae40df29bf63e SHA1: 94f74d5bbbe03d567d08c68ca772bc1d6943e9fc SHA256: 9d26d1dac5af5151c79087cae38ace0c7886475eba6c6f281a3be26b58e517de SHA512: d5da26f1866c35cda533634155d94fbbcf51c0bda9d6df5982108e248fd0e832febdfd59d690f6174a09ca19c17b3acefc532b2c43c231138d2b174bd0c6a728 Homepage: https://cran.r-project.org/package=infotheo Description: CRAN Package 'infotheo' (Information-Theoretic Measures) Implements various measures of information theory based on several entropy estimators. Package: r-cran-infoxtr Architecture: arm64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1045 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_arm64.deb Size: 407502 MD5sum: 976144495938eaaa09e0c9a00b14d81c SHA1: b315e31351ea047fcaf9af1cfa702fef55cdfd5f SHA256: a3891d5b374c39f239ea451bba4fb9b7b08a29edc7e37fd5467876a2c9bd65e6 SHA512: 421551017b3e8ad10fed425fea7aa3c2963e8a38ed1a22aff7673174201a84fcf4c78ac0dc094c646da1b707eea4082c435109676215b02e9bf3157661de0db2 Homepage: https://cran.r-project.org/package=infoxtr Description: CRAN Package 'infoxtr' (Information-Theoretic Measures for Revealing VariableInteractions) Implements information-theoretic measures to explore variable interactions, including KSG mutual information estimation for continuous variables from Kraskov et al. (2004) , knockoff conditional mutual information described in Zhang & Chen (2025) , synergistic-unique-redundant decomposition introduced by Martinez-Sanchez et al. (2024) , allowing detection of complex and diverse relationships among variables. Package: r-cran-inlabru Architecture: arm64 Version: 2.14.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4040 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 3254366 MD5sum: d3c27185b1c224a90a3a3701fc25a6e8 SHA1: 5213d17d9768dde9cbe61d884a0c221e7a8c0750 SHA256: 7fe2b49fda0363d78702a8f64e522af6a586f1ed55b8e795b69186e87198cdde SHA512: 46261da6d427ba8cae5b51ed1397e312bfb9be9da0a19f411c6ccd45ca2fa9063473032fec29ff66cefcd05cfd39145242dac27758796aa737bc56ddcb4bc2c1 Homepage: https://cran.r-project.org/package=inlabru Description: CRAN Package 'inlabru' (Bayesian Latent Gaussian Modelling using INLA and Extensions) Facilitates spatial and general latent Gaussian modeling using integrated nested Laplace approximation via the INLA package (). Additionally, extends the GAM-like model class to more general nonlinear predictor expressions, and implements a log Gaussian Cox process likelihood for modeling univariate and spatial point processes based on ecological survey data. Model components are specified with general inputs and mapping methods to the latent variables, and the predictors are specified via general R expressions, with separate expressions for each observation likelihood model in multi-likelihood models. A prediction method based on fast Monte Carlo sampling allows posterior prediction of general expressions of the latent variables. Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019) . Package: r-cran-inlaspacetime Architecture: arm64 Version: 0.1.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix, r-cran-fmesher, r-cran-inlatools, r-cran-inlabru Suggests: r-cran-sf, r-cran-terra, r-cran-knitr, r-cran-ggplot2, r-cran-rmarkdown, r-cran-data.table, r-cran-rnaturalearth, r-cran-rnaturalearthdata, r-cran-ggpubr, r-cran-doypacolors, r-cran-s2, r-cran-lubridate, r-cran-ggoceanmaps, r-cran-sp, r-cran-spdep Filename: pool/dists/resolute/main/r-cran-inlaspacetime_0.1.14-1.ca2604.1_arm64.deb Size: 196480 MD5sum: e45edc0b0726ac55b52f5de9f8ec7cb0 SHA1: 6b85eea30d7a780ea365fe2a102c846a108e262b SHA256: 619e6dfc847972ace94f04524cf57a4a4fd3b9b01c33c2fcd92de6fd0b12a3b8 SHA512: f310b57da80deead914b271ef49e5b5c414ab6ff8a801cdd9b176ffb45a73cd03726bd8c5456ec2cad4fbe8eddf8edc8b1f37d3b3193f75116b25bb0bd48057a Homepage: https://cran.r-project.org/package=INLAspacetime Description: CRAN Package 'INLAspacetime' (Spatial and Spatio-Temporal Models using 'INLA') Prepare objects to implement models over spatial and spacetime domains with the 'INLA' package (). These objects contain data to for the 'cgeneric' interface in 'INLA', enabling fast parallel computations. We implemented the spatial barrier model, see Bakka et. al. (2019) , and some of the spatio-temporal models proposed in Lindgren et. al. (2024) . Details are provided in the available vignettes and from the URL bellow. Package: r-cran-inlatools Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: libc6 (>= 2.34), r-base-core (>= 4.6.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-inlatools_0.1.4-1.ca2604.1_arm64.deb Size: 182982 MD5sum: f2a8d5a0f86047e1429523a6caf6bbfb SHA1: 7e7dc11248b514bba43c453b90bdf9f3820ce293 SHA256: 4c033d11d990177376a4b9bcb247cd82e0dc53cfedb559ff2db494500ae16810 SHA512: b4221332f8dbfcd696ece891bdc996456af2e721cef3211910b0455533d050bfe2a37604bb67f395a8ce5f5d4e2e4b9f8c5475e25573465fcc1466d164f376f1 Homepage: https://cran.r-project.org/package=INLAtools Description: CRAN Package 'INLAtools' (Functionalities for the 'INLA' Package) Contain code to work with a C struct, in short cgeneric, to define a Gaussian Markov random (GMRF) model. The cgeneric contain code to specify GMRF elements such as the graph and the precision matrix, and also the initial and prior for its parameters, useful for model inference. It can be accessed from a C program and is the recommended way to implement new GMRF models in the 'INLA' package (). The 'INLAtools' implement functions to evaluate each one of the model specifications from R. The implemented functionalities leverage the use of 'cgeneric' models and provide a way to debug the code as well to work with the prior for the model parameters and to sample from it. The `generic0` can be used to implement intrinsic models with the scaling as proposed in Sørbye & Rue (2014) , and the required constraints. A very useful functionality is the Kronecker product method that creates a new model from multiple cgeneric models. It also works with the rgeneric, the R version of the cgeneric intended to easy try implementation of new GMRF models. The Kronecker between two cgeneric models where each one needs a constraint, such as spatio-temporal intrinsic interaction models, the needed constraints are automatically set. Package: r-cran-inplace Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 387 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 92294 MD5sum: 6aa3fe2820391667a6843320ac364a4d SHA1: 2f63b83f75f20505ff6a37c9c9db2f2388af0f2d SHA256: 1b4967483de581ca8c9c24296bc01799a9a5103320c877adcdb1083276771a3b SHA512: f273d13b0079a9daf86013a641aab67e8d997c2719a9681e5eba3d1975e7b1cb24aa8a298944acd0e75a9697bd13fd372094845515bb1195dd5a71893f7ab91d Homepage: https://cran.r-project.org/package=inplace Description: CRAN Package 'inplace' (In-place Operators for R) It provides in-place operators for R that are equivalent to '+=', '-=', '*=', '/=' in C++. Those can be applied on integer|double vectors|matrices. You have also access to sweep operations (in-place). 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Functions report missingness, categorical levels, numeric distribution, correlation, column types and memory usage. Package: r-cran-instantiate Architecture: arm64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 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_arm64.deb Size: 68952 MD5sum: 33a8ff699b4eae399ab8990b5f41b165 SHA1: 4b01d37301d8f7b59b007a4bc07d4fde229c821d SHA256: 30efa9327e89a5c55e13a44e099dd90d45ec98b83b20097fea53736f0cd9b9fb SHA512: 8b7c81034d02574151f1db99c539a1b0d6b4278e5076677bb6c447f84c971a028f49daa928737c7d14ecaab73f5ecda64cb843fa769798f25fab280fb61b913d 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: arm64 Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 928 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 816288 MD5sum: 9c308be5a49c5db158daee905214e710 SHA1: f1a6d8c30996960508b969074362ad8703d30710 SHA256: eaf4eea1faab8b3e61039087245fdbe04228341aa9e052a7be2740564c42eb82 SHA512: 4d2a34a81f514b01da3b708807bc72e50fcd39c85cad594553d1697fd667c8b5eca984175aecb58517be5f3a95dc88173a551201e11276ead6cd605504ab4653 Homepage: https://cran.r-project.org/package=interep Description: CRAN Package 'interep' (Interaction Analysis of Repeated Measure Data) Extensive penalized variable selection methods have been developed in the past two decades for analyzing high dimensional omics data, such as gene expressions, single nucleotide polymorphisms (SNPs), copy number variations (CNVs) and others. However, lipidomics data have been rarely investigated by using high dimensional variable selection methods. This package incorporates our recently developed penalization procedures to conduct interaction analysis for high dimensional lipidomics data with repeated measurements. The core module of this package is developed in C++. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University. Package: r-cran-interflex Architecture: arm64 Version: 1.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 986 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 757032 MD5sum: 572035cf2ffba526bda79a5110ad29a1 SHA1: 1937f39a1bd70c4468fcb33dc7dd2e48709bc5fe SHA256: 9c95cfe46987c8f54fa76ab97474f14dcc738e56e9e7a32e0d2cd79f92d20444 SHA512: 2add0a4ea68be34e33b1483b99c29adc6735cebc114c30af785c93dbf8ff0275109d0b219bc96e423ccaccd4d18a126c90019d80496ef1623c14388f44026bea Homepage: https://cran.r-project.org/package=interflex Description: CRAN Package 'interflex' (Multiplicative Interaction Models Diagnostics and Visualization) Performs diagnostic tests of multiplicative interaction models and plots non-linear marginal effects of a treatment on an outcome across different values of a moderator. Package: r-cran-interleave Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 99750 MD5sum: 1160db0299678bf95666085b53b2217f SHA1: 423530f31b4287ebb1a789421b3bb7eef873ff8a SHA256: bd8805e155f1aa6ce2334e5ff2a82669ad46e33f8c650ecb12261e60b6f2afc7 SHA512: 48ae1a6dbb5b62902c92ef40379bd27bc06b1ee482b26818fdafa940101dbb2b042b14acf72733c21d378f5a413461812367428ed0d1556cbde6146525f65187 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. 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Package: r-cran-interp Architecture: arm64 Version: 1.1-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2154 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_arm64.deb Size: 1499880 MD5sum: 69469e5a06bb5315219db586af3d22c0 SHA1: 57b3e6d1b4c7cfc5fb10bd1011c4dc66fe2eaa61 SHA256: 513395e8f0c7c7ffd26025cf12c9b2dddf5531990ae8b2380dac32a32cd5ca89 SHA512: aecde7b1e9e37ffd96833f7bb47d1afe8d05ab78b09b7766f1f4d203b7b1cd7150349c882ec9a2fcef19ec5c7ddd263c4bce84bd357df94142300bd92394b5dc Homepage: https://cran.r-project.org/package=interp Description: CRAN Package 'interp' (Interpolation Methods) Bivariate data interpolation on regular and irregular grids, either linear or using splines are the main part of this package. It is intended to provide FOSS replacement functions for the ACM licensed akima::interp and tripack::tri.mesh functions. Linear interpolation is implemented in interp::interp(..., method="linear"), this corresponds to the call akima::interp(..., linear=TRUE) which is the default setting and covers most of akima::interp use cases in depending packages. A re-implementation of Akimas irregular grid spline interpolation (akima::interp(..., linear=FALSE)) is now also available via interp::interp(..., method="akima"). Estimators for partial derivatives are now also available in interp::locpoly(), these are a prerequisite for the spline interpolation. The basic part is a GPLed triangulation algorithm (sweep hull algorithm by David Sinclair) providing the starting point for the irregular grid interpolator. As side effect this algorithm is also used to provide replacements for almost all functions of the tripack package which also suffers from the same ACM license restrictions. All functions are designed to be backward compatible with their akima / tripack counterparts. Package: r-cran-interpolators Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 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_arm64.deb Size: 137116 MD5sum: 5b1b55355231b77193c0e12435f63cc7 SHA1: 520af0f5a6a2a8f0e8149ec42ee03789c7a1e204 SHA256: bce90c8cdf7675213dc243e60ec57505e76ed3ad99e1c98d278ca4ad75d99d22 SHA512: bb7304e873903efb9af653358371352ff5e36fc170c36ee697225a506e80d663e55aed545e2bdef112351af0b15aaddb15847b0a9877d8d1857fb05ce74f1e81 Homepage: https://cran.r-project.org/package=interpolators Description: CRAN Package 'interpolators' (Some Interpolation Methods) Some interpolation methods taken from 'Boost': barycentric rational interpolation, modified Akima interpolation, PCHIP (piecewise cubic Hermite interpolating polynomial) interpolation, and Catmull-Rom splines. Package: r-cran-interpret Architecture: arm64 Version: 0.1.35-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 715 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_arm64.deb Size: 253874 MD5sum: f87627b9f95605c749ad9cbd3ad4ffd4 SHA1: ca51ee4cdb211488acd5a588f448fc8ac60e21de SHA256: b24a7186b54246f1bd3a53c522bdd1612fb755182b537c9016a10c7c45b968ad SHA512: 9282f815adace03130316d26d94bf01c67e4b23431f805e288e93b5b7cca55b415b4cc0c33029dbc38714fc18bd7fb4a55bcc3a8eb83103d1a5a6345a0bd059a Homepage: https://cran.r-project.org/package=interpret Description: CRAN Package 'interpret' (Fit Interpretable Machine Learning Models) Package for training interpretable machine learning models. Historically, the most interpretable machine learning models were not very accurate, and the most accurate models were not very interpretable. Microsoft Research has developed an algorithm called the Explainable Boosting Machine (EBM) which has both high accuracy and interpretable characteristics. EBM uses machine learning techniques like bagging and boosting to breathe new life into traditional GAMs (Generalized Additive Models). This makes them as accurate as random forests and gradient boosted trees, and also enhances their intelligibility and editability. Details on the EBM algorithm can be found in the paper by Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad (2015, ). Package: r-cran-interprocess Architecture: arm64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 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_arm64.deb Size: 101546 MD5sum: e0bcd7c0c4be825f3ec0a2aa0a10a0f7 SHA1: 6caeb1044d2147c876ffa1378458dbc5926a344e SHA256: 7b6eeaa432c2ed28f1905fef66480a70abb04994d7caf1ad2a5914be161d6d4d SHA512: 5719311edad33102dfd1501e6d0998ed948ae4c6a5e175ccbc02138c39414f21392c69f86b558be1f4de98dbb2bb0e85ffa695bf580c1d55e620e5053d6fc06c 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. 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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) . 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Specifically, the package implements the 'TWO-NN' and 'Gride' estimators and the 'Hidalgo' Bayesian mixture model. In addition, the first reference contains an extended vignette on the usage of the 'TWO-NN' and 'Hidalgo' models. References: Denti (2023, ); Allegra et al. (2020, ); Denti et al. (2022, ); Facco et al. (2017, ); Santos-Fernandez et al. (2021, ). 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The functions for estimating and testing factor risk premia implement the Fama-MachBeth (1973) two-pass approach, the misspecification-robust approaches of Kan-Robotti-Shanken (2013) , and the approaches based on tradable factor risk premia of Quaini-Trojani-Yuan (2023) . The functions for selecting the "strong" risk factors are based on the Oracle estimator of Quaini-Trojani-Yuan (2023) and the factor screening procedure of Gospodinov-Kan-Robotti (2014) . The functions for evaluating model misspecification implement the HJ model misspecification distance of Kan-Robotti (2008) , which is a modification of the prominent Hansen-Jagannathan (1997) distance. The functions for testing model identification specialize the Kleibergen-Paap (2006) and the Chen-Fang (2019) rank test to the regression coefficient matrix of test asset returns on risk factors. Finally, the function for heteroskedasticity and autocorrelation robust covariance estimation implements the Newey-West (1994) covariance estimator. Package: r-cran-intsurv Architecture: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1435 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_arm64.deb Size: 620148 MD5sum: a130237180af79fb2357a9640d78dfed SHA1: b2800aadd3bc23c82b0ddac66defecf38804a18c SHA256: da16806ab8b4d36d788f8baca9cf66d4e8cf749a3c6d4c1c41015bbc92fc716c SHA512: bdbf621baee3875774e29999407d00cddcedb0a71e47cfea1917ea6354b591e671c041c86a36a51a8039981885e2890a45a2ea67484376e4ba523957e27ee172 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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(2014) in a comprehensive pipeline for processing proteomics data in data-independent acquisition mode (Pham et al. 2020 ; Pham et al. 2026 ). It offers additional options for protein quantification using the N most intense fragment ions, using all fragment ions, the median polish algorithm by Tukey (1977, ISBN:0201076160), and a robust linear model. In general, the tool can be used to integrate multiple proportional observations into a single quantitative value. 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It implements methods to estimate colonization and extinction rates (including environmental variables) given presence-absence data, simulates community assembly, and performs model selection. 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Linear, logistic, Poisson and gamma regressions with several link functions are implemented. The algorithm is described in the original paper; see and discussed in a tutorial . Package: r-cran-isnullptr Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 108 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-isnullptr_1.0.2-1.ca2604.1_arm64.deb Size: 10762 MD5sum: 23505e7c9bcec0916f563542d559d3cc SHA1: d7b9b27a737d19ce79e293903556807f5e89c356 SHA256: 8fe50919bb4580dd544c50b7846551c11da787a55b056c1b79e28b290c954973 SHA512: 4fe4aa945be9e2f5968c4ce30ff127ae120edd1072f360f513563a3cd46218bdc808cfcd05d2a065da99fd642c2cb778126a19bcbae388d11a7a7a508c7d9cac Homepage: https://cran.r-project.org/package=isnullptr Description: CRAN Package 'isnullptr' (Check if an 'externalptr' is a Null Pointer) Check if an 'externalptr' is a null pointer. 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Uncertainty propagation follows Annex B of that standard. Reference: International Organization for Standardization (2016) . Package: r-cran-iso8601 Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: libc6 (>= 2.17), 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-iso8601_0.1.2-1.ca2604.1_arm64.deb Size: 91012 MD5sum: 0791f1aa0292b4854a4a92306f296e13 SHA1: 2791cd218fba86a224e746183acc31a1d5826e3f SHA256: e01e90c07c2d27ca239e95669be6637a093d33d354677206147f67b0644bcbbf SHA512: a5d56a93000e106983a13d27ec216c3afb3ad684d8de58350ff856e9c7740f152cae6e0e063ed1b89f2e5f1daf7da767bffda62744d4a062f5853dd2271a5431 Homepage: https://cran.r-project.org/package=iso8601 Description: CRAN Package 'iso8601' (Working with ISO8601 Dates and Times) Functions to parse strings with ISO8601 dates, times, and date-times into R-objects. 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Package: r-cran-isodistrreg Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1786 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-osqp, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-isodistrreg_0.1.0-1.ca2604.1_arm64.deb Size: 1547868 MD5sum: c5dda8bb04156aed48e6a02365e0c26d SHA1: 7c5d106a1d612752eff739cd53761b72ea41fc57 SHA256: e0afb30782d26228393541c254865f87f37c28b505e22a5fa8f7df8062a1d5d6 SHA512: 63cc7f67f8aaa4d08e576bfe35f173b5a2e0096f48af7bc151e71bcec1871580eed4bb0bf503f74c5d815384b0082e5059daaa5add851328e05b13587236d5f2 Homepage: https://cran.r-project.org/package=isodistrreg Description: CRAN Package 'isodistrreg' (Isotonic Distributional Regression (IDR)) Distributional regression under stochastic order restrictions for numeric and binary response variables and partially ordered covariates. See Henzi, Ziegel, Gneiting (2020) . Package: r-cran-isospecr Architecture: arm64 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_arm64.deb Size: 144920 MD5sum: 37f1f6b5443269522832488106d3e3b5 SHA1: e6b25c258b7f00670eb38663acd405441145fbad SHA256: 44d8dc01aeba64f1f4347f320e87deb0064eb6e7cc4ccf0f678c4afa978aa205 SHA512: 89e890fee3b1cbb24078a8e97c8a01c4ab09f2e81d970f30842f698b49baa3a98de48eeefe085b92026f1760c7fbd7fea65202a9495d3a2ff4e018c91c4a2f4f 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. 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Provides simple heuristics for fitting the model to categorical columns and handling missing data, and offers options for varying between random and guided splits, and for using different splitting criteria. 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This is achieved using Fourier transformation methods proposed by Zhu and Zeng (2006) , convolution transformation methods proposed by Zeng and Zhu (2010) , and iterative Hessian transformation methods proposed by Cook and Li (2002) . Additionally, mitdr() function provides optimal estimators for sufficient dimension reduction subspaces in multivariate regression by optimizing a discrepancy function using a Fourier transform approach proposed by Weng and Yin (2022) , and selects the sufficient variables using Fourier transform sparse inverse regression estimators proposed by Weng (2022) . 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(2023) ). Package: r-cran-itp Architecture: arm64 Version: 1.2.2-1.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 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-itp_1.2.2-1.ca2604.1_arm64.deb Size: 133258 MD5sum: 1b19ef70d131e2b95cb3f49ec1447ef1 SHA1: cd429eb8f7699db391ab30178bfdae6a1a8f97b8 SHA256: ddcd64d5224aeb9adcd7f3ccdad06cf671e2594af0c8b1244989db42a450f7e4 SHA512: adcfbe99fde7d12f994a72a7741dd9e92e2a2fcdfcdd49f43c5e77452229870631fc8a83683191c8f96bcda81e9a5e453ac44397536214d2b38a2f8e50da1175 Homepage: https://cran.r-project.org/package=itp Description: CRAN Package 'itp' (The Interpolate, Truncate, Project (ITP) Root-Finding Algorithm) Implements the Interpolate, Truncate, Project (ITP) root-finding algorithm developed by Oliveira and Takahashi (2021) . The user provides the function, from the real numbers to the real numbers, and an interval with the property that the values of the function at its endpoints have different signs. If the function is continuous over this interval then the ITP method estimates the value at which the function is equal to zero. If the function is discontinuous then a point of discontinuity at which the function changes sign may be found. The function can be supplied using either an R function or an external pointer to a C++ function. Tuning parameters of the ITP algorithm can be set by the user. Default values are set based on arguments in Oliveira and Takahashi (2021). Package: r-cran-ivdoctr Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 500 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 317108 MD5sum: 0e8fba3286b05263bd7cb49108992584 SHA1: f8b21307fa5d1e942431abb1c31b135572e9e3d4 SHA256: 508073d364ec30ba1bcf9dba993530a60a2eb6b45a5e63f8c9cf64f3a9ac0a6b SHA512: 1da46a96c91b63b782fd98385eaea990a766ced111c11419e18361263ab74918d3c109eebefc34cde8a4eedbea9a6e9f04743072fdf98aa559bb6e8367b1a40d 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: arm64 Version: 2.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 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_arm64.deb Size: 213004 MD5sum: af6545c41af0202fe103bd44edf1f536 SHA1: 2708aeb277ecb33cf4c2f964dce44c2d68e14a49 SHA256: 7d98d4e923c07136b7d52a788f08ebd080096be1d073e601b410e62535c3bb50 SHA512: 17d65bd2fd7acaa15349d599dc4ca58666ce913e9c59a38ca412653568ebeda69d70a539b5587cd90af3c4cf907612af6a0ea0b91cb3a67f8ead54de3cd52842 Homepage: https://cran.r-project.org/package=ivtools Description: CRAN Package 'ivtools' (Instrumental Variables) Contains tools for instrumental variables estimation. Currently, non-parametric bounds, two-stage estimation and G-estimation are implemented. Balke, A. and Pearl, J. (1997) , Vansteelandt S., Bowden J., Babanezhad M., Goetghebeur E. (2011) . Package: r-cran-ivx Architecture: arm64 Version: 1.1.1-1.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), 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_arm64.deb Size: 393420 MD5sum: 13679255a17bd4f5cbc993f06f891999 SHA1: 65bb2cc600ebbfda5671a30fddc1adcc0cea404f SHA256: e904a988f47a06b992dd475b4c80e1a29d465f7b4737bfedaba835988f01c6e0 SHA512: e072d6e5e1b5a80054ac21b25cfc31aaec71ac2afe7d8e75ddbb48c68d301a92d0e02077d5fa09b7be54ac68446de5c04a939bb7c2751e33b0df49b8ac51e966 Homepage: https://cran.r-project.org/package=ivx Description: CRAN Package 'ivx' (Robust Econometric Inference) Drawing statistical inference on the coefficients of a short- or long-horizon predictive regression with persistent regressors by using the IVX method of Magdalinos and Phillips (2009) and Kostakis, Magdalinos and Stamatogiannis (2015) . Package: r-cran-jaccard Architecture: arm64 Version: 0.1.2-1.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, r-bioc-qvalue, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-jaccard_0.1.2-1.ca2604.1_arm64.deb Size: 82416 MD5sum: 7d4bc584c00f0f3090a9f26b255e108e SHA1: e62a756be66e95c221dcca25a8a6ade068a8086a SHA256: 19d3d2e33aba6f49bef6fcc8317fe359b0d6c7ded9a3d1ff93a4c76387c58ed2 SHA512: 64e0de68f13fa5dccf364776f94a44eee0928efc8476e4cf21df651e028fec494bd4b05bd75707402a0fd7907572f90f6082e86b570489a51898b41d4fc1d967 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: arm64 Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4100 Depends: libblas3 | libblas.so.3, libbz2-1.0, libc6 (>= 2.38), libcurl4t64 (>= 7.18.0), libgcc-s1 (>= 4.2), liblapack3 | liblapack.so.3, liblzma5 (>= 5.1.1alpha+20120614), libstdc++6 (>= 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_arm64.deb Size: 2761822 MD5sum: a5a70c8a1c138a4bd63ad2c9914567b3 SHA1: 51b19043024b0a2a9b61e6e6e7f33c9a7e738ac0 SHA256: f36652023f5e19150eaf2402ba59679e0ede2273850247265f732c26fda4e2f3 SHA512: 3e338b4ad8d248c41376da7fccdb9b2eab602a959da4c3d817db3212502d413706117ffa751a10dcee41e70a9ff12e844cc9ef6894ccd9255050cba478be700a Homepage: https://cran.r-project.org/package=jackalope Description: CRAN Package 'jackalope' (A Swift, Versatile Phylogenomic and High-Throughput SequencingSimulator) Simply and efficiently simulates (i) variants from reference genomes and (ii) reads from both Illumina and Pacific Biosciences (PacBio) platforms. It can either read reference genomes from FASTA files or simulate new ones. Genomic variants can be simulated using summary statistics, phylogenies, Variant Call Format (VCF) files, and coalescent simulations—the latter of which can include selection, recombination, and demographic fluctuations. 'jackalope' can simulate single, paired-end, or mate-pair Illumina reads, as well as PacBio reads. These simulations include sequencing errors, mapping qualities, multiplexing, and optical/polymerase chain reaction (PCR) duplicates. Simulating Illumina sequencing is based on ART by Huang et al. (2012) . PacBio sequencing simulation is based on SimLoRD by Stöcker et al. (2016) . All outputs can be written to standard file formats. Package: r-cran-jacobi Architecture: arm64 Version: 3.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 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_arm64.deb Size: 166060 MD5sum: 6ee0c9ad18d3dbcc2b8e32bde337e44c SHA1: 075283649c648573f5f0f91c05c99c3d4d098f4b SHA256: 456e3ce940c16fad1cbec55c416bff8a8b3bcd1eb07447497c0cf1b317adb1ee SHA512: 730e49d7f1fff0f90f2e3bfd415cb6f979f3d8426b5daaad4c4d8f25659ac3d39967ffd9ad04c5e908905c67f550b280be17bf1f98e928a42b082dbed0b2a2b3 Homepage: https://cran.r-project.org/package=jacobi Description: CRAN Package 'jacobi' (Jacobi Theta Functions and Related Functions) Evaluation of the Jacobi theta functions and related functions: Weierstrass elliptic function, Weierstrass sigma function, Weierstrass zeta function, Klein j-function, Dedekind eta function, lambda modular function, Jacobi elliptic functions, Neville theta functions, Eisenstein series, lemniscate elliptic functions, elliptic alpha function, Rogers-Ramanujan continued fractions, and Dixon elliptic functions. Complex values of the variable are supported. Package: r-cran-jacobieigen Architecture: arm64 Version: 0.3-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 223002 MD5sum: 973ae89dc9cfd684677095a82c0d0abf SHA1: 230599f61d4829621bfa4af21795749e0bd47ed1 SHA256: b84e66571cd97ca485d4aa0762911247128b4b4862a3a1dfd418eb4e2dd80d73 SHA512: a2808da6a8d80abf1bcc2e74999f6d4a4db8e468d7f130e1fe44f52b93dfe288c27af9c52d0e9ef246100279f4b5a489de293c0be37b0741bced955c3cdb7ca6 Homepage: https://cran.r-project.org/package=JacobiEigen Description: CRAN Package 'JacobiEigen' (Classical Jacobi Eigenvalue Algorithm) Implements the classical Jacobi algorithm for the eigenvalues and eigenvectors of a real symmetric matrix, both in pure 'R' and in 'C++' using 'Rcpp'. Mainly as a programming example for teaching purposes. Package: r-cran-jade Architecture: arm64 Version: 2.0-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2518 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2281104 MD5sum: 9f8e46a6b12a1569d34ffec5b59134b7 SHA1: fa15eafb34fdf3df45e426709be6d64f7e0d2435 SHA256: a7d12afec32887844731fe6d39248ace072a7024c2996b3a4e30045656e93dd5 SHA512: 46beefb4188a28728f399d483032906af628040b14bdc56b08b775e02fd55f3580321fe587ad7523190b3f8a5975c8fd6efe5933545470b51050d8882f93a450 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: arm64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1067 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_arm64.deb Size: 502380 MD5sum: cd2476c620005fdc2ec62f2ebc2547cc SHA1: 205f7ecb8ab8797c351ff7ef7c0a7d9b2502acc4 SHA256: 7d9c41224d2d05edb7f08aa69e52da20b3576be75e53a5104a397a620b9b844b SHA512: f3f264920b4dc9aa4198157c569ebd4037f4c72bad63ac90f402e4d5254cefb12348243fbf04a8b905ac4dc9e4e37d86f7e479cccd6aee1616ff059a99127ae1 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: arm64 Version: 0.6-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 54402 MD5sum: f1bf95debaae242aa8b031c9dc040839 SHA1: a4cb35090bd1fe886304b192771be5d29860c7a8 SHA256: 81d8accb860f79f68b7446dce886a80ef41146e264fd13563df9879eeb8271d9 SHA512: 953c824ecc411d3e6312512093b1c3740dd15e94ddd0cb8f34d8af0d3f470da9e7a87eeaeb13c9f77ccc8decc2aaec119fbf3eb317d4204b301137381f17765a 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: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13012 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 5188080 MD5sum: 4088606c5cfd096f9690580e6be2cbff SHA1: bcd1d6cc4114403afcf2b11b2e8d2e64316e76b0 SHA256: c95dd1336bce402c5a654418b3be4b67b76509a80685de7d175e7655b2ea0207 SHA512: b7ceada024c8cb90c5aed8a7c1b6c273f7e58ec1979e481aeeda06aa6594ce79d77e57d09d1eef2492c03676f4ba6a23185cb2fd3642640aa6ce4d3efbe7a09b 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: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 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_arm64.deb Size: 103788 MD5sum: bca31ef0cc24f883a748a55e4eef25b4 SHA1: 6d5122eb074a7f73df08d0d6da7d7dc6f70c00f8 SHA256: b234cf7f2b37dace6221185fb69287341679c8da03c8ac860a6fc456f0e8b0a9 SHA512: db4281f3ee0a9ffc59ad2e37982c56b69de37cc549cabb7baf0b1b27d6d86a9c77452c390a8ecab4932e76215784d591b3ca60dfab9b05d849cb3fa93cf479fc 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-jinjar Architecture: arm64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 733 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-fs, r-cran-jsonlite, r-cran-rlang, r-cran-cpp11 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-jinjar_0.3.2-1.ca2604.1_arm64.deb Size: 253132 MD5sum: 4f57051290a14fdb1449bc278c1e14d2 SHA1: ab48caa0e610b26ed24a25d4878382adf0873eb1 SHA256: b91af8e1c43c3724bd1eb29db65a11735444a0a1aa1ad60a4dc86db713b59c7f SHA512: 326370fd43f6824dfb7a481d415ff3a3f908687fbcf7f57d30c51276be9e418ba7fb86ac069d342b562f8f3c058874518311f38f8fdd54f332579411cc38256a Homepage: https://cran.r-project.org/package=jinjar Description: CRAN Package 'jinjar' (Template Engine Inspired by 'Jinja') Template engine powered by the 'inja' C++ library. Users write a template document, using syntax inspired by the 'Jinja' Python package, and then render the final document by passing data from R. The template syntax supports features such as variables, loops, conditions and inheritance. Package: r-cran-jlpm Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 481 Depends: libc6 (>= 2.38), 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_arm64.deb Size: 317126 MD5sum: ece90c7f663c090af5e35749829f59af SHA1: a73becc7247845bfc5fc3e1a89ac21c1d2895ed4 SHA256: fceb280c057fb8fda724e313e188be1341940dd9ce31cc510f7828949334fba7 SHA512: 243dc3ccbe47c20268999585cc13e64c076455eb22d9935de91fb11972f73dbafab25bfce30e096eb7bbca15c1ca61d2c4fef557f27073814d63bea1d16a6cdd 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: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 Depends: libc6 (>= 2.34), r-base-core (>= 4.5.0), r-api-4.0, r-cran-juliacall, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-jlview_0.1.0-1.ca2604.1_arm64.deb Size: 53182 MD5sum: 60f4b34ae68dba23a3a0339767482018 SHA1: 3bc3df99ff775d2ee5f11ade171a4b2923af48cf SHA256: 8f3d7f6cd0f511aff419ec20b335d8be29da39f7c07a021d3facf4f46ae0d8b0 SHA512: e5491ecc3f5e5af7fcf9f9dad46f6ff20e68e4b849999e132710940723ce28c294f8bc2029a57a144191c412cd3be9b080e3efaa2c310b3f647d184bf866d75d Homepage: https://cran.r-project.org/package=jlview Description: CRAN Package 'jlview' (Zero-Copy Julia to R Array Bridge via ALTREP) Provides zero-copy R views of Julia-owned arrays by implementing ALTREP (Alternative Representations) classes that return pointers directly into Julia's memory. 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Package: r-cran-jmatrix Architecture: arm64 Version: 1.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1578 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_arm64.deb Size: 328070 MD5sum: 11c3e738984ea81511ce4b1cce0baa45 SHA1: f21016843ff4d172e2c68c1ed1dd9a701c1216c9 SHA256: 825c9c121a893e889bffe2bdee07dac4507baa146a9d9c0e847c1b0e1c64f474 SHA512: b23708595e2c21b9c7b8c2c098c99a6170190e3a3177edddbdb734a60b2db862ee8878982761a42a11b64497f5c0f79f9bde1abe46ed9f4b3e7ff103b08683b0 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: arm64 Version: 0.6-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1613 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_arm64.deb Size: 1075344 MD5sum: 9d3177fd8139197ce21d639fa9d0085e SHA1: e8fd3e19c8ea0178bf265ca64d6602a9d1a612d6 SHA256: f5e6de643f523988ca6393e216514273d08410948ab66bf9df70e21207735c06 SHA512: d2ecb00c1e7b02fdd1efccbb1a377ee1e9d2a2fad7e1acacf868f4a6322933f1e3d6712f9f2be1b755b2d41ff357cc12c74212c9f2dd93aeddaaa105cf3922a3 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). Package: r-cran-jmbayes Architecture: arm64 Version: 0.9-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1602 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-nlme, r-cran-survival, r-cran-doparallel, r-cran-rstan, r-cran-mass, r-cran-foreach, r-cran-rcpp, r-cran-jagsui, r-cran-xtable, r-cran-shiny, r-cran-hmisc, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-jmbayes_0.9-0-1.ca2604.1_arm64.deb Size: 1195302 MD5sum: 98617f21fbcb235bc1c53d42ec15f6bd SHA1: de6966b9cbb1bf2faff56d0af9f374a67132cf95 SHA256: 6b316712174f8c07a50d55c5550927ca45031cea732e2dddb3d9ce0412235cbc SHA512: cdae06445863b99b05accf5ee1310ee0b6a54016833c90015815685bb160fbd7bd9ec4dbb994f2e21388c1592c6a2d27bd8868d3936598fd3104fc7da22a5fc7 Homepage: https://cran.r-project.org/package=JMbayes Description: CRAN Package 'JMbayes' (Joint Modeling of Longitudinal and Time-to-Event Data under aBayesian Approach) Shared parameter models for the joint modeling of longitudinal and time-to-event data using MCMC; Dimitris Rizopoulos (2016) . Package: r-cran-jmcm Architecture: arm64 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_arm64.deb Size: 1280332 MD5sum: 45d5bcaa15c88a6a2aacf71343603c62 SHA1: 0d91d2980a04dee2e9940b96509967e30fa1c5d6 SHA256: 730af2979295e2a3791153a8206121f5800d0eeeaedbca7b75824d8327cc2f22 SHA512: 2ec82105ad8d3b52e39051001e10dca3c08c2551ac9b7541f483e7b8361d7e356354d2757395b1934bb169fd22de54ea662a84e4622a892e57b67c51a864da91 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. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Armadillo' C++ library for numerical linear algebra and 'RcppArmadillo' glue. Package: r-cran-jmh Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1331 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_arm64.deb Size: 591070 MD5sum: 13e6a4995cf1c48a7d1425cc6e641516 SHA1: 859516a6c4a45d5a970ec9203847c135055819a0 SHA256: 6dc0ac0799ff7df08414741a1bc4eb0b0d16f667c9c9952353e4308ff184500b SHA512: deb9b5e7f21e5cbcc2068ac5e9d03967c4100feeef6185a996cd683711e761bca7669b238ad26f5628beebde28951076795ad4d1b24591fb8c7985d856ef6e40 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'. Package: r-cran-jmi Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 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-jmi_0.1.0-1.ca2604.1_arm64.deb Size: 59970 MD5sum: 5885c0af659d720fd98b66345ae43dcb SHA1: 14f87274c151a0424edc179588255ef3cae52569 SHA256: 92ef42d19057fd2fa43fc4e4eb42c0c6bddcdcf845e9e346bd68a3252d689998 SHA512: 01f69be867b2e2db0d45917611fbcd6d33a46fa3d11f80ad573ad5aefe5fe77fd79d98424154e93b5dc1576b50454b04d0e99eb78d541d38bbbb73b02480d608 Homepage: https://cran.r-project.org/package=JMI Description: CRAN Package 'JMI' (Jackknife Mutual Information) Computes the Jackknife Mutual Information (JMI) between two random vectors and provides the p-value for dependence tests. See Zeng, X., Xia, Y. and Tong, H. (2018) . Package: r-cran-jmotif Architecture: arm64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1606 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-jmotif_1.2.1-1.ca2604.1_arm64.deb Size: 1325200 MD5sum: 10a1306858235d6daae3e3a92667311d SHA1: 465b6e2cf313bd815715675cc65a6a2b5813645f SHA256: fd51c5d53bebc7709b6e270ecbd07ea137c3e3423762b0b07f5b6256e5c8b548 SHA512: ec62149d3c1ec7ee399f1ed9bf931d0df797ba2ea4de454f41ac181fb11a5be2be424bd768ba9154033207ae9ca22eed145646090096e427542c4d11ad2b7d6e Homepage: https://cran.r-project.org/package=jmotif Description: CRAN Package 'jmotif' (Time Series Analysis Toolkit Based on Symbolic AggregateDiscretization, i.e. SAX) Implements time series z-normalization, SAX, HOT-SAX, VSM, SAX-VSM, RePair, and RRA algorithms facilitating time series motif (i.e., recurrent pattern), discord (i.e., anomaly), and characteristic pattern discovery along with interpretable time series classification. Package: r-cran-jmvconnect Architecture: arm64 Version: 2.5.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-jmvcore, r-cran-evaluate, r-cran-rappdirs, r-cran-httr, r-cran-rcpp, r-cran-bh Filename: pool/dists/resolute/main/r-cran-jmvconnect_2.5.7-1.ca2604.1_arm64.deb Size: 70632 MD5sum: ea762216d9e8663b8aaee4d984ee92fd SHA1: d292d4e6d0844b274f42383f1f4a9e0ed7ba0ee4 SHA256: b2c68cdcbbb98769432720364c28b018a640abb089226224b92cb403f4a4f973 SHA512: 8671652603c3fdb853f28b806c5c513b62da3087969fda309ffbdf5a3e37c86724e1ee063bf0e10ea4635b2c70b6444da34faa9467348a37b073b304008f7eac Homepage: https://cran.r-project.org/package=jmvconnect Description: CRAN Package 'jmvconnect' (Connect to the 'jamovi' Statistical Spreadsheet) Methods to access data sets from the 'jamovi' statistical spreadsheet (see for more information) from R. 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The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project was funded by the Medical Research Council (Grant number MR/M013227/1). Package: r-cran-jointdiag Architecture: arm64 Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 146 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-jointdiag_0.4-1.ca2604.1_arm64.deb Size: 55726 MD5sum: 49548fda3297b54be811185f5d8d529c SHA1: 966c5c085357575f72780b301a9409052f55e7e6 SHA256: 086cb4222162f293225ffdd3d93e8d6eea67061523900cbf65f7b36ec79b3878 SHA512: a51d98205acbe936c9fc2f1baef73fbe3e8a5e92ea5c0d5c41003803d8957d2ab9aa45dfc17ce9d71c0e066983d508efff40aa82eb32c503df6c494b4fbff1f3 Homepage: https://cran.r-project.org/package=jointDiag Description: CRAN Package 'jointDiag' (Joint Approximate Diagonalization of a Set of Square Matrices) Different algorithms to perform approximate joint diagonalization of a finite set of square matrices. Depending on the algorithm, orthogonal or non-orthogonal diagonalizer is found. 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) . Package: r-cran-jointseg Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1634 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-acnr, r-cran-matrixstats, r-bioc-dnacopy Suggests: r-cran-pscbs, r-cran-r.cache, r-cran-digest, r-cran-changepoint, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-jointseg_1.0.3-1.ca2604.1_arm64.deb Size: 1052404 MD5sum: cf4682c0cc762dad3c452f980c045f6c SHA1: 3fdc7c56a4d80992d7d345ba827fa6f37f12ac3c SHA256: 74cd45eef06e88ac121b753eaa7fb2c1865c9398e20aae57fd8ee6580c4676a1 SHA512: 1ff07bb38fbc2e1d6277972a2a720773f13b45f9d0638b7af1dbe54d067cc68de1d9bb5c158ca8b1930f2c1bdc23eac0d469fb77662393334f33c6a941c8506d Homepage: https://cran.r-project.org/package=jointseg Description: CRAN Package 'jointseg' (Joint Segmentation of Multivariate (Copy Number) Signals) Methods for fast segmentation of multivariate signals into piecewise constant profiles and for generating realistic copy-number profiles. 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) . Package: r-cran-jomo Architecture: arm64 Version: 2.7-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2270 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lme4, r-cran-survival, r-cran-mass, r-cran-ordinal, r-cran-tibble Suggests: r-cran-mitml Filename: pool/dists/resolute/main/r-cran-jomo_2.7-6-1.ca2604.1_arm64.deb Size: 2012816 MD5sum: 726de0a887a8891bceb78e6797029850 SHA1: 7d086491b0ee55e0b7f9a97347e1d2826f398c03 SHA256: b142348af1cb6c4b708c749edd25bc9f5218eacc8f9d62adbf9620fee665d780 SHA512: 202363969dadf8ba8c0ba96a1c193495022e3bc79d7589b43379aa08fa86bc93b5bbbdff2551ee5f3b15e39009c4cd1e94da6b300da02d3650e2fc4b8cb0a745 Homepage: https://cran.r-project.org/package=jomo Description: CRAN Package 'jomo' (Multilevel Joint Modelling Multiple Imputation) Similarly to package 'pan', 'jomo' is a package for multilevel joint modelling multiple imputation (Carpenter and Kenward, 2013) . Novel aspects of 'jomo' are the possibility of handling binary and categorical data through latent normal variables, the option to use cluster-specific covariance matrices and to impute compatibly with the substantive model. 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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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Please cite the manuscript corresponding to this package [Lyu, P. et al., (2023), ]. 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Package: r-cran-kamila Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 138576 MD5sum: 96b00adb1d20a1aacd16080c99abe769 SHA1: edf40f18d99bf218433cb9b6437af8807210801c SHA256: 799b7f327f9d9d1f1af7dd9ee8ffe8322fde256718d5bb081e7b6b627b358167 SHA512: 7a079f269bf79d5c937948ea6ec05d0b9a52486206482736f04a19a5b8eb7e68a0500f1ba2bb30456fe006e01c166d0cbb691fa89a6268f94426368511554e9e Homepage: https://cran.r-project.org/package=kamila Description: CRAN Package 'kamila' (Methods for Clustering Mixed-Type Data) Implements methods for clustering mixed-type data, specifically combinations of continuous and nominal data. Special attention is paid to the often-overlooked problem of equitably balancing the contribution of the continuous and categorical variables. This package implements KAMILA clustering, a novel method for clustering mixed-type data in the spirit of k-means clustering. It does not require dummy coding of variables, and is efficient enough to scale to rather large data sets. Also implemented is Modha-Spangler clustering, which uses a brute-force strategy to maximize the cluster separation simultaneously in the continuous and categorical variables. For more information, see Foss, Markatou, Ray, & Heching (2016) and Foss & Markatou (2018) . Package: r-cran-kanjistat Architecture: arm64 Version: 0.14.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2707 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_arm64.deb Size: 1821472 MD5sum: 659efe548a0f8ee7f0c5f6708c265ab3 SHA1: fe86492288c0d3483a8745e2fe37b8b8c70da026 SHA256: 6ba566db1779730832c066f668d597d8a8c112e89af5888649b7a61f68a9006b SHA512: e63e43bc089b290d91c7e5302d8de1d3b121ca842c9dc574d63ddcdc4c6b943f93fb59578d483eba96ef608a9ea3b296a86d4d71ca48c7835807d815402305f5 Homepage: https://cran.r-project.org/package=kanjistat Description: CRAN Package 'kanjistat' (A Statistical Framework for the Analysis of Japanese KanjiCharacters) Various tools and data sets that support the study of kanji, including their morphology, decomposition and concepts of distance and similarity between them. Package: r-cran-kappalab Architecture: arm64 Version: 0.4-12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 777 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 564432 MD5sum: f3e9a393513459c9e32b4b58fe5f56d4 SHA1: 536c15081fadccc83f35b93a3087206f57ba4bb4 SHA256: 5057df4d6aade2ff16379ec0ddb29174e95e545d66b2b502f23cd915634f4849 SHA512: 09f8e0c6640e1b95a8f44e0c7750b556a8c19a72f476944976defb3b6c7c34a55b28422ef889a3875b711f02ec1b863491dd74e6501bdeeba752d359897c751e Homepage: https://cran.r-project.org/package=kappalab Description: CRAN Package 'kappalab' (Non-Additive Measure and Integral Manipulation Functions) S4 tool box for capacity (or non-additive measure, fuzzy measure) and integral manipulation in a finite setting. It contains routines for handling various types of set functions such as games or capacities. It can be used to compute several non-additive integrals: the Choquet integral, the Sugeno integral, and the symmetric and asymmetric Choquet integrals. An analysis of capacities in terms of decision behavior can be performed through the computation of various indices such as the Shapley value, the interaction index, the orness degree, etc. The well-known Möbius transform, as well as other equivalent representations of set functions can also be computed. Kappalab further contains seven capacity identification routines: three least squares based approaches, a method based on linear programming, a maximum entropy like method based on variance minimization, a minimum distance approach and an unsupervised approach based on parametric entropies. The functions contained in Kappalab can for instance be used in the framework of multicriteria decision making or cooperative game theory. Package: r-cran-kbal Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 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_arm64.deb Size: 204882 MD5sum: 705ffbd52ee79464681533171653093c SHA1: 01be9116a3bc8db9076b0d9b2fd6cd96a11f1bd2 SHA256: e35fd449301b9ecea5043b4e687ccfe25b779777ccf26b973b0933453fb7ac67 SHA512: 9b8ab1fdf358e6a5854498d6f4c14738c1d59201cb865c0b61de33be68db82ed7c4a5c7c2aa1ff061d9cd98a2fca4f1330e363836de8cbb42b5ed9310276c5bb Homepage: https://cran.r-project.org/package=kbal Description: CRAN Package 'kbal' (Kernel Balancing) Provides a weighting approach that employs kernels to make one group have a similar distribution to another group on covariates. This method matches not only means or marginal distributions but also higher-order transformations implied by the choice of kernel. 'kbal' is applicable to both treatment effect estimation and survey reweighting problems. Based on Hazlett, C. (2020) "Kernel Balancing: A flexible non-parametric weighting procedure for estimating causal effects." Statistica Sinica. . Package: r-cran-kcprs Architecture: arm64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 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_arm64.deb Size: 131004 MD5sum: e9d71c549872b01b12c398fe1cf74b44 SHA1: 791a1fb4880d41b2e7afeaf69c095f1d77339f4d SHA256: d099fcdf0c485d46c3cdcc7c783f7b7778425334c884079885ceaafcb2078367 SHA512: ed76ffebd2f213f2f4bbc588f70481ec5ebdec3930e534cae3d91420e85d72fcc3b297e64804ec0d06e0bd55ac34019be82a3c4439815fb76d76c48cc02f8cdb Homepage: https://cran.r-project.org/package=kcpRS Description: CRAN Package 'kcpRS' (Kernel Change Point Detection on the Running Statistics) The running statistics of interest is first extracted using a time window which is slid across the time series, and in each window, the running statistics value is computed. KCP (Kernel Change Point) detection proposed by Arlot et al. (2012) is then implemented to flag the change points on the running statistics (Cabrieto et al., 2018, ). Change points are located by minimizing a variance criterion based on the pairwise similarities between running statistics which are computed via the Gaussian kernel. KCP can locate change points for a given k number of change points. To determine the optimal k, the KCP permutation test is first carried out by comparing the variance of the running statistics extracted from the original data to that of permuted data. If this test is significant, then there is sufficient evidence for at least one change point in the data. Model selection is then used to determine the optimal k>0. Package: r-cran-kde1d Architecture: arm64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 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_arm64.deb Size: 139930 MD5sum: 0fc8c405bc2d24364e9fcdd876c1cdf4 SHA1: c2e740bd6b7ffef2623a05a6fb239add7a24d9c0 SHA256: 726397568ea4e1a18206ad13388f67030ddfd6a64870ee851fb58e780d600eb0 SHA512: 03c0c0ea39fe32ec03f624fdf03b043f2829da7dbaaaed5f6d26f698564089dfa90623b1d1cc78914e65f944f6208af3020335c378c94ec5241c76604792dceb 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: arm64 Version: 0.9.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1167 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_arm64.deb Size: 887574 MD5sum: 49412a3718ec0df10039298c25403293 SHA1: 4727b1768e0695d056cc6e4460fa68627dd741f7 SHA256: 030349847ccc2e092eea0bf16af025c09a3d3f8fc203d9df4c28519edbee2d1f SHA512: f5ef29a99d802de1936ba3b0ec531285c4dfde12b950f006ce902a505c3d8940ed0e8869fd907ad234ea23c6c809f5556e766a0c7060109926bc3350c3388fa0 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) . Package: r-cran-kdemcmc Architecture: arm64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 259 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-kdemcmc_0.0.2-1.ca2604.1_arm64.deb Size: 84162 MD5sum: 85bd1a166db7b343bdf26eacc8353639 SHA1: 7dbd0967b737176b9505666dedc49fb7a67640e3 SHA256: b8e2e01c07853bdc4c1acbcaeceadf0d08e7b33d8f2957fb7ffdc9bff7549c8d SHA512: 0d1d047e2f1a3575b83d0feb315feceb45aca419e8b231474cb7bcd8119cb0fcb3ed46a6b4d6d7741aee39800e0af523ec685ac2264cf6b7d660c2128b351c2a Homepage: https://cran.r-project.org/package=KDEmcmc Description: CRAN Package 'KDEmcmc' (Kernel Density Estimation with a Markov Chain Monte Carlo Sample) Provides methods for selecting the optimal bandwidth in kernel density estimation for dependent samples, such as those generated by Markov chain Monte Carlo (MCMC). Implements a modified biased cross-validation (mBCV) approach that accounts for sample dependence, improving the accuracy of estimated density functions. Package: r-cran-kdevine Architecture: arm64 Version: 0.4.6-1.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-mass, r-cran-rcpp, r-cran-qrng, r-cran-kernsmooth, r-cran-cctools, r-cran-kdecopula, r-cran-vinecopula, r-cran-doparallel, r-cran-foreach Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kdevine_0.4.6-1.ca2604.1_arm64.deb Size: 247796 MD5sum: 52f3a877732458361acdef4719fa7b77 SHA1: 43ab70174a48dc2479ca5f1d4230a660d8b98f37 SHA256: 5be7998fc3d0791aaa9eee579c0fdb6d097db9b7d9c84dc189a55a3510d2ec26 SHA512: d0691765a9f6f80fb23efb2221754d81ce354642f56ab31bed729e8279c036b6c1bb8ada6dbb0c2ffca1afd535d157c81b071281f7addb188b0b5a773951f98f Homepage: https://cran.r-project.org/package=kdevine Description: CRAN Package 'kdevine' (Multivariate Kernel Density Estimation with Vine Copulas) Implements the vine copula based kernel density estimator of Nagler and Czado (2016) . The estimator does not suffer from the curse of dimensionality and is therefore well suited for high-dimensional applications. Package: r-cran-kendall Architecture: arm64 Version: 2.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 140 Depends: libc6 (>= 2.27), r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot Filename: pool/dists/resolute/main/r-cran-kendall_2.2.2-1.ca2604.1_arm64.deb Size: 41992 MD5sum: aa026a12997db6a4aa66dd5f0e759f8e SHA1: 0f1efc1eda8c1531ed121a6167d88a3ce3e67b6c SHA256: bad42717842c5d35e1da1f97b40f9f4e836c7d45d41bf0ad1e08cabd22edff76 SHA512: 1ca8afb487c4b94ef6920cf1935ddce1e2644f4fdd6b0debd75b4e8c35f793bb08b6d732db5002ffebf6100df177f7c0ddeccdb5e548da180310d44cc9f01779 Homepage: https://cran.r-project.org/package=Kendall Description: CRAN Package 'Kendall' (Kendall Rank Correlation and Mann-Kendall Trend Test) Computes the Kendall rank correlation and Mann-Kendall trend test. See documentation for use of block bootstrap when there is autocorrelation. Package: r-cran-kendallknight Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 172 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp4r Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kendallknight_1.0.1-1.ca2604.1_arm64.deb Size: 55078 MD5sum: c5cdde7a0a6f1f6d6b226aef571e317a SHA1: 09d648c9134f8aa3602395fe087e769add82bf46 SHA256: d27f735f577d7f51d3596a974557e031ce451abaa0d6066bde6e6f8f4a56adda SHA512: dddadc07c6bc819e6959d00fe9061882f198830a6bc94d8b7e1650f3ea4b1b900cf4f233ec7ad092ec11b291bf81fa98e9af59592e61cd6731ad465937d4151b Homepage: https://cran.r-project.org/package=kendallknight Description: CRAN Package 'kendallknight' (Efficient Implementation of Kendall's Correlation CoefficientComputation) The computational complexity of the implemented algorithm for Kendall's correlation is O(n log(n)), which is faster than the base R implementation with a computational complexity of O(n^2). For small vectors (i.e., less than 100 observations), the time difference is negligible. However, for larger vectors, the speed difference can be substantial and the numerical difference is minimal. The references are Knight (1966) , Abrevaya (1999) , Christensen (2005) and Emara (2024) . This implementation is described in Vargas Sepulveda (2025) . Package: r-cran-kere Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 603 Depends: libc6 (>= 2.17), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-kere_1.0.0-1.ca2604.1_arm64.deb Size: 386322 MD5sum: 4d5051c5a336b5fa78c2f49787a110a4 SHA1: 1fb44359696d8389c7e37c58678e5ad10842ff45 SHA256: b03db05c812438698431998105175cd8a5148d7d992ecea944ecc4ccfdaeed09 SHA512: b883f981d99e1f7a11342e90b81a4ac3692a581080c7aef9382116222d763498b4e2e3e689870306e13887027da14ffc19ecb5e3acf3541db8b1dd5f08e44009 Homepage: https://cran.r-project.org/package=KERE Description: CRAN Package 'KERE' (Expectile Regression in Reproducing Kernel Hilbert Space) An efficient algorithm inspired by majorization-minimization principle for solving the entire solution path of a flexible nonparametric expectile regression estimator constructed in a reproducing kernel Hilbert space. Package: r-cran-kergp Architecture: arm64 Version: 0.5.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1476 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-testthat, r-cran-nloptr, r-cran-lattice, r-cran-mass, r-cran-numderiv, r-cran-doparallel, r-cran-dofuture Suggests: r-cran-dicekriging, r-cran-dicedesign, r-cran-inline, r-cran-foreach, r-cran-knitr, r-cran-ggplot2, r-cran-reshape2, r-cran-corrplot Filename: pool/dists/resolute/main/r-cran-kergp_0.5.8-1.ca2604.1_arm64.deb Size: 1243930 MD5sum: b1db13bfc6f2475d3cd4b5ac3d6a44f8 SHA1: 7fab0ca71f21ce761f504a1da8cabc5b8cc38a44 SHA256: 8eb049e95b77ad8c42a5b5158905f30cb0b5e24e2c6a69ee4a2d660692c9fc89 SHA512: 2bb25031cc5535f4dde12fdab63905ee5d31f922e7fa1150feff1fbad9c6be5308e933234526e14024d54d5eb2591d43b8370e3c59a69006c8269a2c9d63a7a5 Homepage: https://cran.r-project.org/package=kergp Description: CRAN Package 'kergp' (Gaussian Process Laboratory) Gaussian process regression with an emphasis on kernels. Quantitative and qualitative inputs are accepted. Some pre-defined kernels are available, such as radial or tensor-sum for quantitative inputs, and compound symmetry, low rank, group kernel for qualitative inputs. The user can define new kernels and composite kernels through a formula mechanism. Useful methods include parameter estimation by maximum likelihood, simulation, prediction and leave-one-out validation. Package: r-cran-kerndwd Architecture: arm64 Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 620 Depends: libblas3 | libblas.so.3, 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-kerndwd_2.0.3-1.ca2604.1_arm64.deb Size: 416006 MD5sum: bb370583f7bc9a32fb61e738a6fbb266 SHA1: fecefc1cc158ec0899f8baeb2e3682b655cc9de4 SHA256: d7872d0edf3f7a128fd34e0c3204923616e6edd036f7b227f209235f4c33c210 SHA512: b5cfae977b5d88282d073383c99197da040c234ab0aa7c5e485ab7e06b366f32f9fb4dc0628e362ddba98c94ffe8281d86f253f60da8c9359d407d1ea5b8a1a8 Homepage: https://cran.r-project.org/package=kerndwd Description: CRAN Package 'kerndwd' (Distance Weighted Discrimination (DWD) and Kernel Methods) A novel implementation that solves the linear distance weighted discrimination and the kernel distance weighted discrimination. Reference: Wang and Zou (2018) . Package: r-cran-kernelboot Architecture: arm64 Version: 0.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: libc6 (>= 2.17), 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-future.apply, r-cran-parallelly Suggests: r-cran-covr, r-cran-testthat, r-cran-ks, r-cran-kernsmooth, r-cran-cramer Filename: pool/dists/resolute/main/r-cran-kernelboot_0.1.10-1.ca2604.1_arm64.deb Size: 106990 MD5sum: d4a656a8750f7d994ce02373c2ac23ce SHA1: 68b0286693c1e6f342ad666e9ff9ff606eb5a463 SHA256: ed0cdca909e6d3f701a54f2cf88ff2eb76727f139cb7724a3b7a3c5b2751ed22 SHA512: c0c284e9bed29ef632a2a76ec8facd74a2745ffafe0f3a04863ccf45478cfe2c208a5b3a4e147745007ba1b9da8deed02dd07b99cdddf1e1c1b1c7aafe238bae Homepage: https://cran.r-project.org/package=kernelboot Description: CRAN Package 'kernelboot' (Smoothed Bootstrap and Random Generation from Kernel Densities) Smoothed bootstrap and functions for random generation from univariate and multivariate kernel densities. It does not estimate kernel densities. 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Package: r-cran-kissmig Architecture: arm64 Version: 2.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 898 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 767732 MD5sum: 657f53df86b02b789114c9cfc0a03710 SHA1: ea0032742dfeaa4d7124837e16bf4a0fa6da577f SHA256: 9403c22e1a37eeae72d4d3fff025134f869e387999c98f878dd0e466053cf36d SHA512: e9e9bb3894215c735fe9dca3f7f7f9d1f28f525168b23c77e59a334fc256c19acccb4d515618abe599e4f95eb875683ca7aec810f6513ada70728d60a6f46923 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: arm64 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_arm64.deb Size: 142338 MD5sum: f963d166e8c08741df05b75254a1b381 SHA1: 920d2d351261a12728f68f97a07faa279e739f0a SHA256: adfd3c0237d2941456f86af8e718680a35b097e928e65631bc356e5ff5a688f5 SHA512: 8661be4c322245bb52d38b14be35222b826a1fae016c6c98168285f89152b128cb3b1f66fee488279df6a9a49e1648b245e414c2f8ca01dc6d666f06d5b0fafe 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: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-kkmeans_0.1.3-1.ca2604.1_arm64.deb Size: 46448 MD5sum: 1f511121c9bcab082525e6600a43b8dd SHA1: 9ca632615be173e46efadbe6396ebc1f32ed08cf SHA256: 81bda9932ad63329bd3872090f58d254d8078e3931f903b44bb7447f1728662d SHA512: 59f37c01667a35d21c0050dd02155544ea65a812a3f752fdea0f06472a3008cadae9b1fff44877c5b5c8d2cd49357c2781dd08e9ef50f13b354ae9fc232ed299 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: arm64 Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 758 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_arm64.deb Size: 456116 MD5sum: b26c541cd9ae0d90c8ced3f03ef28e6b SHA1: 2fc40311d287c27f6b1c1581868fb4fdd1169f7b SHA256: 436083b345d31d6b510ae556ec716768ba18002409ec4ae570c77082fbd1a6b8 SHA512: e5fc952d36dd9eca3e95125a80ad0b6fb7d7361b2b22719cdcf3373ae2fe9e2476db312789bc2d45c7fb35de08e27ca1c3bf3baddccdf934f4332b3367ec5148 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: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 107172 MD5sum: bdb306d6ab72ba6179e60d458285fcfc SHA1: e36688cad00c51b5c1a4f23ece4b35ed52a97cc8 SHA256: a687e4778c925c56dbd3bb40fa705e028da65e49138b10b7b9a64f5f8481216f SHA512: ebbeaa6872c05452a0036846e3b5a6e4a522f3b23d8f07e20129cacb347167a4aa4d8f72319ca6c82daeaa13789ca2b0994fee6186cc7d80c55f1aa5422e7bee 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: arm64 Version: 0.4-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 110822 MD5sum: 6b908f0eb4befef5ec928fb1e3dc8658 SHA1: b71630bd19b6a952f067365126fc0429d9c07310 SHA256: b04465da7a59de71ebea02b43a201facd7deb3523ec597dd5410f56720771b0e SHA512: 4531c806b8c9b9ce7a3e4be77d755ccdf5707e22616489a2f5ada22c3c718ac16ca190de667bc34c72c919b15a2e786476c95e609cbbd6c294a2aa243f736236 Homepage: https://cran.r-project.org/package=kmc Description: CRAN Package 'kmc' (Kaplan-Meier Estimator with Constraints for Right Censored Data-- a Recursive Computational Algorithm) Given constraints for right censored data, we use a recursive computational algorithm to calculate the the "constrained" Kaplan-Meier estimator. The constraint is assumed given in linear estimating equations or mean functions. We also illustrate how this leads to the empirical likelihood ratio test with right censored data and accelerated failure time model with given coefficients. EM algorithm from emplik package is used to get the initial value. The properties and performance of the EM algorithm is discussed in Mai Zhou and Yifan Yang (2015) and Mai Zhou and Yifan Yang (2017) . More applications could be found in Mai Zhou (2015) . Package: r-cran-kmer Architecture: arm64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 697 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-phylogram, r-cran-rcpp Suggests: r-cran-ape, r-cran-dendextend, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kmer_1.1.3-1.ca2604.1_arm64.deb Size: 415694 MD5sum: 13bf3bcf7d3c4ccaf986de454db0d5e1 SHA1: f0b855d0250e5b356a4b5267f0ffbd506aef7e7f SHA256: 2b32f309e3c373c5648b0b8766d8d54562c19858df5941fa350aea2bec313030 SHA512: fc9b7e3f19b721ed4b92834e9f70c390bef5658e90a3ce386f9562062776b25c942d2b17dda50eedf060b60390d02c87045289136c6c1c5f0c5c5c3847aa94ce Homepage: https://cran.r-project.org/package=kmer Description: CRAN Package 'kmer' (Fast K-Mer Counting and Clustering for Biological SequenceAnalysis) Contains tools for rapidly computing distance matrices and clustering large sequence datasets using fast alignment-free k-mer counting and recursive k-means partitioning. See Vinga and Almeida (2003) for a review of k-mer counting methods and applications for biological sequence analysis. Package: r-cran-kmertone Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1917 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_arm64.deb Size: 1429186 MD5sum: 8ee702fb14b37f941f1649ad169f56aa SHA1: 75761681785a7ce6afe1975b8fd39bfe931a0ad8 SHA256: ee190dee02509a2b2260a40a647d2186f1281b80ae50c1f4e71ec6dd4e4163cf SHA512: 85f456ec39a96e60826159aa52daf78378b870842f36ee846536009f2720ddac047e6c970f85d21b95d856d7731c40c23abbb348fc751302dc3697c08a357b8e 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: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6317 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 6285280 MD5sum: eb114ac2f177eb76d713119356a79262 SHA1: d368f9a35df6d6e732fdbebbb0820f24cf025117 SHA256: fc4351a56e38af1f9af56de61e164d9ce271b13ae102f367a6cf498b238e0563 SHA512: fbe4d32b0069b48a777735c44d761b91bec2f5a66d2549d6ea091cf6ec56e1baca9bf833a63c285c3b063cb17eac25bfe3061efbac88c1c2dd7dcd3f1614c257 Homepage: https://cran.r-project.org/package=KMT Description: CRAN Package 'KMT' (Khmaladze Martingale Transformation Goodness-of-Fit Test) Consider a goodness-of-fit problem of testing whether a random sample comes from one sample location-scale model where location and scale parameters are unknown. It is well known that Khmaladze-martingale-transformation method proposed by Khmaladze (1981) provides asymptotic distribution free test. This package provides test statistic and critical value of the test for normal, Cauchy, and logistic distributions. This package used the main algorithm proposed by Kim (2020) and tests for other distributions will be available at the later version. Package: r-cran-knn.covertree Architecture: arm64 Version: 1.1-1.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, 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_arm64.deb Size: 80226 MD5sum: b441894c3ee2c36b38fa833fd3c3706e SHA1: 0fbba2021b95c6da3b59c3854ec867f32a7d2869 SHA256: 51dfb599a715e2ceb43b80263ff31d87d375e7f1b12d75b2c5714132ba7b8388 SHA512: 0904a07e771aa3b52c2b01a10167cb23b092c07ecacf2c974b77366062cd1b5c17cf2e280f8f9b48a2b889096e92103968146e8e3f8fb1dd05fefb97d5f7be90 Homepage: https://cran.r-project.org/package=knn.covertree Description: CRAN Package 'knn.covertree' (An Accurate kNN Implementation with Multiple Distance Measures) Similarly to the 'FNN' package, this package allows calculation of the k nearest neighbors (kNN) of a data matrix. The implementation is based on cover trees introduced by Alina Beygelzimer, Sham Kakade, and John Langford (2006) . Package: r-cran-knnmi Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 150 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-knnmi_1.0-1.ca2604.1_arm64.deb Size: 57654 MD5sum: 13984a46808e1091f327694a4139d600 SHA1: e2bef848bafcbb1e9274f195f8d633311573dde5 SHA256: 63adec5f19c92e0e8bf9384497c660083b7d59f90f1bc6b73a053e2447266e5b SHA512: c6de1dc6008df9dda199b8e79b67ae762ecfdc87f7d12fdf6ef56d66b324f3c3c88ea79c88acb47973a63c7a85986da61ecf3d6875f78725823842418b432d6a Homepage: https://cran.r-project.org/package=knnmi Description: CRAN Package 'knnmi' (k-Nearest Neighbor Mutual Information Estimator) This is a 'C++' mutual information (MI) library based on the k-nearest neighbor (KNN) algorithm. There are three functions provided for computing MI for continuous values, mixed continuous and discrete values, and conditional MI for continuous values. They are based on algorithms by A. Kraskov, et. al. (2004) , BC Ross (2014), and A. Tsimpiris (2012) , respectively. Package: r-cran-kodama Architecture: arm64 Version: 3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3020 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rtsne, r-cran-umap, r-cran-rcpp, r-cran-rnanoflann, r-cran-matrix, r-cran-rcpparmadillo Suggests: r-cran-rgl, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-kodama_3.3-1.ca2604.1_arm64.deb Size: 2783510 MD5sum: ef04a3b7787e1f42a45d4828409316a2 SHA1: 9f4830632dde7c4f2a757a4c109c6c2ebce9da0d SHA256: 1e08ae458272736e0175b2c2096ed093bea67f934c6b9bee3050eff3ff8e38e1 SHA512: a8bbb191d241817edc4c8ec4751db2529c2a883698aa980f201823c9b9266105a45a7f7ea89bf8ab43b76011eba830f945b53f1596caf8ee87bd974944b3c70b Homepage: https://cran.r-project.org/package=KODAMA Description: CRAN Package 'KODAMA' (Knowledge Discovery by Accuracy Maximization) A self-guided, weakly supervised learning algorithm for feature extraction from noisy and high-dimensional data. It facilitates the identification of patterns that reflect underlying group structures across all samples in a dataset. The method incorporates a novel strategy to integrate spatial information, improving the clarity of results in spatially resolved data. Package: r-cran-kohonen Architecture: arm64 Version: 3.0.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1905 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 1702356 MD5sum: cd6f26c5dc2d65ac5dc312f859a306e1 SHA1: f4d6dfeee39ea2442907c83bf6d3761067e60624 SHA256: 8913f989b631d8ca96f92a6b2a7d4e08514784a8d6cfbd3b8cd39c8298c6e9fa SHA512: d5008b09e1ba4c19d5c19a08440706ed23c7067aa651a7199cbd551f3bbc4b594f33b273c73854a3645d438e91328f66f96e530991b99fe918c7b2d612c9a342 Homepage: https://cran.r-project.org/package=kohonen Description: CRAN Package 'kohonen' (Supervised and Unsupervised Self-Organising Maps) Functions to train self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM. Package: r-cran-konpsurv Architecture: arm64 Version: 1.0.4-1.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-survival, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-konpsurv_1.0.4-1.ca2604.1_arm64.deb Size: 115520 MD5sum: ca45d8420adddf68b801fcbd69b91044 SHA1: d4390e442b6f5bc2224065f8062a036e5e5eb2e3 SHA256: 77d9dac513dd67e33bfac5f04e1a034792f7fa25402dff8742d496392d947e61 SHA512: b96f48fb6a764ab115ab6478d01ebbfd8e515efd6b1384d8f2d6e4e524db66e17d4c9565bea96aa027e881068d96fca10fb01c0db76804d2d5d5b2bd56754e82 Homepage: https://cran.r-project.org/package=KONPsurv Description: CRAN Package 'KONPsurv' (KONP Tests: Powerful K-Sample Tests for Right-Censored Data) The K-sample omnibus non-proportional hazards (KONP) tests are powerful non-parametric tests for comparing K (>=2) hazard functions based on right-censored data (Gorfine, Schlesinger and Hsu, 2020, ). These tests are consistent against any differences between the hazard functions of the groups. The KONP tests are often more powerful than other existing tests, especially under non-proportional hazard functions. Package: r-cran-krige Architecture: arm64 Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 987 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 770716 MD5sum: 34ec361b37404002dd0ace7c511acc94 SHA1: f693945683fe3dc6a3157317381d9a8585f82464 SHA256: 1d0be5b86d8e828e63005c51b16d5fcaea3bdde382d564cfa450c7e247a9c73b SHA512: e9cedf9fd74313592164158d0da83a33f732adf8b20e3fbeda709b6595d7a24837ed243fd106cbd25e55a5e07e7ca55595d5bc80bda55244b5ffb6edbdae37b5 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: arm64 Version: 2022.10-17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-kyotil Suggests: r-cran-runit, r-cran-mass Filename: pool/dists/resolute/main/r-cran-krm_2022.10-17-1.ca2604.1_arm64.deb Size: 128740 MD5sum: 3d40e599b4cb98b12f2dcfd4a2e9b4c2 SHA1: 84a529c3a337be281a0a148bda9e387706480da4 SHA256: 31d77a21e4e35bc875429d9eb99dd5b90d67508bf727ef618b80ee3e8beda8fa SHA512: b556db4a0b0ccaaffac801431aec2bd1decfb07d642101714657b99633bb0a295d03bffab517d82229ba5adb90af66bcdb9319e6a6620b6f96f4a1727dbf4a52 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. 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The 'kstMatrix' package provides basic functionalities to generate, handle, and manipulate knowledge structures and knowledge spaces. Opposed to the 'kst' package, 'kstMatrix' uses matrix representations for knowledge structures. Furthermore, 'kstMatrix' contains several knowledge spaces developed by the research group around Cornelia Dowling through querying experts. 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Clustering procedure is carried out by minimizing the overall robust scale so-called tau scale. (see Gonzalez, Yohai and Zamar (2019) ). Package: r-cran-ktweedie Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 758 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tweedie Filename: pool/dists/resolute/main/r-cran-ktweedie_1.0.3-1.ca2604.1_arm64.deb Size: 467894 MD5sum: 9b924b215c679268fa7b270bf2bb75c2 SHA1: 710d424bf72aee2e84190ba4dfef2c29d8e80fba SHA256: 55971205ffcb3b8d176fe4b074f223170e93f46a608fcda0d768dd87b93ad064 SHA512: f684bd2cc46bb53e285a41bd631edbacdedfba2c73c520381710e0118cc18fbaa640afd97c2de9d388e7819e51e7f039708d6332b42350e6d3dd4d2f5fda3d5d Homepage: https://cran.r-project.org/package=ktweedie Description: CRAN Package 'ktweedie' ('Tweedie' Compound Poisson Model in the Reproducing KernelHilbert Space) Kernel-based 'Tweedie' compound Poisson gamma model using high-dimensional predictors for the analyses of zero-inflated response variables. The package features built-in estimation, prediction and cross-validation tools and supports choice of different kernel functions. For more details, please see Yi Lian, Archer Yi Yang, Boxiang Wang, Peng Shi & Robert William Platt (2023) . Package: r-cran-kvh Architecture: arm64 Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 205 Depends: libc6 (>= 2.17), 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-kvh_1.4.2-1.ca2604.1_arm64.deb Size: 79376 MD5sum: 4707cdc0f8e64b1be9762ecea4932711 SHA1: 90e2cfcb7da7c769818fbe668f2ab6daab213cfe SHA256: 06b3b9272b25c6a23ffb53b8372a65bb52393e4cb3e25cc59992dd6761f38360 SHA512: c06af44aee13284a982ec43cea8d79d0463a7b54c76f66d1f1ee3a421c4cd7678e8ef404fe0210b5de9eb344c6f41b71ebc18423e1c06ff8fbd6c2090b30787b Homepage: https://cran.r-project.org/package=kvh Description: CRAN Package 'kvh' (Read/Write Files in Key-Value-Hierarchy Format) The format KVH is a lightweight format that can be read/written both by humans and machines. It can be useful in situations where XML or alike formats seem to be an overkill. 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Method for detecting non-stationarity in resting state functional Magnetic Resonance Imaging (fMRI) scans as seen in Ramsay, K., & Chenouri, S. (2025) is implemented in fmri_changepoints(). Also includes depth- and rank-based implementation of the wild binary segmentation algorithm for detecting multiple changepoints in multivariate data. 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Package: r-cran-l0ara Architecture: arm64 Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-l0ara_0.1.7-1.ca2604.1_arm64.deb Size: 110428 MD5sum: 8133896173cd8fcb1478a83fe201d4e8 SHA1: 11fa0075104521e359a160bd8aca96ca7d79f570 SHA256: 3a8b1a71aaa3eb724e4b945eb8fa85937dedd35d86619ef1feb29c63b69fe293 SHA512: 5f3420a3b9c8a6f9cd066b383401e63fc51d5489b864559423c4274b3d0042cd79a25abb1051fafed039d2a97a02baf9a16496d6f166cbe48133e777841abb15 Homepage: https://cran.r-project.org/package=l0ara Description: CRAN Package 'l0ara' (Sparse Generalized Linear Model with L0 Approximation forFeature Selection) Fits sparse generalized linear models using an adaptive ridge approximation to an L0 penalty. 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Network estimation is performed using the Local Linear Approximation (LLA) framework (Fan & Li, 2001 ; Zou & Li, 2008 ) with five penalty functions: arctangent (Wang & Zhu, 2016 ), EXP (Wang, Fan, & Zhu, 2018 ), Gumbel, Log (Candes, Wakin, & Boyd, 2008 ), and Weibull. Adaptive penalty parameters for EXP, Gumbel, and Weibull are estimated via maximum likelihood, and model selection uses information criteria including AIC, BIC, and EBIC (Extended BIC). Simulation functions generate multivariate normal data from GGMs with stochastic block model or small-world (Watts-Strogatz) network structures. Package: r-cran-l0learn Architecture: arm64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3345 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-matrix, r-cran-ggplot2, r-cran-reshape2, r-cran-mass, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-pracma, r-cran-raster, r-cran-covr Filename: pool/dists/resolute/main/r-cran-l0learn_2.1.0-1.ca2604.1_arm64.deb Size: 1231306 MD5sum: a216c18b6379e5512cf5f57cc90d77df SHA1: ee21a9d9a9f21eb89642d453dda9ca9cf971f141 SHA256: 557ebfc9575696600ca1d75501aa692a09e08fe81ed7300b91b130cd6fdce048 SHA512: cf7a0a0fd239af8ba0a6cc9070af9bf936cfba7319c35242e280a0421e28fde777971eabdb388356606c00970ade6d8374d4445de876866cf0128b73b1fd0ccb Homepage: https://cran.r-project.org/package=L0Learn Description: CRAN Package 'L0Learn' (Fast Algorithms for Best Subset Selection) Highly optimized toolkit for approximately solving L0-regularized learning problems (a.k.a. best subset selection). The algorithms are based on coordinate descent and local combinatorial search. For more details, check the paper by Hazimeh and Mazumder (2020) . Package: r-cran-l1centrality Architecture: arm64 Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 450 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-igraph, r-cran-rcpp, r-cran-withr Filename: pool/dists/resolute/main/r-cran-l1centrality_0.5.1-1.ca2604.1_arm64.deb Size: 353260 MD5sum: df778f4f96a240372bc8ad9c92b6bd48 SHA1: d680721e76c59366ab852f4037a07b96087eec1b SHA256: 3c1a017e04692c49951cb3cffa9b1f04577753ed6fb0c40d3ab91c1673b90dc1 SHA512: ce2b97ba33c5495f3d8ec9b0c82561cbc71cd29950899fb8917dbcc72b3f380d2265c35d91c0c01ed28712bbce51ea676a6f718f5d11560213f643f5827c6cc6 Homepage: https://cran.r-project.org/package=L1centrality Description: CRAN Package 'L1centrality' (Graph/Network Analysis Based on L1 Centrality) Analyze graph/network data using L1 centrality and prestige. 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) . Package: r-cran-l1pack Architecture: arm64 Version: 0.62-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastmatrix Filename: pool/dists/resolute/main/r-cran-l1pack_0.62-4-1.ca2604.1_arm64.deb Size: 164170 MD5sum: 40ed9546e76cb1d38adde50cf171abe6 SHA1: f7fea925d5c5d526c3c623504aef3e7582794c1b SHA256: a7503b158f89670521ddc74f4b35e38600f75d436ce29485903e49996136d1ec SHA512: c87ba120083b7efccd004b785f779af35c2708efd318a963ef0be3e769cd1cdf62838f5f751ac83867350b06c2253a40296a3eaa02a365bb56ce0f77676331ee Homepage: https://cran.r-project.org/package=L1pack Description: CRAN Package 'L1pack' (Routines for L1 Estimation) L1 estimation for linear regression using Barrodale and Roberts' method and the EM algorithm . Estimation of mean and covariance matrix using the multivariate Laplace distribution, density, distribution function, quantile function and random number generation for univariate and multivariate Laplace distribution . Implementation of Naik and Plungpongpun for the Generalized spatial median estimator is included. Package: r-cran-l1spectral Architecture: arm64 Version: 0.99.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 240 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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-igraph, r-cran-matrix, r-cran-aricode, r-cran-caret, r-cran-glmnet, r-cran-ggplot2, r-cran-cvtools, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-l1spectral_0.99.6-1.ca2604.1_arm64.deb Size: 100338 MD5sum: ab04d8e4d0def4f959475b1894f9deb6 SHA1: 295be196d2395a0ed251e22db20636b521c0f503 SHA256: bac662208b791cd07a943984f7b4abd28e8b19678c683e2c5575f1c5175f03fd SHA512: 5e81bbc5436af66f41220c26893dd3dd1814b4e4b77e40cbe207566a5a2e6452b6aeecf5cbbb94619d7fcec5cd9286f8435ff44ad24f3fd22964030795aad7c8 Homepage: https://cran.r-project.org/package=l1spectral Description: CRAN Package 'l1spectral' (An L1-Version of the Spectral Clustering) Provides an l1-version of the spectral clustering algorithm devoted to robustly clustering highly perturbed graphs using l1-penalty. This algorithm is described with more details in the preprint C. Champion, M. Champion, M. Blazère, R. Burcelin and J.M. Loubes, "l1-spectral clustering algorithm: a spectral clustering method using l1-regularization" (2022). Package: r-cran-la Architecture: arm64 Version: 2.3-1.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 (>= 4.9), 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_arm64.deb Size: 111172 MD5sum: 7fd3ce06897924f321bfb52fc252a5ad SHA1: 3c30172b2aea2bfa6c2b086044d79e9855777c70 SHA256: d9cdf3f475943d5b731f6ddeb4e701f77573cd278257bcfe0400937cd5c7c511 SHA512: 369056402d52c1644f069cbc67ecf6419c4de8c4c69db90dba583831628302e8cf4e545b5f55a253e14f890bb830b91d2ed61d83a1b99056df4631bf385a4a95 Homepage: https://cran.r-project.org/package=LA Description: CRAN Package 'LA' (Lioness Algorithm (LA)) Contains Lioness Algorithm (LA) for finding optimal designs over continuous design space, optimal Latin hypercube designs, and optimal order-of-addition designs. LA is a brand new nature-inspired meta-heuristic optimization algorithm. Detailed methodologies of LA and its implementation on numerical simulations can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) . Package: r-cran-labdsv Architecture: arm64 Version: 2.3-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-cluster, r-cran-rtsne, r-cran-mass Suggests: r-cran-fso Filename: pool/dists/resolute/main/r-cran-labdsv_2.3-1-1.ca2604.1_arm64.deb Size: 341548 MD5sum: 60e535ffa420c11314bcaf1c681ba612 SHA1: 6b6bce41a525ba36d59b7a4b438140a8ee3d5bf3 SHA256: 76a09c496d302293771f9a510288aecdfcf6fe97a32e396d96af1043163a4d8d SHA512: 05ce23d2f361da7da2c8ac903e5ae7f1a17b85d78d09f03a378cd8cc6bb11056e47a64af13d456f5be3f521b636c6dc1264bc525014676317cdac708972e49b7 Homepage: https://cran.r-project.org/package=labdsv Description: CRAN Package 'labdsv' (Ordination and Multivariate Analysis for Ecology) A variety of ordination and community analyses useful in analysis of data sets in community ecology. Includes many of the common ordination methods, with graphical routines to facilitate their interpretation, as well as several novel analyses. Package: r-cran-lacm Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-numderiv, r-cran-statmod Filename: pool/dists/resolute/main/r-cran-lacm_0.1.2-1.ca2604.1_arm64.deb Size: 57846 MD5sum: 542339990744e0e7d8c2f35ee64cb009 SHA1: 6ad1dd7497eee8f500acf032f9844f9e0a3806d9 SHA256: 2740f0c0872e5ae87234306965d9cf764aee995f82c6e4b0e3e158676c2fdd3d SHA512: c6985ffb1bf6b1a4ccd116d684415a3774d3817e4a78e78ee522ef473528fcfedf80bff7f6a3ff2a2e4618ed24cd14e97ae0664ad17906288efeb70e65ca2f44 Homepage: https://cran.r-project.org/package=lacm Description: CRAN Package 'lacm' (Latent Autoregressive Count Models) Perform pairwise likelihood inference in latent autoregressive count models. See Pedeli and Varin (2020) for details. Package: r-cran-lacunr Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1439 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_arm64.deb Size: 1058326 MD5sum: b65ac283a645832864172362f7929cbe SHA1: d588d1585f4014385a41a8280208e95286449033 SHA256: bb86eda39f794788e06e6588c2c8fb0dbcd6cd4ea45ef0044cd2f6e672234884 SHA512: 2612511e8f59dbce7ab1286553616601257cdc5f7259c1ec0d26733807fb40c9fade78782ddf2f0e50f476c873aa3b063b2f2ed74961e4fcc28c42f89410756d Homepage: https://cran.r-project.org/package=lacunr Description: CRAN Package 'lacunr' (Fast 3D Lacunarity for Voxel Data) Calculates 3D lacunarity from voxel data. It is designed for use with point clouds generated from Light Detection And Ranging (LiDAR) scans in order to measure the spatial heterogeneity of 3-dimensional structures such as forest stands. It provides fast 'C++' functions to efficiently bin point cloud data into voxels and calculate lacunarity using different variants of the gliding-box algorithm originated by Allain & Cloitre (1991) . Package: r-cran-laf Architecture: arm64 Version: 0.8.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1029 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_arm64.deb Size: 689300 MD5sum: 7bfb4702261126187c7e9821b4731245 SHA1: 2b42b257419cbc1de60e9896d09624908d7d14ef SHA256: 542f34221ea2018a9ec93fce43425899b1d49138ff875359280c15fead2fa284 SHA512: dcd500654e26a91ef28983328fbab7630ca51e24dac3101b964a976cbdd1ced3b8c3dc026c2697b8744c720fa60b0432ca865156eb946eac848cd981883ed986 Homepage: https://cran.r-project.org/package=LaF Description: CRAN Package 'LaF' (Fast Access to Large ASCII Files) Methods for fast access to large ASCII files. Currently the following file formats are supported: comma separated format (CSV) and fixed width format. 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Package: r-cran-lagp Architecture: arm64 Version: 1.5-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1606 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), 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_arm64.deb Size: 1342796 MD5sum: fa203344acc90e52b9f2167377b486cb SHA1: 6c73b758c3b36e97c96556e229d6364da89e6a20 SHA256: a4ef84aec65d0337a2f51ce99be828dd573551e53bccf448d8dfd4634c46e63b SHA512: 3db8ba149233b0fa5d5ffed602d9ab20bedb7b328446e5d256c8cbd0aa0a5383b280c06efe8c41316fd3b39246afc78f9960330e8e4284f860dddfccc7aec8ce Homepage: https://cran.r-project.org/package=laGP Description: CRAN Package 'laGP' (Local Approximate Gaussian Process Regression) Performs approximate GP regression for large computer experiments and spatial datasets. The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine. OpenMP and GPU features may require special compilation. An interface to lower-level (full) GP inference and prediction is provided. Wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration, are also provided. For details and tutorial, see Gramacy (2016 . Package: r-cran-lakemetabolizer Architecture: arm64 Version: 1.5.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3340 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_arm64.deb Size: 594422 MD5sum: c6c984d3646339bc0458e5c6fc93ce8e SHA1: bce92fc41bdce89659ad2fffef25d448b215e9fa SHA256: 6ff5cb596d56030eb94bad329619ff3b151ca955e13474653a2f56aa65fd2993 SHA512: e7beb179c79db310d1f87fad20be77a406434a808bb4c3e95072d1fd031c6c33e820a1fa6ba2f6f822463d9bedbd5e1f9e03f5e86686241e179c0854edc9025c Homepage: https://cran.r-project.org/package=LakeMetabolizer Description: CRAN Package 'LakeMetabolizer' (Tools for the Analysis of Ecosystem Metabolism) A collection of tools for the calculation of freewater metabolism from in situ time series of dissolved oxygen, water temperature, and, optionally, additional environmental variables. LakeMetabolizer implements 5 different metabolism models with diverse statistical underpinnings: bookkeeping, ordinary least squares, maximum likelihood, Kalman filter, and Bayesian. Each of these 5 metabolism models can be combined with 1 of 7 models for computing the coefficient of gas exchange across the air–water interface (k). LakeMetabolizer also features a variety of supporting functions that compute conversions and implement calculations commonly applied to raw data prior to estimating metabolism (e.g., oxygen saturation and optical conversion models). Package: r-cran-lakhesis Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 279774 MD5sum: 25c17ac118cb02ac7bbbabf2764a62c3 SHA1: b856787231acc50d47bc828efdcede7776db1e98 SHA256: 4621056ee1ace8f7afff7ba6f643e1252daef7613bc2282fab23f636fee5c3af SHA512: 30d8397309b000157aac15c8b349eaea19f433b0deb03b64bc4c605531aec36cde75f7233d702a8f42495c7b4dcdba1b9f6c5bbf9b24e870497f5274d08ace6b Homepage: https://cran.r-project.org/package=lakhesis Description: CRAN Package 'lakhesis' (Consensus Seriation for Binary Data) Determining consensus seriations for binary incidence matrices, using a two-step process of Procrustes-fit correspondence analysis for heuristic selection of partial seriations and iterative regression to establish a single consensus. Contains the Lakhesis Calculator, a graphical platform for identifying seriated sequences. Collins-Elliott (2024) . Package: r-cran-lam Architecture: arm64 Version: 0.7-22-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 521 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_arm64.deb Size: 292748 MD5sum: be0f6f717ebc8b23e5d5b01f0b863233 SHA1: 4ddaa7853a050251016db6c9df631ca2e8e4e0c7 SHA256: fccde5153fe4b6826a743170f618c4ea756a7be0ad872ba6e994909bae221599 SHA512: bfcd4c097a08e4e380fd5bc4b164cb4d537dcf582505b60f317a69241fb9761e6ea70878fbfee600e6283a499f8a4f8679976489837614b70abaf35e3c721f8a Homepage: https://cran.r-project.org/package=LAM Description: CRAN Package 'LAM' (Some Latent Variable Models) Includes some procedures for latent variable modeling with a particular focus on multilevel data. The 'LAM' package contains mean and covariance structure modelling for multivariate normally distributed data (mlnormal(); Longford, 1987; ), a general Metropolis-Hastings algorithm (amh(); Roberts & Rosenthal, 2001, ) and penalized maximum likelihood estimation (pmle(); Cole, Chu & Greenland, 2014; ). Package: r-cran-lama Architecture: arm64 Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4460 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_arm64.deb Size: 2928874 MD5sum: 00283b99409eb561d76239513d73a3b5 SHA1: 2574c24b7564234bf106a147c41d7b96e48a5af7 SHA256: 497a70d73d037c9e4474ec31336ad94078e77839f230af12d46e19998b338be3 SHA512: 8b562a29e6331edf4a4b9a49a4c335292785680296a9ced68f4b0263d644161cc47e3bcbaed7dc499f762b29c4d14c487aaca4114f4a8cbac895915f5a189985 Homepage: https://cran.r-project.org/package=LaMa Description: CRAN Package 'LaMa' (Fast Numerical Maximum Likelihood Estimation for Latent MarkovModels) A variety of latent Markov models, including hidden Markov models, hidden semi-Markov models, state-space models and continuous-time variants can be formulated and estimated within the same framework via directly maximising the likelihood function using the so-called forward algorithm. Applied researchers often need custom models that standard software does not easily support. Writing tailored 'R' code offers flexibility but suffers from slow estimation speeds. We address these issues by providing easy-to-use functions (written in 'C++' for speed) for common tasks like the forward algorithm. These functions can be combined into custom models in a Lego-type approach, offering up to 10-20 times faster estimation via standard numerical optimisers. To aid in building fully custom likelihood functions, several vignettes are included that show how to simulate data from and estimate all the above model classes. Package: r-cran-lambertw Architecture: arm64 Version: 0.6.9-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1187 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_arm64.deb Size: 848822 MD5sum: a8006b46aac05084d9186bf14bbd2c18 SHA1: 9035c66f65dbdf06e3417a61eca502c03a2cf7ef SHA256: 218cd0d1bdbc4a3f5338d2a5117b7eec4992cd162c41cd30ca725faae2a4a80b SHA512: 016820ea3de2477488a9d25cddc00cdd5adda26d48216674d8a6656edc919c9ede972ea1a3563ab6e145115d53b8771e3aec2c45553d8afdc66a2f7ae9dbd2c0 Homepage: https://cran.r-project.org/package=LambertW Description: CRAN Package 'LambertW' (Probabilistic Models to Analyze and Gaussianize Heavy-Tailed,Skewed Data) Lambert W x F distributions are a generalized framework to analyze skewed, heavy-tailed data. It is based on an input/output system, where the output random variable (RV) Y is a non-linearly transformed version of an input RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed). The transformed RV Y has a Lambert W x F distribution. This package contains functions to model and analyze skewed, heavy-tailed data the Lambert Way: simulate random samples, estimate parameters, compute quantiles, and plot/ print results nicely. The most useful function is 'Gaussianize', which works similarly to 'scale', but actually makes the data Gaussian. A do-it-yourself toolkit allows users to define their own Lambert W x 'MyFavoriteDistribution' and use it in their analysis right away. Package: r-cran-lamle Architecture: arm64 Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 893 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 427234 MD5sum: c69e1f22de6623fdc135441d2185242f SHA1: f69081424d823795bd98f54b4c8c2d32de02001c SHA256: 33b38f0995f699d68a25721570ef4f20aa7e8906d2e2d72bef435edfe2480a0c SHA512: 39e56148d238ee6c4fde505e26167df481d78e37210825d3886faab5ddeb3488bde9bbb2e738cd7edf5329d8ffcb38aa465c9223d5ff4991907079e5bc2d3e20 Homepage: https://cran.r-project.org/package=lamle Description: CRAN Package 'lamle' (Maximum Likelihood Estimation of Latent Variable Models) Approximate marginal maximum likelihood estimation of multidimensional latent variable models via adaptive quadrature or Laplace approximations to the integrals in the likelihood function, as presented for confirmatory factor analysis models in Jin, S., Noh, M., and Lee, Y. (2018) , for item response theory models in Andersson, B., and Xin, T. (2021) , and for generalized linear latent variable models in Andersson, B., Jin, S., and Zhang, M. (2023) . Models implemented include the generalized partial credit model, the graded response model, and generalized linear latent variable models for Poisson, negative-binomial and normal distributions. Supports a combination of binary, ordinal, count and continuous observed variables and multiple group models. Package: r-cran-lamw Architecture: arm64 Version: 2.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 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_arm64.deb Size: 45334 MD5sum: 67ac6e932b3f0e918538e7a240186d33 SHA1: 6f3753d991c657d294230190f48ebac8c8d5937e SHA256: 135c9f63f6a4ac46e5b1cbf8e331178dcf0af1d02955319382aacab9abd38954 SHA512: fcef24f9058374b57924344370d78951a865b1b01c30f80ecb2b10263f01d878f731705f14f66d752712ebc4abd8e8b56f26937ba91c9dbdbe2a077468149bf5 Homepage: https://cran.r-project.org/package=lamW Description: CRAN Package 'lamW' (Lambert-W Function) Implements both real-valued branches of the Lambert-W function (Corless et al, 1996) without the need for installing the entire GSL. Package: r-cran-landscapemetrics Architecture: arm64 Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2002 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_arm64.deb Size: 1588990 MD5sum: 0f0b8963888c6bf31320c614278604b7 SHA1: 3b83750e42a629efca69047f25de6603e1781b04 SHA256: b050c256082833ff4d779857dd204ffc5163228d9d34bb27c73cbd632dcd72a0 SHA512: 0f265f00fad73761492c4424ac21b9a165fbaadd8f0135ad25a6bbbf5a0015aae84238c2a484d844a9e7912591a5eb8e3c6a463e35b536c838e3c974466b6e80 Homepage: https://cran.r-project.org/package=landscapemetrics Description: CRAN Package 'landscapemetrics' (Landscape Metrics for Categorical Map Patterns) Calculates landscape metrics for categorical landscape patterns in a tidy workflow. 'landscapemetrics' reimplements the most common metrics from 'FRAGSTATS' () and new ones from the current literature on landscape metrics. This package supports 'terra' SpatRaster objects as input arguments. It further provides utility functions to visualize patches, select metrics and building blocks to develop new metrics. Package: r-cran-landscaper Architecture: arm64 Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 291 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 110758 MD5sum: 4ae8b881349a19ef9289ea76aeb0868b SHA1: 162c7c716aeb534d58f4a4b656f26971718fa73e SHA256: bfe084770c5bb31b8ea6d80d4414694cb201b7f221f5fb87acdac08b9fcbcc6e SHA512: 53be914c883382d867c047fc61f6ce57eadd6830554d016a45a5c722763148a7344ad718bb9a0d9a7e190a484ddb71848ec49d38b2bdd5acbb7daa2e279e94d0 Homepage: https://cran.r-project.org/package=landscapeR Description: CRAN Package 'landscapeR' (Categorical Landscape Simulation Facility) Simulates categorical maps on actual geographical realms, starting from either empty landscapes or landscapes provided by the user (e.g. land use maps). Allows to tweak or create landscapes while retaining a high degree of control on its features, without the hassle of specifying each location attribute. In this it differs from other tools which generate null or neutral landscapes in a theoretical space. The basic algorithm currently implemented uses a simple agent style/cellular automata growth model, with no rules (apart from areas of exclusion) and von Neumann neighbourhood (four cells, aka Rook case). Outputs are raster dataset exportable to any common GIS format. Package: r-cran-landsepi Architecture: arm64 Version: 1.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4538 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-sp, r-cran-rcpp, r-cran-matrix, r-cran-mvtnorm, r-cran-fields, r-cran-splancs, r-cran-sf, r-cran-dbi, r-cran-rsqlite, r-cran-foreach, r-cran-doparallel, r-cran-desolve, r-cran-testthat Suggests: r-cran-shiny, r-cran-shinyjs, r-cran-dt, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-landsepi_1.5.3-1.ca2604.1_arm64.deb Size: 3595462 MD5sum: b996cbc2d375a68a598b0120b7c830e7 SHA1: cfb284862f2d160a818abf0b939418cb8236de1e SHA256: b0ce67998f1fae789627e7b0f386f21e7e8e1eb44868957ddb80afc9328600ed SHA512: 3bb104a104029205c1da245374853ec456f6f563f41feb8faa070e694ade76133c5ccc09eeeca6a9302420d09090addeba27908aa8d057c118f714dc3a8c18de Homepage: https://cran.r-project.org/package=landsepi Description: CRAN Package 'landsepi' (Landscape Epidemiology and Evolution) A stochastic, spatially-explicit, demo-genetic model simulating the spread and evolution of a plant pathogen in a heterogeneous landscape to assess resistance deployment strategies. It is based on a spatial geometry for describing the landscape and allocation of different cultivars, a dispersal kernel for the dissemination of the pathogen, and a SEIR ('Susceptible-Exposed-Infectious-Removed’) structure with a discrete time step. It provides a useful tool to assess the performance of a wide range of deployment options with respect to their epidemiological, evolutionary and economic outcomes. Loup Rimbaud, Julien Papaïx, Jean-François Rey, Luke G Barrett, Peter H Thrall (2018) . Package: r-cran-langevin Architecture: arm64 Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 822 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-langevin_1.3.3-1.ca2604.1_arm64.deb Size: 591718 MD5sum: 7cc680010bbf28e79296ddd4865286d0 SHA1: a6502eeb9cfca11cc3acbfdaa31d8bc05273b891 SHA256: 82e534ddf90c3c9a4b15d397e9d278bb679dfc3b62ffe1920242752266bccb29 SHA512: cbc16111cd359d1a881d6a7f2bd37b72ebb2f2fb945da6b368c83cb9577eb03f98457945d975c444a8741dd898b773b5f9b8229c10c493453163d885dde02fd5 Homepage: https://cran.r-project.org/package=Langevin Description: CRAN Package 'Langevin' (Langevin Analysis in One and Two Dimensions) Estimate drift and diffusion functions from time series and generate synthetic time series from given drift and diffusion coefficients. 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The Language Server protocol is used by an editor client to integrate features like auto completion. See for details. Package: r-cran-larisk Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 392 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-dplyr Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-larisk_1.0.0-1.ca2604.1_arm64.deb Size: 145878 MD5sum: e35fa05fc49e5b26d04487f944e4c494 SHA1: 4d7ea17174a09a65c830be7a7cab577ecd3405d2 SHA256: 84cbe0525c7abc628fe0f895e49ddeac54771dcb0dbf99979e094bab8ae426ba SHA512: e107ec4c900cd7954d8fe73aca9e1d86b0feb8c704c58cedc80b6822cae3c410304ce234e630b12783dc886cbb13e6d1efe467f11fe40789b973acafcd510145 Homepage: https://cran.r-project.org/package=LARisk Description: CRAN Package 'LARisk' (Estimation of Lifetime Attributable Risk of Cancer fromRadiation Exposure) Compute lifetime attributable risk of radiation-induced cancer reveals that it can be helpful with enhancement of the flexibility in research with fast calculation and various options. Important reference papers include Berrington de Gonzalez et al. (2012) , National Research Council (2006, ISBN:978-0-309-09156-5). Package: r-cran-lars Architecture: arm64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-lars_1.3-1.ca2604.1_arm64.deb Size: 227666 MD5sum: 32458ccb870c9684b1129c4cba7cbbbd SHA1: 6e9bc3d5d8ed721a275c2f02ad2e433ab324c61e SHA256: 24fa75dbec139e4afdc661088358b92210eba32700b6fbf59940289e4fe00866 SHA512: e0faa9592a2da4bd4d60c43cf21004f682b485f0b971486f6f1b8a4a77f534ee3591ea330a20189fe81ceb456b830673b6fd8e3be6bae4b7c67ea0e88aed9c0b Homepage: https://cran.r-project.org/package=lars Description: CRAN Package 'lars' (Least Angle Regression, Lasso and Forward Stagewise) Efficient procedures for fitting an entire lasso sequence with the cost of a single least squares fit. Least angle regression and infinitesimal forward stagewise regression are related to the lasso, as described in the paper below. Package: r-cran-lassobacktracking Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 108382 MD5sum: 895c16343e64ff5efc51241e2e18b288 SHA1: 406f5126c3654728ea8b076da72030db5b2e4655 SHA256: f5f55dfc06664336bc07c42054748ec34aadf04a55f4f58dc98bc89f07405913 SHA512: 114907b9b558c266202490f0fb906114965ca0cb4d506bb19f29b1fb5f1d1fd0bf5a95065ebaa87cfc71df4f2880cf5c0a384c8b133384721657cfa2248b6d8a 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: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1289 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_arm64.deb Size: 595650 MD5sum: 76f9e74795b46c20844959bd7867bfd4 SHA1: 824d1f82ac91b595a713c5cd1f94445b0cab0df0 SHA256: 562d8377f50f811f0a97a83c3a5cbcd0ac22242d058cca9281c4797562a8c43f SHA512: eeb279ca74dc029f3573c877d9283da7e5c1c73ed40e80c0563fe93d33773015eda8508cb338673332ea3b7c04850e9ec31f83f9b61359abdb9ce3f6291c99bd 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: arm64 Version: 0.8.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 77770 MD5sum: c4d071da9f539d894ed95fd0fc5441ac SHA1: 4c348dd8271aa8ca346fb2d56323d5226b9b25bb SHA256: 61291e5d3da3513d1219104e9f0e094ef031da81d0f2a9a611e6f7635390dabc SHA512: 407e188ae59aef6d6de0d0e9fcb714fb87fe4135fb739e02bd3b3bb2857cc34f880da2137778a7c408a6becd2f6d9375ea9bd42efd47ae53ff6dcffe3dfdfdd1 Homepage: https://cran.r-project.org/package=LassoNet Description: CRAN Package 'LassoNet' (3CoSE Algorithm) Contains functions to estimate a penalized regression model using 3CoSE algorithm, see Weber, Striaukas, Schumacher Binder (2018) . Package: r-cran-latentcor Architecture: arm64 Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3316 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pcapp, r-cran-fmultivar, r-cran-mnormt, r-cran-matrix, r-cran-mass, r-cran-heatmaply, r-cran-ggplot2, r-cran-plotly, r-cran-geometry, r-cran-dofuture, r-cran-foreach, r-cran-future, r-cran-dorng, r-cran-microbenchmark Suggests: r-cran-rmarkdown, r-cran-markdown, r-cran-knitr, r-cran-testthat, r-cran-lattice, r-cran-cubature, r-cran-plot3d, r-cran-covr Filename: pool/dists/resolute/main/r-cran-latentcor_2.0.2-1.ca2604.1_arm64.deb Size: 3134042 MD5sum: f15e1acb08d5c2778fee92f1e2e5f02d SHA1: 86a281f2982a404b0afe34a632cc5074120dd350 SHA256: dab00cb501ed2bbc705930f7f70d4d23686da77840aa0475fdca306735bcc025 SHA512: 2f7a733f64ca6c9be6b38b5934fd1b5e2cd257c5536ba34e6adeec8d7f496e081f2aa142c3446b404166eb7f2a2de205218651de8646101367fd107cd6046468 Homepage: https://cran.r-project.org/package=latentcor Description: CRAN Package 'latentcor' (Fast Computation of Latent Correlations for Mixed Data) The first stand-alone R package for computation of latent correlation that takes into account all variable types (continuous/binary/ordinal/zero-inflated), comes with an optimized memory footprint, and is computationally efficient, essentially making latent correlation estimation almost as fast as rank-based correlation estimation. The estimation is based on latent copula Gaussian models. For continuous/binary types, see Fan, J., Liu, H., Ning, Y., and Zou, H. (2017). For ternary type, see Quan X., Booth J.G. and Wells M.T. (2018) . For truncated type or zero-inflated type, see Yoon G., Carroll R.J. and Gaynanova I. (2020) . For approximation method of computation, see Yoon G., Müller C.L. and Gaynanova I. (2021) . The latter method uses multi-linear interpolation originally implemented in the R package . Package: r-cran-latentnet Architecture: arm64 Version: 2.12.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 621 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-network, r-cran-ergm, r-cran-sna, r-cran-mvtnorm, r-cran-abind, r-cran-coda, r-cran-statnet.common Suggests: r-cran-snowft, r-cran-rgl, r-cran-heplots, r-cran-rlecuyer, r-cran-covr, r-cran-kernsmooth Filename: pool/dists/resolute/main/r-cran-latentnet_2.12.0-1.ca2604.1_arm64.deb Size: 507942 MD5sum: 255be8c84877c3862ac3a23685262bfb SHA1: cbc9145198ee8a7a1370f669452aee29719435d9 SHA256: 5110065be0182a2013574c126774cbc5d59e0a432d4c06be6b3e9d2734041ce1 SHA512: e0be5cdd23528aab3f6ae70c8daaec635210211346fe19f9d916eafc607bc9d586ced1b75d7c4a002c684d66e769e924e23a2e8ac40e44dd30ca8653b6929f38 Homepage: https://cran.r-project.org/package=latentnet Description: CRAN Package 'latentnet' (Latent Position and Cluster Models for Statistical Networks) Fit and simulate latent position and cluster models for statistical networks. See Krivitsky and Handcock (2008) and Krivitsky, Handcock, Raftery, and Hoff (2009) . Package: r-cran-later Architecture: arm64 Version: 1.4.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 386 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_arm64.deb Size: 133214 MD5sum: 43956a823063c85feb92d226911191a0 SHA1: c9bd19df08e3718c621f29b6adfabfa74d845e52 SHA256: 9660bf338f368844aeaa68b7145167939aeb74607aac5726f6d6ac357ab34968 SHA512: e012e6a658fd574a779ce4eeb7e7d5c35f2104f08f6f362a0b863d31f2423ccf92df5a8b021b275f4ade4dc9aa58349af28ec581375b65575caf2d66c8afc385 Homepage: https://cran.r-project.org/package=later Description: CRAN Package 'later' (Utilities for Scheduling Functions to Execute Later with EventLoops) Executes arbitrary R or C functions some time after the current time, after the R execution stack has emptied. The functions are scheduled in an event loop. Package: r-cran-lattice Architecture: arm64 Version: 0.22-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1736 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-kernsmooth, r-cran-mass, r-cran-latticeextra, r-cran-colorspace Filename: pool/dists/resolute/main/r-cran-lattice_0.22-9-1.ca2604.1_arm64.deb Size: 1397752 MD5sum: 282b17a87fd460dd18a3886c5c335495 SHA1: cf2056f840331a8945dd8a4157f3855e70e99176 SHA256: 929e6f278aa4b197fb8078b299b9de831b595e6cd77f778a4e4fd7287325f1eb SHA512: f8870eef6b4f4c9ed1ba8449fdf584b74e0e74d29a0815c5fe48090aa596b7c3656e1f4879e362f6fc799038a643763b6de96cc03358153d1d0c40d8aa931ef5 Homepage: https://cran.r-project.org/package=lattice Description: CRAN Package 'lattice' (Trellis Graphics for R) A powerful and elegant high-level data visualization system inspired by Trellis graphics, with an emphasis on multivariate data. Lattice is sufficient for typical graphics needs, and is also flexible enough to handle most nonstandard requirements. See ?Lattice for an introduction. Package: r-cran-latticedesign Architecture: arm64 Version: 4.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 459 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nloptr Filename: pool/dists/resolute/main/r-cran-latticedesign_4.0-1-1.ca2604.1_arm64.deb Size: 376770 MD5sum: ff1dc8a993582f67cffaf95537c86578 SHA1: 3cc21c529488e1b778fd6f5c9d4ba270c90b9ffa SHA256: cdd6e7c0c1c8665e9e7dcbcc00690537464a0f672ffeb6edad877acb4477449d SHA512: 9c63a62363ed01d0b4760331d4beacf785272be63cbcd59c6af8a3faba190dea3c1ca11c0a390c33237ec974f744ec337b0d495db9825f00883dd0859bb28fe3 Homepage: https://cran.r-project.org/package=LatticeDesign Description: CRAN Package 'LatticeDesign' (Lattice-Based Space-Filling Designs) Lattice-based space-filling designs with fill or separation distance properties including interleaved lattice-based minimax distance designs proposed in Xu He (2017) , interleaved lattice-based maximin distance designs proposed in Xu He (2018) , interleaved lattice-based designs with low fill and high separation distance properties proposed in Xu He (2024) , (sliced) rotated sphere packing designs proposed in Xu He (2017) and Xu He (2019) , densest packing-based maximum projections designs proposed in Xu He (2020) and Xu He (2018) , maximin distance designs for mixed continuous, ordinal, and binary variables proposed in Hui Lan and Xu He (2025) , and optimized and regularly repeated lattice-based Latin hypercube designs for large-scale computer experiments proposed in Xu He, Junpeng Gong, and Zhaohui Li (2025) . Package: r-cran-latticekrig Architecture: arm64 Version: 9.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 726 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 607640 MD5sum: b9414ca7c06fd5d7940b382ed6788483 SHA1: c51ba9676d999f002a6a2e3940d93d0bbfc55bc0 SHA256: deaf3e809ff014ff5774f1717d2386b4240e734dbbd5dd25e38dabe9e2fdc079 SHA512: b0f9afdf0b2988bacba9e54a8e91605917b65443325ba1a26b22bb002d874feabd7f7a258d16ebf4cc0827ff428bd8aa5b68c071bbba7a9c09a6d113e4b89720 Homepage: https://cran.r-project.org/package=LatticeKrig Description: CRAN Package 'LatticeKrig' (Multi-Resolution Kriging Based on Markov Random Fields) Methods for the interpolation of large spatial datasets. This package uses a basis function approach that provides a surface fitting method that can approximate standard spatial data models. Using a large number of basis functions allows for estimates that can come close to interpolating the observations (a spatial model with a small nugget variance.) Moreover, the covariance model for this method can approximate the Matern covariance family but also allows for a multi-resolution model and supports efficient computation of the profile likelihood for estimating covariance parameters. This is accomplished through compactly supported basis functions and a Markov random field model for the basis coefficients. These features lead to sparse matrices for the computations and this package makes of the R spam package for sparse linear algebra. An extension of this version over previous ones ( < 5.4 ) is the support for different geometries besides a rectangular domain. The Markov random field approach combined with a basis function representation makes the implementation of different geometries simple where only a few specific R functions need to be added with most of the computation and evaluation done by generic routines that have been tuned to be efficient. One benefit of this package's model/approach is the facility to do unconditional and conditional simulation of the field for large numbers of arbitrary points. There is also the flexibility for estimating non-stationary covariances and also the case when the observations are a linear combination (e.g. an integral) of the spatial process. Included are generic methods for prediction, standard errors for prediction, plotting of the estimated surface and conditional and unconditional simulation. See the 'LatticeKrigRPackage' GitHub repository for a vignette of this package. Development of this package was supported in part by the National Science Foundation Grant 1417857 and the National Center for Atmospheric Research. Package: r-cran-lavacreg Architecture: arm64 Version: 0.2-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-fastghquad, r-cran-pracma, r-cran-sparsegrid, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-lavacreg_0.2-2-1.ca2604.1_arm64.deb Size: 163156 MD5sum: 4f1431fbc0e651583442e4736b9cfb8b SHA1: 7aaefe1623d10f07f6c68300680c4d116779994f SHA256: a2ce60dc63b6f98b961df3b63c94f609ba6f4d10fc918a5b4e1e45df78a2d1ba SHA512: d973638814aa9c583654ff115f78e599fec1aba2e35c5a3c484a286e10bc6e5112828a79e2754e63a2fb431cb21e44298764222d3cea293fe09d547ac618a66e Homepage: https://cran.r-project.org/package=lavacreg Description: CRAN Package 'lavacreg' (Latent Variable Count Regression Models) Estimation of a multi-group count regression models (i.e., Poisson, negative binomial) with latent covariates. This packages provides two extensions compared to ordinary count regression models based on a generalized linear model: First, measurement models for the predictors can be specified allowing to account for measurement error. Second, the count regression can be simultaneously estimated in multiple groups with stochastic group weights. The marginal maximum likelihood estimation is described in Kiefer & Mayer (2020) . 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The package contains three main functionalities: Wald tests/F-tests with improved control of the type 1 error in small samples, adjustment for multiple comparisons when searching for local dependencies, and adjustment for multiple comparisons when doing inference for multiple latent variable models. Package: r-cran-lazyeval Architecture: arm64 Version: 0.2.3-1.ca2604.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-lazyeval_0.2.3-1.ca2604.2_arm64.deb Size: 166944 MD5sum: d2435261bcd903242514c08319767099 SHA1: ac432428139231b121b65c6f9e2f43ef2bacf5df SHA256: fa4a8c96eac9edaf0e7500662cad6a84d02de6314b42a007e879916c5e9bc7b7 SHA512: de92df9d4b45a6358edfe51f13016ee1e988830eb0fa4dfbe81282b29519ea2da3400fe6af8ffe38e052bbe8cf6305675f79280de8cce1d36463c1c9f393f9bc Homepage: https://cran.r-project.org/package=lazyeval Description: CRAN Package 'lazyeval' (Lazy (Non-Standard) Evaluation) An alternative approach to non-standard evaluation using formulas. Provides a full implementation of LISP style 'quasiquotation', making it easier to generate code with other code. Package: r-cran-lbamodel Architecture: arm64 Version: 0.2.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 922 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggdmcprior, r-cran-ggdmcmodel, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-ggdmcheaders Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-lbamodel_0.2.9.2-1.ca2604.1_arm64.deb Size: 547354 MD5sum: 101ac71a9f0c4b1b1f144007fe5aeee9 SHA1: 19994523a726152f2df456ec3ebf6c514ca705ec SHA256: 01b57f5b930a23eef390faaea2112093ec9ecd6e9ed2a05c984f96ea98c0aa01 SHA512: 03f26958c30c85ed813855aadce5a7cf3a8f60b6c953ed5615d8a0025d2d0f9a7b19d170331da810bde9361baa1c97ca2c436d7d858a31065aa6c404e2a300da Homepage: https://cran.r-project.org/package=lbaModel Description: CRAN Package 'lbaModel' (The Linear Ballistic Accumulation Model) Provides density, distribution and random generation functions for the Linear Ballistic Accumulation (LBA) model, a widely used choice response time model in cognitive psychology. 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Fit the LBSPR model to length data to estimate selectivity, relative apical fishing mortality, and the spawning potential ratio for data-limited fisheries. See Hordyk et al (2016) for more information about the LBSPR assessment method. Package: r-cran-lcmcr Architecture: arm64 Version: 0.4.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 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 Filename: pool/dists/resolute/main/r-cran-lcmcr_0.4.14-1.ca2604.1_arm64.deb Size: 217590 MD5sum: fe4c8cc02a30864753f47676e5355440 SHA1: df506004f04eb143449dee3eb60794876b83542b SHA256: a0e1af7086680d84098020ea1f00c185094be8997fa6b77dd2f092cb648bec68 SHA512: a958f0632033c71c3669292dfe6328251f70322c47bdc9bf5942fde5fdab2c15b9f0fd48626b00c0ce2f601bf94d3a2bd5df9b27582474bc149fb7af21f48c34 Homepage: https://cran.r-project.org/package=LCMCR Description: CRAN Package 'LCMCR' (Bayesian Non-Parametric Latent-Class Capture-Recapture) Bayesian population size estimation using non parametric latent-class models. 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Package: r-cran-lconnect Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 555 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 191322 MD5sum: 42e35eb6ab0b1e32a56bb19157ec6ce3 SHA1: aed5ca3c02a725e26b0b91022bf544bd7aa2702e SHA256: df74bebc9d6111502e4005b132190c9617049a11a5b9c13538356eabdca24253 SHA512: 56347ee788f3933182f8e7e22f43cfa209d2f900ee47adc9560af71526e95d48585d76ce954029bae300c826a80453ab0d183aa0c9f0b4d5a92b1ff3dfbb8eab Homepage: https://cran.r-project.org/package=lconnect Description: CRAN Package 'lconnect' (Simple Tools to Compute Landscape Connectivity Metrics) Provides functions to upload vectorial data and derive landscape connectivity metrics in habitat or matrix systems. Additionally, includes an approach to assess individual patch contribution to the overall landscape connectivity, enabling the prioritization of habitat patches. The computation of landscape connectivity and patch importance are very useful in Landscape Ecology research. The metrics available are: number of components, number of links, size of the largest component, mean size of components, class coincidence probability, landscape coincidence probability, characteristic path length, expected cluster size, area-weighted flux and integral index of connectivity. Pascual-Hortal, L., and Saura, S. (2006) Urban, D., and Keitt, T. (2001) Laita, A., Kotiaho, J., Monkkonen, M. (2011) . Package: r-cran-lcopula Architecture: arm64 Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 344 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-rcpp Suggests: r-cran-wdm Filename: pool/dists/resolute/main/r-cran-lcopula_1.0.7-1.ca2604.1_arm64.deb Size: 218994 MD5sum: 04a12b4826f11442c248f5770d8db509 SHA1: 1a645f57837beae249b45342eea71465df7914a3 SHA256: 08affce285a46cf85547d9b07652ef4d9470d1839388cf22c85108ce6924b19e SHA512: 5c35dc0ddf83260c27712caf061d1efa70267a99de2f95bc2423b3cca8c14252a4ac14301dc774ca1a66d73e972426548d38bfb11d120ef531e3a913d63b588c Homepage: https://cran.r-project.org/package=lcopula Description: CRAN Package 'lcopula' (Liouville Copulas) Collections of functions allowing random number generations and estimation of 'Liouville' copulas, as described in Belzile and Neslehova (2017) . 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It implements state-of-the-art parameter estimation methods, including the expectation–maximization (EM) algorithm, neural network estimation (NNE; requires users to have 'Python' and its dependent libraries installed on their computer), and integration with 'Mplus' (requires users to have 'Mplus' installed on their computer). In addition, it provides commonly used model fit indices such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC), as well as classification accuracy measures such as entropy. The package also includes fully functional likelihood ratio tests (LRT) and bootstrap likelihood ratio tests (BLRT) to facilitate model comparison, along with bootstrap-based and observed information matrix-based standard error estimation. Furthermore, it supports the standard three-step approach for LCA, LPA, and latent transition analysis (LTA) with covariates, enabling detailed covariate analysis. Finally, it includes several user-friendly auxiliary functions to enhance interactive usability. Package: r-cran-lda Architecture: arm64 Version: 1.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3921 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-matrix, r-cran-reshape2, r-cran-ggplot2, r-cran-penalized, r-cran-nnet Filename: pool/dists/resolute/main/r-cran-lda_1.5.2-1.ca2604.1_arm64.deb Size: 3899314 MD5sum: c653894b781495fc257e99ab4add1cab SHA1: 24d16bb3b06552aa4b7aa1caa6205a46a951516f SHA256: edf48b5a9f2b2724fc1e893ccfb33d20faf4a4ed5614d1baf0650ae7aac8a982 SHA512: 93c397c7aae551a005b070bb98fc725be0a808c2a363fa64b5cd006fadda59830b0c7a4693148d6e8267d05e8ba3d7bd902553ab1ad1d4ce6783cbf190b0ae09 Homepage: https://cran.r-project.org/package=lda Description: CRAN Package 'lda' (Collapsed Gibbs Sampling Methods for Topic Models) Implements latent Dirichlet allocation (LDA) and related models. 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Package: r-cran-ldaandldas Architecture: arm64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3501 Depends: libc6 (>= 2.17), 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-ldaandldas_1.1.3-1.ca2604.1_arm64.deb Size: 3396218 MD5sum: 1a965673835ffaee1653b875e0ce88a4 SHA1: cefd49d9ba3b7337f38358e805299d51be54e152 SHA256: e62927920cc3884c2eec21059826c2884bfb3c238bef8b742165fb1ac82e8df5 SHA512: 1dafb2f411b93520711fa1f133cc55ce9a31065b5db839c4365201ed04a69ff268290b21958ea02ce2f20adf3f6b345e95845bad369d1035e754dbe13e5c2f10 Homepage: https://cran.r-project.org/package=LDAandLDAS Description: CRAN Package 'LDAandLDAS' (Linkage Disequilibrium of Ancestry (LDA) and LDA Score (LDAS)) Computation of linkage disequilibrium of ancestry (LDA) and linkage disequilibrium of ancestry score (LDAS). 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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: arm64 Version: 2.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1195 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_arm64.deb Size: 692802 MD5sum: f9bceb6262482a3171eb809f1436360f SHA1: ecaac96fb20253976a5c7409cac96529779709f8 SHA256: 1e999ae341f173ca09506daee3e960321edc9feda2758c1438fa752404e78259 SHA512: 2809b801f4074fb388d7253d5b2644f61dc59b6ffb5709ebe69e81be1ac216967414940d54b56bc6fdc54e699ff9531d3742748da31e3c59b481d51c9a5729bc 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-lefko3 Architecture: arm64 Version: 6.7.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9937 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 4212794 MD5sum: 2392a9fd43722f6e41e29265e6a327a9 SHA1: f63ec2273ee88421a1845de2188ace51989369ae SHA256: 80a417e3c72273c978933c8a7d6c2ac0212401a673dbcf17b01106a11d34542f SHA512: d93581bde24cb3e0f4d24de877fbd78b93afe7449cf5f24263aee87f3fbb94886a902421d1a099c39748f49dadb491d7977330fc414a51133311ba0f2233a460 Homepage: https://cran.r-project.org/package=lefko3 Description: CRAN Package 'lefko3' (Historical and Ahistorical Population Projection Matrix Analysis) Complete analytical environment for the construction and analysis of matrix population models and integral projection models. Includes the ability to construct historical matrices, which are 2d matrices comprising 3 consecutive times of demographic information. Estimates both raw and function-based forms of historical and standard ahistorical matrices. It also estimates function-based age-by-stage matrices and raw and function-based Leslie matrices. Package: r-cran-legion Architecture: arm64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1412 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 885826 MD5sum: 6321e3fa1fd8ad8e46e7095573c1dbde SHA1: e5829f67d2dbc565f9342a9e6460b740692cb503 SHA256: 788c5fe9e261c16616b3c8c79d0e3b70193c095a188eab1f0e680e834a770840 SHA512: 85ce7a25072f9553c0be50dbb9bafc1c1d50a7c4ec77faac8c297944cd20aafe9fd20caafc9ca0078aaf339a47fe279ce7b8ab378184da2409f03cd232531991 Homepage: https://cran.r-project.org/package=legion Description: CRAN Package 'legion' (Forecasting Using Multivariate Models) Functions implementing multivariate state space models for purposes of time series analysis and forecasting. The focus of the package is on multivariate models, such as Vector Exponential Smoothing, Vector ETS (Error-Trend-Seasonal model) etc. It currently includes Vector Exponential Smoothing (VES, de Silva et al., 2010, ), Vector ETS (Svetunkov et al., 2023, ) and simulation function for VES. Package: r-cran-leidenalg Architecture: arm64 Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 608 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_arm64.deb Size: 221436 MD5sum: e558982b8c9a56549e286d50b93df37d SHA1: 1969ac994b57c470cb901f1ec058c91c8eb23313 SHA256: bf19bde1b25ac492ca7f72a81dd526f85e1e8708d4247dbe849a9310797c5ba1 SHA512: cebf6215a55a50e9e5e80c1d75624311dd4064525367c69d8540c1196f6631914b0ee565b96cd9646e609f339278b112d6fe43dda7a3729464c080f64059938d Homepage: https://cran.r-project.org/package=leidenAlg Description: CRAN Package 'leidenAlg' (Implements the Leiden Algorithm via an R Interface) An R interface to the Leiden algorithm, an iterative community detection algorithm on networks. The algorithm is designed to converge to a partition in which all subsets of all communities are locally optimally assigned, yielding communities guaranteed to be connected. The implementation proves to be fast, scales well, and can be run on graphs of millions of nodes (as long as they can fit in memory). The original implementation was constructed as a python interface "leidenalg" found here: . The algorithm was originally described in Traag, V.A., Waltman, L. & van Eck, N.J. "From Louvain to Leiden: guaranteeing well-connected communities". Sci Rep 9, 5233 (2019) . Package: r-cran-leidenbase Architecture: arm64 Version: 0.1.37-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2929 Depends: libblas3 | libblas.so.3, libc6 (>= 2.43), libgcc-s1 (>= 4.2), libglpk40 (>= 4.59), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-igraph Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-pandoc Filename: pool/dists/resolute/main/r-cran-leidenbase_0.1.37-1.ca2604.1_arm64.deb Size: 1118330 MD5sum: d8a869adf6c260515782193f9181f483 SHA1: e9d2fbb26aba00a16f3d8a672b4d676673a19951 SHA256: fc2626af460c0babcbbf2a6b3349cffc3169b5c2a9819a24b57a5891d1527e6d SHA512: 6fd1cff633f11bb37153d7a011a625ae1934fc9822054fcdd7e47e6cb7051ee460c7dc662d7726f4084cb3252e132bf2356fbd47d4e7699938015db49af1ebe1 Homepage: https://cran.r-project.org/package=leidenbase Description: CRAN Package 'leidenbase' (R and C/C++ Wrappers to Run the Leiden find_partition() Function) An R to C/C++ interface that runs the Leiden community detection algorithm to find a basic partition (). It runs the equivalent of the 'leidenalg' find_partition() function, which is given in the 'leidenalg' distribution file 'leiden/src/functions.py'. This package includes the required source code files from the official 'leidenalg' distribution and functions from the R 'igraph' package. The 'leidenalg' distribution is available from and the R 'igraph' package is available from . The Leiden algorithm is described in the article by Traag et al. (2019) . Leidenbase includes code from the packages: igraph version 0.9.8 with license GPL (>= 2), leidenalg version 0.8.10 with license GPL 3. Package: r-cran-lemarns Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1050 Depends: libc6 (>= 2.17), 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-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-lemarns_0.1.2-1.ca2604.1_arm64.deb Size: 517952 MD5sum: b22eaaf9edfdbc7cbde214d8e43d05fa SHA1: 4ee6db8ba6b29c80aef96937d9722aafbd519652 SHA256: 0769e6e9fecd715e28c531d3b0abd8f19c80f50336a965ee194f9d3a751a2992 SHA512: d35d196e18581eda752fa6255a623944bb5a66809961d6ebfcae8e243060531365647c3b711cae09199a8a7c98ca322df584074beda022073ee482d93f160abb Homepage: https://cran.r-project.org/package=LeMaRns Description: CRAN Package 'LeMaRns' (Length-Based Multispecies Analysis by Numerical Simulation) Set up, run and explore the outputs of the Length-based Multi-species model (LeMans; Hall et al. 2006 ), focused on the marine environment. 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Lemna is a standard test macrophyte used in ecotox effect studies. The model was described and published by the SETAC Europe Interest Group Effect Modeling. It is a refined description of the Lemna TKTD model published by Schmitt et al. (2013) . 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Useful for estimating linear models with multiple group fixed effects, and for estimating linear models which uses factors with many levels as pure control variables. See Gaure (2013) Includes support for instrumental variables, conditional F statistics for weak instruments, robust and multi-way clustered standard errors, as well as limited mobility bias correction (Gaure 2014 ). Since version 3.0, it provides dedicated functions to estimate Poisson models. 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We developed a least-squares estimation approach for confounder and effect sizes estimation that provides a unique framework for several categories of genomic data, not restricted to genotypes. The speed of the new algorithm is several times faster than the existing GEA approaches, then our previous version of the 'LFMM' program present in the 'LEA' package (Frichot and Francois, 2015, ). 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The first two functions simulate from a univariate and a multivariate log-GARCH model, respectively, whereas the latter two estimate a univariate and multivariate log-GARCH model, respectively. Package: r-cran-lgcu Architecture: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 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-tictoc Filename: pool/dists/resolute/main/r-cran-lgcu_0.1.5-1.ca2604.1_arm64.deb Size: 163510 MD5sum: 8f55b876af65aca4678d2a59cd685d56 SHA1: 04d3c93965f033b9c837c28949d0f7bb766e77fc SHA256: 1a82d8bdcbc1f02033a3c4dd8f12ff15058eb35c146ccc6c7c4743691f067a9c SHA512: ea56413f3349477b2473e393a8d1f6d6f11de3d5817a40bd80bf3e4911dd761760775806f78171e33509c8b5d162f733b5852b6abcf48b1596171c9806541f26 Homepage: https://cran.r-project.org/package=LGCU Description: CRAN Package 'LGCU' (Implementation of Learning Gamma CUSUM (Cumulative Sum) ControlCharts) Implements Cumulative Sum (CUSUM) control charts specifically designed for monitoring processes following a Gamma distribution. Provides functions to estimate distribution parameters, simulate control limits, and apply cautious learning schemes for adaptive thresholding. It supports upward and downward monitoring with guaranteed performance evaluated via Monte Carlo simulations. It is useful for quality control applications in industries where data follows a Gamma distribution. Methods are based on Madrid-Alvarez et al. (2024) and Madrid-Alvarez et al. (2024) . 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Provides tools for fitting, prediction, and inference using a constrained optimization approach to enforce smoothness. Supports generalized linear models, Weibull accelerated failure time (AFT) models, Cox proportional hazards models, quadratic programming constraints, and customizable working-correlation structures, with options for parallel fitting. The core spline construction builds on Ezhov et al. (2018) . Quadratic-programming and SQP details follow Goldfarb & Idnani (1983) and Nocedal & Wright (2006) . For smoothing spline and penalized spline background, see Wahba (1990) and Wood (2017) . For variance-component and correlation-parameter estimation, see Searle et al. (2006) . The default multivariate partitioning step uses k-means clustering as in MacQueen (1967). Package: r-cran-lhs Architecture: arm64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 829 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-doe.base, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-lhs_1.3.0-1.ca2604.1_arm64.deb Size: 352440 MD5sum: fe9751b233f17fd388ffd11f3e449517 SHA1: d8269b25f1c9570490c5d9d6ecf958019beb31e6 SHA256: eb5a483803c6316e30455e9ab04f965600234fb8ad17af18b4bce69da3f6d6ed SHA512: d3ad94f5ddcd82e71e1c9f6fadbbad4f73f1667c87dc71e8af98bc029877cd46bbcbff41128836893da233caa572641be27b1ec6df89f13f5b44104d2fe0d5de Homepage: https://cran.r-project.org/package=lhs Description: CRAN Package 'lhs' (Latin Hypercube Samples) Provides a number of methods for creating and augmenting Latin Hypercube Samples and Orthogonal Array Latin Hypercube Samples. Package: r-cran-libcoin Architecture: arm64 Version: 1.0-12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1764 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-coin, r-cran-bibtex Filename: pool/dists/resolute/main/r-cran-libcoin_1.0-12-1.ca2604.1_arm64.deb Size: 768862 MD5sum: 20d676223e98cb29bd72d2d900ad66f6 SHA1: c753f239bd59f65babf32d9ea71c010ff4ed1b36 SHA256: 392374eb024c5d32e4534ed143d28b7cbffa5edb5359665a4593247aa68a6c25 SHA512: 5b2f241394656babeb25430c47d9c1876933d680c03eaea62935f37a5b5dcc8082278cc233d5523668ea69497ec91ee9dea4e222162a042ca47c5e22e0f31dd6 Homepage: https://cran.r-project.org/package=libcoin Description: CRAN Package 'libcoin' (Linear Test Statistics for Permutation Inference) Basic infrastructure for linear test statistics and permutation inference in the framework of Strasser and Weber (1999) . This package must not be used by end-users. CRAN package 'coin' implements all user interfaces and is ready to be used by anyone. Package: r-cran-libdeflate Architecture: arm64 Version: 1.25-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-libdeflate_1.25-0-1.ca2604.1_arm64.deb Size: 71940 MD5sum: 415b6b3c7f066f2dd5eb97848f08b3c6 SHA1: 255390d90309d1768b98bfb95c97c8711916fd53 SHA256: 31b643382ebeaad46f808e0c8bcb76bf51de89d0a794eb50b43cd1c0acec6a1a SHA512: b5a273d15d95e18a1e4d37bca293c590de52b966a3e35e79e961b3889ecc3b4e0fd037e0fb1dc957c861a869581389adbe1ea37007a65fc643d77fe203c98c8e Homepage: https://cran.r-project.org/package=libdeflate Description: CRAN Package 'libdeflate' (DEFLATE Compression and Static Library) Whole-buffer DEFLATE-based compression and decompression of raw vectors using the 'libdeflate' library (see ). 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Package: r-cran-libgeos Architecture: arm64 Version: 3.11.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2834 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 Filename: pool/dists/resolute/main/r-cran-libgeos_3.11.1-3-1.ca2604.1_arm64.deb Size: 806726 MD5sum: 9438f6a5b2e896d67936280315c11b14 SHA1: 4eff700408068032c37b6f1bef03c7d3244fd3d5 SHA256: 7ef4f6ff527fad1a8f6152626a1f7cc0c0ca7abfe26ef2515209b9f72f7e3398 SHA512: b5defc5ab49d4e1504610d360ebd90dd3c776eb80ff38c5811538f8a67a6c40f4ae1fb56a7a8641e519e0223a8944c6d2f789eeacf762a2d26578c31286a7c93 Homepage: https://cran.r-project.org/package=libgeos Description: CRAN Package 'libgeos' (Open Source Geometry Engine ('GEOS') C API) Provides the Open Source Geometry Engine ('GEOS') as a C API that can be used to write high-performance C and C++ geometry operations using R as an interface. 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Package: r-cran-liblinear Architecture: arm64 Version: 2.10-24-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-sparsem, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-liblinear_2.10-24-1.ca2604.1_arm64.deb Size: 77664 MD5sum: d54e9f29cce3c6b59c1115343316e058 SHA1: cfcd8e8d51038f44508335d6942eea5632dc1408 SHA256: 8f9fbb25730a8f8a7ed75fb4dc707c68b38fd02b4cd2446182d1c3985e2534f2 SHA512: 7f48009cad82187b0dc7d7f73a9972113d7325c0cfc43306559832da6590ef233685207e93329f8e92d6cde0cf2484252ce0a8b41b5c3cf0c098853385b4e5b8 Homepage: https://cran.r-project.org/package=LiblineaR Description: CRAN Package 'LiblineaR' (Linear Predictive Models Based on the LIBLINEAR C/C++ Library) A wrapper around the LIBLINEAR C/C++ library for machine learning (available at ). 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Package: r-cran-libra Architecture: arm64 Version: 1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 461 Depends: libc6 (>= 2.17), libgsl28 (>= 2.8+dfsg), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnls Suggests: r-cran-lars, r-cran-mass, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-libra_1.7-1.ca2604.1_arm64.deb Size: 378204 MD5sum: 61554830352496f00b6617363d654444 SHA1: 5295598ca500e7526f21faa18e0260d99b984ba4 SHA256: 57096c396b50d25f302c824b9cf6b32a82cae02d601f30a4ff9f5e07b4255029 SHA512: 4170e9f97b639409423686783b0bc19d0bbe886b35bc5d24c39a0f5b66989c132428b12376abe96ddf21179ef41bed097015404a85882aec07f02df532d0cbdd Homepage: https://cran.r-project.org/package=Libra Description: CRAN Package 'Libra' (Linearized Bregman Algorithms for Generalized Linear Models) Efficient procedures for fitting the regularization path for linear, binomial, multinomial, Ising and Potts models with lasso, group lasso or column lasso(only for multinomial) penalty. 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Package: r-cran-libstable4u Architecture: arm64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 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-testthat Filename: pool/dists/resolute/main/r-cran-libstable4u_1.0.5-1.ca2604.1_arm64.deb Size: 92886 MD5sum: b2a5a1229488450ec7062e7c1d37b98d SHA1: 4a8e019476ee35be6044674d538a0227d9e72a1c SHA256: f66c1fe7bab67903c2c9149696993f11bb98ac1a4ab23779ea0b9974a3da1a1c SHA512: 33ed6ec8c3aa645f36dcbed8227eabd923ae3335f480e6e9086ee8be847246fe1702ff5e8350099839c13c4e0e8fedf04e78947e702f6ef3c76c69fb0f32c0b8 Homepage: https://cran.r-project.org/package=libstable4u Description: CRAN Package 'libstable4u' (Stable Distribution Functions...For You) Tools for fast and accurate evaluation of skew stable distributions (CDF, PDF and quantile functions), random number generation, and parameter estimation. 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Alcoriza-Balaguer MI, Garcia-Canaveras JC, Lopez A, Conde I, Juan O, Carretero J, Lahoz A (2019) . 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This survey methodology is also known as the item count technique or the unmatched count technique and is an alternative to the commonly used randomized response method. The package implements the methods developed by Imai (2011) , Blair and Imai (2012) , Blair, Imai, and Lyall (2013) , Imai, Park, and Greene (2014) , Aronow, Coppock, Crawford, and Green (2015) , Chou, Imai, and Rosenfeld (2017) , and Blair, Chou, and Imai (2018) . This includes a Bayesian MCMC implementation of regression for the standard and multiple sensitive item list experiment designs and a random effects setup, a Bayesian MCMC hierarchical regression model with up to three hierarchical groups, the combined list experiment and endorsement experiment regression model, a joint model of the list experiment that enables the analysis of the list experiment as a predictor in outcome regression models, a method for combining list experiments with direct questions, and methods for diagnosing and adjusting for response error. In addition, the package implements the statistical test that is designed to detect certain failures of list experiments, and a placebo test for the list experiment using data from direct questions. 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See Bartolucci, Pandolfi, Pennoni (2017). Package: r-cran-lmm Architecture: arm64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 664 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_arm64.deb Size: 441592 MD5sum: 73c93dc79229941723260f02fc19fc6a SHA1: 436443978f950334918d01e28a6f497965316ca8 SHA256: 2ca070510f282706dcb59c3074dc2e9e8740ad133046e001d83b4801ce745e95 SHA512: f30d7b4a21a85880a7a703f7e7a24daf474e660c9a1b2e049759f06307cc909f5f9ae31cdeb5249d1ba79db2ef3de781f78f72b4bfec140a9769a387fdc4fda4 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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This allows models on the within-group variance with mixed effects, and between-group variances with fixed effects. The MELSM can be used to model volatility, intraindividual variance, uncertainty, measurement error variance, and more. Multivariate MELSMs (MMELSMs) extend the model to include multiple correlated outcomes, and therefore multiple locations and scales. The latent multivariate MELSM (LMMELSM) further includes multiple correlated latent variables as outcomes. This package implements two-level mixed effects location scale models on multiple observed or latent outcomes, and between-group variance modeling. Williams, Martin, Liu, and Rast (2020) . Hedeker, Mermelstein, and Demirtas (2008) . 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Local Gaussian parameters are useful for characterizing and testing for non-linear dependence within bivariate data. See e.g. Tjostheim and Hufthammer, Local Gaussian correlation: A new measure of dependence, Journal of Econometrics, 2013, Volume 172 (1), pages 33-48 . Package: r-cran-localllm Architecture: arm64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 803 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-jsonlite, r-cran-digest, r-cran-curl, r-cran-r.utils Suggests: r-cran-testthat, r-cran-covr, r-cran-irr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-localllm_1.3.0-1.ca2604.1_arm64.deb Size: 380704 MD5sum: f0e342e86edfcdf69bfa27afb9ad3d84 SHA1: d305693659aead168da068368f9f140595c60784 SHA256: 6929b0545cbaee613e1dec96f6a99621d267c3b8a7ff55c5a69aaf0883065144 SHA512: f763d4549998d367955c9da36b1eeb141c2565bcbda87c6c3a6c81a449ab234170258589343f89b7c986140156ec6a6ca164f6fe5c20e894a01215bb4df1fb5e Homepage: https://cran.r-project.org/package=localLLM Description: CRAN Package 'localLLM' (Running Local LLMs with 'llama.cpp' Backend) Provides R bindings to the 'llama.cpp' library for running large language models. The package uses a lightweight architecture where the C++ backend library is downloaded at runtime rather than bundled with the package. Package features include text generation, reproducible generation, and parallel inference. Package: r-cran-localscore Architecture: arm64 Version: 2.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1249 Depends: libc6 (>= 2.38), 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-testthat Filename: pool/dists/resolute/main/r-cran-localscore_2.0.5-1.ca2604.1_arm64.deb Size: 761064 MD5sum: eafd42102db6b13452ccef706c8c0b32 SHA1: 9c2d207e50eeffdd84eb555621faf00916f9716f SHA256: 127d8713bf360ba779ea86b646260c1aeab7dfa707877ba89554e1d7e23fc09d SHA512: a401e3a1d80f91d1084093b0f4e2b94184cf50e7b8753a77e37a51a9ffd9b12196a09ab304bf307b562481ca9a2d2e9b8bffb887717a46f43e97e4b4491b40ef Homepage: https://cran.r-project.org/package=localScore Description: CRAN Package 'localScore' (Package for Sequence Analysis by Local Score) Functionalities for calculating the local score and calculating statistical relevance (p-value) to find a local Score in a sequence of given distribution (D. Robelin, S. Déjean, S. Mercier (2025) ; S. Mercier and J.-J. Daudin (2001) ) ; S. Karlin and S. Altschul (1990) ; S. Mercier, D. Cellier and F. Charlot (2003) ; A. Lagnoux, S. Mercier and P. Valois (2017) ). 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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. . 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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) . 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Package: r-cran-locstra Architecture: arm64 Version: 1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 481 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_arm64.deb Size: 234060 MD5sum: 7d0d533323638b9c63ec4e72ac1c0c6e SHA1: 289d85d2cf3eacff68c95bcb93090a50416ad35f SHA256: f8414316dbc9dba993e0cac5addd2312474a0bcbec91c6076c324f56afa8d721 SHA512: 7b49fa8682300f3b68e17bc687e3370103515c764658e0653fef530f45e1d897e968066fb86623a7939d2cbad57294c8032becc6b7d4a0fd57fc4087bef0d83d 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) ). 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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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Package: r-cran-lqmm Architecture: arm64 Version: 1.5.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 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_arm64.deb Size: 282244 MD5sum: 3fc00d0ddaf481a166f95d8d69f1c3a5 SHA1: ffeded19d0c6a18da28ae02922d6aba21d504d8e SHA256: 6e0068f0f190f427552b8a3fbe3a09e500251485c81648c2e9f520478b077e8b SHA512: 0e6bb8559ff9e701abc145792fa9f95e66a4d1cab8f71d87e70f092cec7dfd1887dea8391abe07d00bfb6eb29baa485d980f4c5107317eceb58f32fdb3298525 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) . 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Package: r-cran-lrqvb Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 91220 MD5sum: e0442596f6f3f7174bf3b2ec048c3cd5 SHA1: 62dcaf6e08961be0d80e3da40f1512a864927b9c SHA256: 1336e0eb8a7629aaaab8c23ce3b27d78d837230ce822c0e3a3dfe691860ae8a2 SHA512: 5104e3d0a1b0b2cb73d6024bec7d14501ba884c345824b557e8814300ee2ec201e13cacc08f407aa72d003ef09a1ac4f4c720e51d99f97029dfd36955609ba06 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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Package: r-cran-lsoda Architecture: arm64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 244 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_arm64.deb Size: 81722 MD5sum: 2f3ef9048ad800ca2c96add2c7f0d81d SHA1: ef89466a0900b98a5d8d627bb68209b9192c0929 SHA256: c6d739fd6ebcd9ee13ad2d17aeabc9a25fcc2446019fa47fb389fecde2afc09a SHA512: 27951452218c5631f066ac63cd8bde2089f5e3fe9efd91c0901a161e603e523257cc1b750c189efab02f35b84c4bbbf20293f6c63e104fc9e613ae7bda170c81 Homepage: https://cran.r-project.org/package=lsoda Description: CRAN Package 'lsoda' ('C++' Header Library for Ordinary Differential Equations) A 'C++' header library for using the 'libsoda-cxx' library with R. 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This includes, amongst others, data import, export, application of age models, curve deconvolution, sequence analysis and plotting of equivalent dose distributions. Package: r-cran-lutz Architecture: arm64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4521 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-lubridate Suggests: r-cran-testthat, r-cran-sf, r-cran-sp, r-cran-covr, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-lutz_0.3.2-1.ca2604.1_arm64.deb Size: 4414620 MD5sum: 97ff384669850c349ecd2fab59af31f4 SHA1: cccb00265f9d541f87cc78b4c0c4402ec7bf3b5f SHA256: 860592da29d6f12316b7288e183ca11b93707e561b2c44db433919fc3dc9975a SHA512: 2689efee2c6f1eb30df28870691350120343df9fa563165b58ea56b7891d832332f3a1e2d70581f404574bacd9f449e6ed2adfde18fab856406854f7ba2b25bc Homepage: https://cran.r-project.org/package=lutz Description: CRAN Package 'lutz' (Look Up Time Zones of Point Coordinates) Input latitude and longitude values or an 'sf/sfc' POINT object and get back the time zone in which they exist. Two methods are implemented. One is very fast and uses 'Rcpp' in conjunction with data from the 'Javascript' library (). This method also works outside of countries' borders and in international waters, however speed comes at the cost of accuracy - near time zone borders away from populated centres there is a chance that it will return the incorrect time zone. The other method is slower but more accurate - it uses the 'sf' package to intersect points with a detailed map of time zones from here: . The package also contains several utility functions for helping to understand and visualize time zones, such as listing of world time zones, including information about daylight savings times and their offsets from UTC. You can also plot a time zone to visualize the UTC offset over a year and when daylight savings times are in effect. Package: r-cran-lwfbrook90r Architecture: arm64 Version: 0.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2266 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-vegperiod, r-cran-foreach, r-cran-iterators, r-cran-dofuture, r-cran-future, r-cran-parallelly, r-cran-progressr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-lwfbrook90r_0.6.3-1.ca2604.1_arm64.deb Size: 1921556 MD5sum: e2a81619a7b8cc20603b9d3c1a30bc76 SHA1: 6e7dbfa397cc4d7fc92a63a74caa0392c7277219 SHA256: 8987605cc95b8f9b765506a4b03bcd166604a45f885b10fa9556a2f897ea8ac8 SHA512: f4a5ebde7b0df65f157bf1df498e5a9b00212fa2bd414044b49a8e8bd28e45c9fb3675f249ba0d980e337494127226755ee80e0d11c3571957cf74297d55d559 Homepage: https://cran.r-project.org/package=LWFBrook90R Description: CRAN Package 'LWFBrook90R' (Simulate Evapotranspiration and Soil Moisture with the SVATModel LWF-Brook90) Provides a flexible and easy-to use interface for the soil vegetation atmosphere transport (SVAT) model LWF-BROOK90, written in Fortran. The model simulates daily transpiration, interception, soil and snow evaporation, streamflow and soil water fluxes through a soil profile covered with vegetation, as described in Hammel & Kennel (2001, ISBN:978-3-933506-16-0) and Federer et al. (2003) . A set of high-level functions for model set up, execution and parallelization provides easy access to plot-level SVAT simulations, as well as multi-run and large-scale applications. Package: r-cran-lwgeom Architecture: arm64 Version: 0.2-16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1030 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libgeos-c1t64 (>= 3.5.0), libproj25 (>= 6.0.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-units, r-cran-sf Suggests: r-cran-covr, r-cran-sp, r-cran-geosphere, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-lwgeom_0.2-16-1.ca2604.1_arm64.deb Size: 393482 MD5sum: dfefbd797b9c6f5b4766422879027b05 SHA1: f48650e0973758a48e595c14b2012b6478a9cc61 SHA256: 7271b32fd2ab913802d945ca01cce988ff68b264816315ec7aa0136ce44d9829 SHA512: d99c43f99c0fd104b16cfad1b585179d190ca1c6c2f90eebd8415add05efa1fa1e0ff6148f346bd5564f33f28b03bad330dea89dbe2c9b14e861f9b15f901c6c Homepage: https://cran.r-project.org/package=lwgeom Description: CRAN Package 'lwgeom' (Bindings to Selected 'liblwgeom' Functions for Simple Features) Access to selected functions found in 'liblwgeom' , the light-weight geometry library used by 'PostGIS' . Package: r-cran-lzstring Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1754 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-bench, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-lzstring_0.2.0-1.ca2604.1_arm64.deb Size: 1681728 MD5sum: bb1d7188d5d0ec0476c6ff2f2dc379d0 SHA1: c3fadfe9d0913ac51d9f1bb9f5710fbc1dd4f437 SHA256: 9913a159120bf0d64b259a974134b9ba6c889e402ddf30698af564714239f406 SHA512: a0f18503dffc6b785ced184494d84368375476c2c2108bc3436e259f795b3ace5ce6d190f454a4bc83ef3fe12d2cf7586aa881f49a739bb5c0cafd507ef89936 Homepage: https://cran.r-project.org/package=lzstring Description: CRAN Package 'lzstring' (Wrapper for 'lz-string' 'C++' Library) Provide access to the 'lz-string' 'C++' library for Lempel-Ziv (LZ) based compression and decompression of strings. Package: r-cran-m2r Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 826 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mpoly, r-cran-stringr, r-cran-memoise, r-cran-gmp, r-cran-usethis, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-microbenchmark, r-cran-testthat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-m2r_1.0.3-1.ca2604.1_arm64.deb Size: 643922 MD5sum: d8241b9875bfe371d4b085f9dee8b33c SHA1: e69c6664681d8aa79c8527592665b44b4563305d SHA256: 85424ac755a61f6ec568f5ae63384a71b2b094565ad8ced82aeb435884cef2e6 SHA512: 22e44baf399805f87737cd52b6cfd87f2b3276a58e565ddf2246009b43e74e1b043390470cef35411a65e7a9c44051ac4f2b9e406efeaa60e84058db65a50ac5 Homepage: https://cran.r-project.org/package=m2r Description: CRAN Package 'm2r' (Interface to 'Macaulay2') Persistent interface to 'Macaulay2' and front-end tools facilitating its use in the 'R' ecosystem. For details see Kahle et. al. (2020) . Package: r-cran-mable Architecture: arm64 Version: 4.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1439 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1060818 MD5sum: af0b70848abc5a7cd437f2bd9d202d68 SHA1: 9fb9e7b9f84722225d1e1fc356c3dd8977ab96e0 SHA256: cdda5594926d80ac3206b8663c45f8b0f78d36105ae467276e4e13282b85febb SHA512: 0b089fd3b08f92cf31bd749b414e7b975079c478a2be82bb25795ef0606bc77d0ccf851dd37d1ac755ae7793c0c8cc3a67c471cd525e67864484c6ad2051bf55 Homepage: https://cran.r-project.org/package=mable Description: CRAN Package 'mable' (Maximum Approximate Bernstein/Beta Likelihood Estimation) Fit data from a continuous population with a smooth density on finite interval by an approximate Bernstein polynomial model which is a mixture of certain beta distributions and find maximum approximate Bernstein likelihood estimator of the unknown coefficients. Consequently, maximum likelihood estimates of the unknown density, distribution functions, and more can be obtained. If the support of the density is not the unit interval then transformation can be applied. This is an implementation of the methods proposed by the author of this package published in the Journal of Nonparametric Statistics: Guan (2016) and Guan (2017) . For data with covariates, under some semiparametric regression models such as Cox proportional hazards model and the accelerated failure time model, the baseline survival function can be estimated smoothly based on general interval censored data. Package: r-cran-maboust Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 277 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 102792 MD5sum: 3dba78beea86a996c97b76111cb8a718 SHA1: b2f3d5f3ac8f5e37ccaf11ed2480c86e876e2063 SHA256: c2530982d3e4de320c3d4bd706620367f61fa524d3fa02f563b432a2bb11a06c SHA512: 787a628e4878e865e4a7a5041a1807b2fdeb90df9d220f63ddc773092d8dc9c1462343b6aa77971799afa1f8bc1dff314aa8e01551986d1b9f64e861f44ad171 Homepage: https://cran.r-project.org/package=MABOUST Description: CRAN Package 'MABOUST' (Multi-Armed Bayesian Ordinal Utility-Based Sequential Trial) Conducts and simulates the MABOUST design, including making interim decisions to stop a treatment for inferiority or stop the trial early for superiority or equivalency. Package: r-cran-machineshop Architecture: arm64 Version: 3.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3334 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_arm64.deb Size: 2186870 MD5sum: eb3f47c0cb5d2ddd38d34972c97b16cf SHA1: 2e8f5a82c313c0eaa471b76cebe37886060c15d4 SHA256: b7d7b1334c8b5e5a512a607989d747d03a8efdf91ce996b04de4eae34e15f46a SHA512: c6aeed715a20f0dc748aa72ca31d090d657c5db93053cac3155543f3cc42938cd88a319374c064f9dd1132aa57d4470ecc1bce4f202a498f632e9a9695bbfa01 Homepage: https://cran.r-project.org/package=MachineShop Description: CRAN Package 'MachineShop' (Machine Learning Models and Tools) Meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Approaches for model fitting and prediction of numerical, categorical, or censored time-to-event outcomes include traditional regression models, regularization methods, tree-based methods, support vector machines, neural networks, ensembles, data preprocessing, filtering, and model tuning and selection. Performance metrics are provided for model assessment and can be estimated with independent test sets, split sampling, cross-validation, or bootstrap resampling. Resample estimation can be executed in parallel for faster processing and nested in cases of model tuning and selection. Modeling results can be summarized with descriptive statistics; calibration curves; variable importance; partial dependence plots; confusion matrices; and ROC, lift, and other performance curves. Package: r-cran-mactivate Architecture: arm64 Version: 0.6.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mactivate_0.6.6-1.ca2604.1_arm64.deb Size: 447334 MD5sum: c8d2a01bf64b117efa924d395841d012 SHA1: 0a75c2c93e8579668660f8c62ad8684a78f5ef25 SHA256: eb06ee2bea70219b7a4a2f287cc0a9c71a5c5684255ab862862cf8d268369de8 SHA512: 11fa6d758b763e0d497607aea27a625293d3e519993d0c03a4a2464897b6631c2257a04667c5359c3ec940b9da8b6eabbba31f522643432119809c228f5ac097 Homepage: https://cran.r-project.org/package=mactivate Description: CRAN Package 'mactivate' (Multiplicative Activation) Provides methods and classes for adding m-activation ("multiplicative activation") layers to MLR or multivariate logistic regression models. M-activation layers created in this library detect and add input interaction (polynomial) effects into a predictive model. M-activation can detect high-order interactions -- a traditionally non-trivial challenge. Details concerning application, methodology, and relevant survey literature can be found in this library's vignette, "About." Package: r-cran-madmmplasso Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 312928 MD5sum: e93b899c97de4756814d2a9a97b438f1 SHA1: d512fca9d80b0152e849bb2fdc1a63508474bad6 SHA256: 21b9cf8ab58b76482519c83fc56011e62fac7e05d29d5c8d175d59aadd23e57a SHA512: 3d7ef91d29f805e6a726a0f8d1d3415868d36c05e804df56a04d73bb87791effb384c784749e0f62c2a87f414f3434a579ad5744ba4625182c9fc7653ef64df4 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: arm64 Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1519 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_arm64.deb Size: 579124 MD5sum: b66013bd1b7f14be7cf5782000089972 SHA1: b38272bbc59e351bb08c7e1a01f5aa9c6ffe8b45 SHA256: 288b53ae319abcbb62981e666baa535ccf9d0093c79ed8cfe2735e1b1a877ded SHA512: 26cbc4231a22fb0618aed1c112c44f1423360a16d4608cb1e61f6e2a7db64fa63203894ace5721252fbc3c84a9d16ebc7d1c91efea80799fe0f1c8517589466d Homepage: https://cran.r-project.org/package=MADPop Description: CRAN Package 'MADPop' (MHC Allele-Based Differencing Between Populations) Tools for the analysis of population differences using the Major Histocompatibility Complex (MHC) genotypes of samples having a variable number of alleles (1-4) recorded for each individual. A hierarchical Dirichlet-Multinomial model on the genotype counts is used to pool small samples from multiple populations for pairwise tests of equality. Bayesian inference is implemented via the 'rstan' package. Bootstrapped and posterior p-values are provided for chi-squared and likelihood ratio tests of equal genotype probabilities. Package: r-cran-magee Architecture: arm64 Version: 1.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2200 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libdeflate0 (>= 1.0), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1842648 MD5sum: a447c62aad0d72a55cba8f63fea3667d SHA1: be0f2adc382c9d015d8aa54435498c0b6cdbce36 SHA256: a14402afc1a1208054c730f5d5e0ef24c1378aaa1d5ba096b1b0cbd52437c48c SHA512: b72601e47c35736da34a93aaa3ac779d40fb9235faf0a9add422793b9fd9a8d3fe453974e1a2ff33e383ccb808002ebfec68a066c3fc08e255aeacf5ef2c6b78 Homepage: https://cran.r-project.org/package=MAGEE Description: CRAN Package 'MAGEE' (Mixed Model Association Test for GEne-Environment Interaction) Use a 'glmmkin' class object (GMMAT package) from the null model to perform generalized linear mixed model-based single-variant and variant set main effect tests, gene-environment interaction tests, and joint tests for association, as proposed in Wang et al. (2020) . 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Implements the MAGI method (MAnifold-constrained Gaussian process Inference) of Yang, Wong, and Kou (2021) . A user guide is provided by the accompanying software paper Wong, Yang, and Kou (2024) . Package: r-cran-magick Architecture: arm64 Version: 2.9.1-1.ca2604.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7515 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_arm64.deb Size: 4820790 MD5sum: 2c35ceec85169016a4f7b193bf59c9dd SHA1: 20196a390e17f385ad7133656d91918f0f7a08f6 SHA256: c69ad6987e46187d42d7f9b5768d5840d919c9468f8f628989e17d355614382d SHA512: 15971f0254d6d7a9c39d068c81fd005ddc8f6ba9c7d94238bc7a9569356cb3f92a0ff741a786e3af8c7bbf0e85614e84ace54f1ad5521e63620fb13e454ac535 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. 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Two main algorithms, called 'Magma' and 'MagmaClust', are available to perform predictions for supervised learning problems, in particular for time series or any functional/continuous data applications. The corresponding articles has been respectively proposed by Arthur Leroy, Pierre Latouche, Benjamin Guedj and Servane Gey (2022) , and Arthur Leroy, Pierre Latouche, Benjamin Guedj and Servane Gey (2023) . Theses approaches leverage the learning of cluster-specific mean processes, which are common across similar tasks, to provide enhanced prediction performances (even far from data) at a linear computational cost (in the number of tasks). 'MagmaClust' is a generalisation of 'Magma' where the tasks are simultaneously clustered into groups, each being associated to a specific mean process. User-oriented functions in the package are decomposed into training, prediction and plotting functions. Some basic features (classic kernels, training, prediction) of standard Gaussian processes are also implemented. Package: r-cran-magree Architecture: arm64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-magree_1.2-1.ca2604.1_arm64.deb Size: 122072 MD5sum: 4aa81274d734642b5e97a1c202f18537 SHA1: f27225a58558569bbc6090a17532943ab54d6975 SHA256: 12e7016016de52442d4e32dc085d4bbcd1b3a00bc4e1931aa3268c1e118eb374 SHA512: d679d2fa232f7492dc8e23cb4a4788507a91551e97fcbb703e5bc7c0840e669e83bec9a677e5c81d4d9fcee323f3464f0f2e63bebeae6cb0891ebbb530a50db5 Homepage: https://cran.r-project.org/package=magree Description: CRAN Package 'magree' (Implements the O'Connell-Dobson-Schouten Estimators of Agreementfor Multiple Observers) Implements an interface to the legacy Fortran code from O'Connell and Dobson (1984) . 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We provide a number of algorithms for selected problems in optimization and statistical inference. For general exposition to the topic with focus on statistical context, see the book by Banerjee and Roy (2014, ISBN:9781420095388). 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'MarkerPen' is a semi-supervised algorithm for detecting marker genes by combining prior marker information with bulk transcriptome data. Package: r-cran-markets Architecture: arm64 Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2096 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_arm64.deb Size: 1133934 MD5sum: b80c4b71a91a7e6e5c7c4150990e90ee SHA1: 206c9c5134b1d31d3022264692b7fb7a0428e148 SHA256: de5080082bb92687833f15170872aa5016a94b3f8dd82c2ee6e9352355f50ad2 SHA512: 6f04523c5a8ac40d6b2582f15c8ed4bb63234be01971452ce0770e95b4f935dc36b9fb3d616856558a1119f6baee710e565b1bb25e7572d7a9334af6d0822c1f Homepage: https://cran.r-project.org/package=markets Description: CRAN Package 'markets' (Estimation Methods for Markets in Equilibrium and Disequilibrium) Provides estimation methods for markets in equilibrium and disequilibrium. Supports the estimation of an equilibrium and four disequilibrium models with both correlated and independent shocks. Also provides post-estimation analysis tools, such as aggregation, marginal effect, and shortage calculations. See Karapanagiotis (2024) for an overview of the functionality and examples. The estimation methods are based on full information maximum likelihood techniques given in Maddala and Nelson (1974) . They are implemented using the analytic derivative expressions calculated in Karapanagiotis (2020) . Standard errors can be estimated by adjusting for heteroscedasticity or clustering. The equilibrium estimation constitutes a case of a system of linear, simultaneous equations. Instead, the disequilibrium models replace the market-clearing condition with a non-linear, short-side rule and allow for different specifications of price dynamics. Package: r-cran-markophylo Architecture: arm64 Version: 1.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 548 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ape, r-cran-numderiv, r-cran-phangorn, r-cran-geiger, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-testthat, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-markophylo_1.0.9-1.ca2604.1_arm64.deb Size: 259790 MD5sum: aecff7a2eef79c03c6e280ae0fdb168b SHA1: 5f5503767941b1c3b59a35befd169439e2a0090f SHA256: cfa9002f47df48726ff3f0002c14ea5a895a3baa7b7999557c8fab47b8207511 SHA512: 58989655dbf78c1c4223a23110a45965265d212f50772b00bf81ef62539785edbea305e6907a902c4a433eafea7c62e00a4c88ee24965f20532f5526dd55de12 Homepage: https://cran.r-project.org/package=markophylo Description: CRAN Package 'markophylo' (Markov Chain Models for Phylogenetic Trees) Allows for fitting of maximum likelihood models using Markov chains on phylogenetic trees for analysis of discrete character data. Examples of such discrete character data include restriction sites, gene family presence/absence, intron presence/absence, and gene family size data. Hypothesis-driven user- specified substitution rate matrices can be estimated. Allows for biologically realistic models combining constrained substitution rate matrices, site rate variation, site partitioning, branch-specific rates, allowing for non-stationary prior root probabilities, correcting for sampling bias, etc. See Dang and Golding (2016) for more details. Package: r-cran-markovchain Architecture: arm64 Version: 0.10.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2282 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-igraph, r-cran-expm, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-testthat, r-cran-diagram, r-cran-diagrammer, r-cran-msm, r-cran-rsolnp, r-cran-rmarkdown, r-cran-ctmcd, r-cran-bookdown, r-cran-rticles, r-cran-mcmcpack, r-cran-microbenchmark Filename: pool/dists/resolute/main/r-cran-markovchain_0.10.3-1.ca2604.1_arm64.deb Size: 1265580 MD5sum: 0ee9573b9ebdd06e63168e073beac846 SHA1: 1302b31682d3307c279bb970e8be8c5e1c59e4b4 SHA256: a1d5c0c0bb5b4c932422d9fe82ce4d012d47f9945d06e3188add40d1a8e0d135 SHA512: b3f520c5acf78376dc06d5c834edf71762f84686f292a77c72b0ed9fbbec1a01c1555cdaf5ebb1df11e23393d3f08d123f0d3298bf2171b89756ff8d8d7f373d Homepage: https://cran.r-project.org/package=markovchain Description: CRAN Package 'markovchain' (Easy Handling Discrete Time Markov Chains) Functions and S4 methods to create and manage discrete time Markov chains more easily. In addition functions to perform statistical (fitting and drawing random variates) and probabilistic (analysis of their structural proprieties) analysis are provided. See Spedicato (2017) . Some functions for continuous times Markov chains depend on the suggested ctmcd package. Package: r-cran-markovmix Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 257 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 109248 MD5sum: 14aba1810c0967b7040b5e5eaec0b716 SHA1: 50166fa1dd4f2ed7f085305d9d3d3f769f46b1b7 SHA256: b2547f431c1b61e62160b406413236f9a4604f976b91fba88402d2b0c81462d0 SHA512: 8c231fae61da1872312206011f2deeb4e15d9b1f27b3436e5c4d8f6442bda32ccbdb47e5ae30d6b37ef8918c8bff08799fa33a43f04fd49833bde4a820747f78 Homepage: https://cran.r-project.org/package=markovmix Description: CRAN Package 'markovmix' (Mixture of Markov Chains with Support of Higher Orders andMultiple Sequences) Fit mixture of Markov chains of higher orders from multiple sequences. It is also compatible with ordinary 1-component, 1-order or single-sequence Markov chains. Various utility functions are provided to derive transition patterns, transition probabilities per component and component priors. In addition, print(), predict() and component extracting/replacing methods are also defined as a convention of mixture models. Package: r-cran-markovmsm Architecture: arm64 Version: 0.1.3-1.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_arm64.deb Size: 187268 MD5sum: ebd1716b6e916691cbbf09e468d6a1f7 SHA1: 75470816380e32e84c6e9ae0a0868d3dac4dc027 SHA256: 8eb2c81b7c1c4da93beafc62bce96bb7028f95114fb98f532a748d7243500b30 SHA512: 7fc3308d3534d9237bfa835a8bfc3a8990b146f9bc4f45e7eea3cf0e382be43fbcfde5900b916bf7adc2f8b09b0cb8411eedbe7a0ad844c8c6260b6564c92dca 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: arm64 Version: 2.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 819 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_arm64.deb Size: 198684 MD5sum: f4f0519ff76f0bcc47fa8846053ac07b SHA1: 52af0469bb142ef16db1675db92dcf3ea0b07229 SHA256: 4ea9e51fd727fb8991a587c56fb4488152796b796245866fc8f308b515aaf44b SHA512: 14dee22c79599eccf3a4eb175078bf9d7e405dbfb1575b04940286d109326aefc2c29ceff91321209f7ffaad0447a390c571aeb168eeab857682fe09d38795a8 Homepage: https://cran.r-project.org/package=marqLevAlg Description: CRAN Package 'marqLevAlg' (A Parallelized General-Purpose Optimization Based onMarquardt-Levenberg Algorithm) This algorithm provides a numerical solution to the problem of unconstrained local minimization (or maximization). It is particularly suited for complex problems and more efficient than the Gauss-Newton-like algorithm when starting from points very far from the final minimum (or maximum). Each iteration is parallelized and convergence relies on a stringent stopping criterion based on the first and second derivatives. See Philipps et al, 2021 . 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Estimates of Natural Indirect Effect (NIE), Natural Direct Effect (NDE) of each taxon, as well as their standard errors and confident intervals, were provided as outputs. Zeros will not be imputed during analysis. See Wu et al. (2022) . Package: r-cran-mas Architecture: arm64 Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 797 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 435526 MD5sum: 662ed11761715bee8abb5de4f69f689d SHA1: 3c6013db02f38b5da066407fa4c78e665fb10116 SHA256: 64fed6552af7c8685a847d41ede22071de48b02302039f47d1cb0796ca8190a0 SHA512: ff879d9a618d967493812071b8248cd1ee9d324d82b4123815cd161da1c1522e381aa479ad1395e713cb01e9137a5926a757d6c0e4f7d30c006048b348499046 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: arm64 Version: 0.2.79-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1270 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_arm64.deb Size: 596998 MD5sum: ff98a9025072fe97840b445501703d66 SHA1: 99844f6d9641025de841d10d148721e49a521800 SHA256: 33b9ddbbb15e6fa9544a41a9c09ac0131912cdf6c31b32d69d08819bee5f9fc3 SHA512: ce7fffe504dff9bda5feb937a7b46f5b9b7141d32b2e67ee37f9f461ac5c2820536ca61d1ba8f7bb20fdb4a35e414f4ee36c68977059de59d853f6c22cba2521 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: arm64 Version: 7.3-65-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1427 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1106996 MD5sum: 572d6129394c1e7ace9afcd6eec6dec5 SHA1: be1ffa3de9bbdfc351d4e41a7c184166b456de40 SHA256: 209c9c6db8bf948942e40586f15d9813c55b526c4e0aa557e927e6c341130a38 SHA512: 03d0aa0ae7207524001c74c3d9f288c21ed74e7776d7aa62a2b1bccb32c8f0ce343ac34b0383597510753a5f7ead51678115c3470a274231041627b3f486dec2 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). Package: r-cran-masterbayes Architecture: arm64 Version: 2.59-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1834 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-genetics, r-cran-gtools, r-cran-kinship2 Filename: pool/dists/resolute/main/r-cran-masterbayes_2.59-1.ca2604.1_arm64.deb Size: 1558466 MD5sum: 96c73aa1f67a3d0258ad22f240d107e9 SHA1: 54ea77d79940e817b91e71623cde2cee7938cfeb SHA256: c9117ef6820f67804dc87bff8f3be63daf5d916b97b5f958caddfb85b64f1ac7 SHA512: 03349c9c2580f9fc3c0438a6d674d5722a459a041f82e45dfd27dcd51f40a3b445aaa6945d832d90c8066e3601fbaac166875540ef5791a388b60322e983aa0f Homepage: https://cran.r-project.org/package=MasterBayes Description: CRAN Package 'MasterBayes' (Maximum Likelihood and Markov Chain Monte Carlo (MCMC) Methodsfor Pedigree Reconstruction and Analysis) The primary aim of 'MasterBayes' is to use Markov chain Monte Carlo (MCMC) techniques to integrate over uncertainty in pedigree configurations estimated from molecular markers and phenotypic data (Hadfield et al. (2006) ). Emphasis is put on the marginal distribution of parameters that relate the phenotypic data to the pedigree. All simulation is done in compiled 'C++' for efficiency. Package: r-cran-mastif Architecture: arm64 Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1606 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-rann, r-cran-cluster, r-cran-corrplot, r-cran-xtable, r-cran-repmis, r-cran-robustbase, r-cran-stringi, r-cran-stringr, r-cran-fullrankmatrix, r-cran-truncatednormal, r-cran-tibble, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mastif_2.4-1.ca2604.1_arm64.deb Size: 1228492 MD5sum: 8711b07002e04583cf2552a9b6da6f69 SHA1: 49e769f8a73a06b8191ea31781223dd36ccdf11f SHA256: a241d456d781d080ce75dacdb444d342df607f889a333e14bc29764e1c484a05 SHA512: a3fe027d73e4be4e1f194697a562a9cc9f3c41ba3e6c77ffa3276ec916053cc4eff207d01fa9161175fe515e046370643326987df3d31f55c18ca0c82ff993a7 Homepage: https://cran.r-project.org/package=mastif Description: CRAN Package 'mastif' (Mast Inference and Forecasting) Analyzes production and dispersal of seeds dispersed from trees and recovered in seed traps. Motivated by long-term inventory plots where seed collections are used to infer seed production by each individual plant. Package: r-cran-mat Architecture: arm64 Version: 2.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 407 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-mat_2.3.2-1.ca2604.1_arm64.deb Size: 148736 MD5sum: 0bb19a9446b3339311a45b67a0d23e87 SHA1: 7368021133d5ddd20c791dd0cc8e66ef62425f93 SHA256: 9395d226159e303d02036aea610475b0800dc7ffb66ae377114e844fc81b8a10 SHA512: 84c09053ad802f0fff02bc5f39a44e9cc961044a12c246b054edd2f447f409c205c9c1a0dd2d198ca1721a4bb7aed66242ac3d67b1486a02f672358d35bed5ed Homepage: https://cran.r-project.org/package=MAT Description: CRAN Package 'MAT' (Multidimensional Adaptive Testing) Simulates Multidimensional Adaptive Testing using the multidimensional three-parameter logistic model as described in Segall (1996) , van der Linden (1999) , Reckase (2009) , and Mulder & van der Linden (2009) . Package: r-cran-matching Architecture: arm64 Version: 4.10-15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 770 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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_arm64.deb Size: 477790 MD5sum: d9da83f75098bc18069823855d300fe9 SHA1: eb4de84ba4159bca2906ef03ded9029c154777ce SHA256: eedb00f73950d0a3a986528a70978544f7dc2a058c6ff386778b426ec7beb83b SHA512: 110588429101ecd7b11bb372c7f8b3157e19238d1ea1503ca75df7fe4635e7c58f6dafe456cf1dc24ba1c8c1d01bc3d63283615d9ff4d98eac6dcf48e020754f 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. A variety of univariate and multivariate metrics to determine if balance has been obtained are also provided. For details, see the paper by Jasjeet Sekhon (2007, ). Package: r-cran-matchingmarkets Architecture: arm64 Version: 1.0-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5203 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-rcppprogress, r-cran-lpsolve, r-cran-lattice, r-cran-partitions, r-cran-rjava, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-matchingmarkets_1.0-5-1.ca2604.1_arm64.deb Size: 4074468 MD5sum: e3700745a267f8241b7c326793acce83 SHA1: 7e16d0b31e42b8c45bcfc7e15ec8674950b373a7 SHA256: 54fa4ccce3beb7bc0e72e37232e9f2a1c12a45f8426de89b696de36a7372d0ca SHA512: 469301fec6c021bf44799246b0405329e926d69226ec07ef382956818dd8ab1b71c6b27c3f993f9dfb0e8108af82774950b18a8d66e7acf6691d20a030acf2dc Homepage: https://cran.r-project.org/package=matchingMarkets Description: CRAN Package 'matchingMarkets' (Analysis of Stable Matchings) Implements structural estimators to estimate preferences and correct for the sample selection bias of observed outcomes in matching markets. This includes one-sided matching of agents into groups (Klein, 2015) as well as two-sided matching of students to schools (Klein et al., 2024) . The package also contains algorithms to find stable matchings in the three most common matching problems: the stable roommates problem (Irving, 1985) , the college admissions problem (Gale and Shapley, 1962) , and the house allocation problem (Shapley and Scarf, 1974) . Package: r-cran-matchingr Architecture: arm64 Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 384 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 161842 MD5sum: 4097844dc661e094752b4163fe004dec SHA1: 6aec30da2c992b1270ccdb1f37e73264bc4d5d7a SHA256: 695ada2d52005bed2769a181572bec588e93c5abb2609015d2b21cfa0311909b SHA512: 99f4457747a926a39bbaa35a5e46a9bc1ae43320a658f8993ea9b0a5e9d36ba8f3ce38ee5449db2af685eb777fcceb8524e7cb8a5d4b0aba776345a09d47b74f Homepage: https://cran.r-project.org/package=matchingR Description: CRAN Package 'matchingR' (Matching Algorithms in R and C++) Computes matching algorithms quickly using Rcpp. Implements the Gale-Shapley Algorithm to compute the stable matching for two-sided markets, such as the stable marriage problem and the college-admissions problem. Implements Irving's Algorithm for the stable roommate problem. Implements the top trading cycle algorithm for the indivisible goods trading problem. Package: r-cran-matchit Architecture: arm64 Version: 4.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3036 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1793274 MD5sum: 3cfe946d1fdc86620996e1185ff670cc SHA1: 4e5838d9d6dd407e4ec583609fc6708207a0a6df SHA256: 1311338881ac42f1ccce54c43bb28cb5412aff6aba029f6825f910713bc439ab SHA512: 60ea262039c05b4af50dad2509cd5b3d172bf8fad99d6ebcf809bd36fa57252be8f25d15f3558e0b0b923b432f43f429eca70e34f4c46f9f0df766b8d07eeb26 Homepage: https://cran.r-project.org/package=MatchIt Description: CRAN Package 'MatchIt' (Nonparametric Preprocessing for Parametric Causal Inference) Selects matched samples of the original treated and control groups with similar covariate distributions -- can be used to match exactly on covariates, to match on propensity scores, or perform a variety of other matching procedures. The package also implements a series of recommendations offered in Ho, Imai, King, and Stuart (2007) . (The 'gurobi' package, which is not on CRAN, is optional and comes with an installation of the Gurobi Optimizer, available at .) 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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. 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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: arm64 Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 910 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 512298 MD5sum: 9b46bc419dda03bd89c2ee417cb09bb6 SHA1: d740019fed340624aaeabaf4d67670c58c927400 SHA256: 1701b87ba12363c3439c5ca24d52f7be14ae5efa29303a4314bb3b5a938297b1 SHA512: 4be1c6cbe936f88019099b4f74311fd16e896e391ecb73523228810c3be3e84e588fac674a32934c4de83254ae5f01c14d04b75d2cef98d9745f262a89be8ea6 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: arm64 Version: 1.7-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7684 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_arm64.deb Size: 4182404 MD5sum: eb335a9f2163c240b819f413b5365f2e SHA1: 061af64daebd9bf59217e4b4dffbc6496388c7a6 SHA256: 1b39ff6d624c3af626db70b4bc4d2064fccff22a169c0d2f33ba0a9008b3db09 SHA512: 0a20f2f018fe36c8f5242f74261ec3812b2fe2dde4d949ce1ffe051076259d3b89a5c00fed01571bd1a057025ca603284d3b1e352294268e27214790f5d0fe1c 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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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: arm64 Version: 0.10.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 136312 MD5sum: 4557f995f75b98aec23c607953911c88 SHA1: 27c08cb811279cdf725f8447d5639e40359807b9 SHA256: c85f043e714c8bcd947018a1c52e8d13874441ecd26644c9031cc340098c8a96 SHA512: 1223635e7f4a965184264854c338c833ee098a0ac8ab1205172a8a22be528990d3fae04a1ff67ee9ac68959dbc5bf7086e07dc7b0fb898822f4e695776546121 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: arm64 Version: 1.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2131 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1073334 MD5sum: 65bca0d6537e4b5d52d81e8979efb982 SHA1: 78f0bf463b5f010487b8649688799be81ca03016 SHA256: 704f3f4055da32ead80e5086fad9f2eb47724f6da389c7b77275d5053584935f SHA512: c283ea91ba41a5c9ba9b9320677e2860065aa9fc8cdec1919b52553a4f02e6e5243b38c47ef6e6874738a0d5583ae9dfa3814c79626a8ca03376e62a39678848 Homepage: https://cran.r-project.org/package=matrixdist Description: CRAN Package 'matrixdist' (Statistics for Matrix Distributions) Tools for phase-type distributions including the following variants: continuous, discrete, multivariate, in-homogeneous, right-censored, and regression. Methods for functional evaluation, simulation and estimation using the expectation-maximization (EM) algorithm are provided for all models. The methods of this package are based on the following references. Asmussen, S., Nerman, O., & Olsson, M. (1996). Fitting phase-type distributions via the EM algorithm, Olsson, M. (1996). Estimation of phase-type distributions from censored data, Albrecher, H., & Bladt, M. (2019) , Albrecher, H., Bladt, M., & Yslas, J. (2022) , Albrecher, H., Bladt, M., Bladt, M., & Yslas, J. (2022) , Bladt, M., & Yslas, J. (2022) , Bladt, M. (2022) , Bladt, M. (2023) , Albrecher, H., Bladt, M., & Mueller, A. (2023) , Bladt, M. & Yslas, J. (2023) . Package: r-cran-matrixextra Architecture: arm64 Version: 0.1.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2419 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 4.2), 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_arm64.deb Size: 1195404 MD5sum: 9d0177cab06d24d395479d689803bae0 SHA1: 3fa5eec730d2e6f5cbb9c944b69ad6ac061e76d4 SHA256: 79758d536ce6c2ed2bb5e8ba531b9b9c73e8ca9c233b051262630ec73ff35e9e SHA512: e81fd786bd39a7957a6d9fa01da0e7ed5b857470f367baf8d0bddc77498d6464e42443bd683abd94e2a82f2b422d1fe602c061f137e6bebd50b4d95044ccf928 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: arm64 Version: 0.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 938 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_arm64.deb Size: 392768 MD5sum: 47419ca8c7710bfef5242b6268c32ebe SHA1: d7c54eccbcb99e2d1f8954e11f51ab6d9bd874c8 SHA256: 6fc19003efcd0efd1b98ee8e959e62e3d4f46e6bfbb5a98ce766437328d75247 SHA512: 929afa90ff4b5d71be1efd037fedbf2ca123c80c0f9876bc054e2bcff0872c8dbd92d42a65ecb6d1a588469e2199fa87f55970583ce1ac62b63c3b235ec10d7e 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. The low level and carefully checked mathematical functions are here. These are implementations of the Matrix Profile concept that was created by CS-UCR . 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Package: r-cran-matrixstats Architecture: arm64 Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 899 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 445906 MD5sum: a50c78294881a8f0cacd78220d07a6b1 SHA1: f443332e2b546294d58a865a5dcb5c909fbc9852 SHA256: f8d28204f7b552f74174762cfb7e2ce81f652ad77a0b79c699c7856628b8e5b6 SHA512: c32abb1645cb1ba149185a3cad31ff74870846f0d5e900e20eac4ec3004b59af27539199d75575fe0601ce15b02369415f74b978f9e4e979e31b4089ff78f13e Homepage: https://cran.r-project.org/package=matrixStats Description: CRAN Package 'matrixStats' (Functions that Apply to Rows and Columns of Matrices (and toVectors)) High-performing functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. There are also optimized vector-based methods, e.g. binMeans(), madDiff() and weightedMedian(). Package: r-cran-mattransmix Architecture: arm64 Version: 0.1.18-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 597 Depends: 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-mattransmix_0.1.18-1.ca2604.1_arm64.deb Size: 432558 MD5sum: 4e8362c2d81b9a8c96929db3bdaa6f00 SHA1: 7560d9054390d18d0cb4d7726aabb5ade9c3b025 SHA256: 801304006d41567e66f1991891a5d322d73b383cf408353eda7828aa2e4500d3 SHA512: 24f14e3af0d6f8fef2019c42983469bb0145a1537ba6e3fa67661c129631f8a4a5a9b06eebb9114f0e4a44bb83ad8645e61caf6a57db68afaa9949cb49ebc353 Homepage: https://cran.r-project.org/package=MatTransMix Description: CRAN Package 'MatTransMix' (Clustering with Matrix Gaussian and Matrix TransformationMixture Models) Provides matrix Gaussian mixture models, matrix transformation mixture models and their model-based clustering results. The parsimonious models of the mean matrices and variance covariance matrices are implemented with a total of 196 variations. For more information, please check: Xuwen Zhu, Shuchismita Sarkar, and Volodymyr Melnykov (2021), "MatTransMix: an R package for matrix model-based clustering and parsimonious mixture modeling", . Package: r-cran-mave Architecture: arm64 Version: 1.3.12-1.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), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mda, r-cran-rcpparmadillo Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-mave_1.3.12-1.ca2604.1_arm64.deb Size: 1185536 MD5sum: bf9dc6bd16a1124aa3690e1430cadc76 SHA1: be80c8adf336b67e8caecb27252784a957e28035 SHA256: 80afc4ef555ad2b33f411d8bb3dcef23ddd5d62afdd958686834bdb59a1df698 SHA512: 4d510508d2ddb77f3d3b57894d34f85d43189abac38223372a85cc799f2830ae2139a885da9f5d92774945f51fa2c064136310b8532f59789618f544866911ed Homepage: https://cran.r-project.org/package=MAVE Description: CRAN Package 'MAVE' (Methods for Dimension Reduction) Functions for dimension reduction, using MAVE (Minimum Average Variance Estimation), OPG (Outer Product of Gradient) and KSIR (sliced inverse regression of kernel version). 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Package: r-cran-maxpro Architecture: arm64 Version: 4.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 149 Depends: libc6 (>= 2.29), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nloptr Filename: pool/dists/resolute/main/r-cran-maxpro_4.1-2-1.ca2604.1_arm64.deb Size: 63986 MD5sum: e6e300d4e0e7c32c3b33e4e94e7d3fdd SHA1: bbaaf7b78f526f8676e9970ab0327884f49fa9b7 SHA256: 3d9b84f07ad187d7da0fd03d51228edff7c346fd58013033330f38d5aa10a642 SHA512: fc7b62d98a21dbec4747029d037c8045d4fa124dea6c912852facd044dc549512d3c50e300e0a282cd315293b19add732c3764f944d0b70a1687001030e610d9 Homepage: https://cran.r-project.org/package=MaxPro Description: CRAN Package 'MaxPro' (Maximum Projection Designs) Generate maximum projection (MaxPro) designs for quantitative and/or qualitative factors. Details of the MaxPro criterion can be found in: (1) Joseph, Gul, and Ba. 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The vignette shows code snippets to fit the distribution to empirical data. See, e.g., Bernegger (1997) freely available on-line. 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Package: r-cran-mclustcomp Architecture: arm64 Version: 0.3.5-1.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-rdpack, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-mclustcomp_0.3.5-1.ca2604.1_arm64.deb Size: 87416 MD5sum: 92663bbf87cb5aac9afa3123c7db09bb SHA1: 403ea9eaa822603c5e45072e03cbec8360651c9c SHA256: e4847e5531d8ad933035c3488c8c13f30e837aa817bf67bfd3f207a6496ae42f SHA512: 01348edb482133165a8bca143a651e18427d1c2e6047317959905d003858b36ab47c3f8d5a0465920891e1d472d3983439015f246973dd937da5f92c9014d6c8 Homepage: https://cran.r-project.org/package=mclustcomp Description: CRAN Package 'mclustcomp' (Measures for Comparing Clusters) Given a set of data points, a clustering is defined as a disjoint partition where each pair of sets in a partition has no overlapping elements. This package provides 25 methods that play a role somewhat similar to distance or metric that measures similarity of two clusterings - or partitions. For a more detailed description, see Meila, M. (2005) . Package: r-cran-mcmc Architecture: arm64 Version: 0.9-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1723 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-xtable, r-cran-iso Filename: pool/dists/resolute/main/r-cran-mcmc_0.9-8-1.ca2604.1_arm64.deb Size: 1226112 MD5sum: e21a0bb4887d6d07b5d554c380a69448 SHA1: 7d84fc7421f27a8856676269e4f4faf327de01c2 SHA256: 7642b81ea18b428ecdef2903a5ff5e67c152b66ad83ffe10f25ca66e65ef631a SHA512: 6cdf59a9995040bba927ce37737e4b24e3ce8c088ca814e003296d4cfca84f71b21c35ce97d161bc9ef7984fd76cd829c3cd905abb94972969b6d755f134dc35 Homepage: https://cran.r-project.org/package=mcmc Description: CRAN Package 'mcmc' (Markov Chain Monte Carlo) Simulates continuous distributions of random vectors using Markov chain Monte Carlo (MCMC). Users specify the distribution by an R function that evaluates the log unnormalized density. Algorithms are random walk Metropolis algorithm (function metrop), simulated tempering (function temper), and morphometric random walk Metropolis (Johnson and Geyer, 2012, , function morph.metrop), which achieves geometric ergodicity by change of variable. 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Stat. Soft.). Package: r-cran-mcmcpack Architecture: arm64 Version: 1.7-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3386 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-mass, r-cran-lattice, r-cran-mcmc, r-cran-quantreg Filename: pool/dists/resolute/main/r-cran-mcmcpack_1.7-1-1.ca2604.1_arm64.deb Size: 1925862 MD5sum: e6e6987c61ca95316f69855712338c03 SHA1: 2ff3eb4755d1a56c2fe63657ad494130c06a942f SHA256: 9aff0b4d50a8fe959044ea3a04f04523f2e5352596db3b9c997ea405349097e7 SHA512: f97f22e259f0b846f93f2ce134f1ac7f00f0c5117431a02a5bb22a17b97ca8ea58fd1e2ed24d5ce56a64b847f713bcc03f8f995fd43bcb5bc59b34d776b63e10 Homepage: https://cran.r-project.org/package=MCMCpack Description: CRAN Package 'MCMCpack' (Markov Chain Monte Carlo (MCMC) Package) Contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return 'coda' mcmc objects that can then be summarized using the 'coda' package. Some useful utility functions such as density functions, pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization are provided. Package: r-cran-mcmcprecision Architecture: arm64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1053 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-matrix, r-cran-combinat, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-mcmcprecision_0.4.2-1.ca2604.1_arm64.deb Size: 610212 MD5sum: 983d3df2db60cb627b0c0ec76d64c74b SHA1: 8449c68bdf0d95ffdcc8dc5162e084a87edcb4a9 SHA256: 66f0993e976b05f156d70aa47cb0da1e6138dfa9faee4120b859ba61c35da5d5 SHA512: a71f1ed58ccfb202262686e0eadf74fe994a4594cca86206a128ffb0f0bdb87c6ba59158848ab1249110179f99920c9f1799e906f6cb7886a6bed52f468f826e Homepage: https://cran.r-project.org/package=MCMCprecision Description: CRAN Package 'MCMCprecision' (Precision of Discrete Parameters in Transdimensional MCMC) Estimates the precision of transdimensional Markov chain Monte Carlo (MCMC) output, which is often used for Bayesian analysis of models with different dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible jump MCMC) relies on sampling a discrete model-indicator variable to estimate the posterior model probabilities. If only few switches occur between the models, precision may be low and assessment based on the assumption of independent samples misleading. Based on the observed transition matrix of the indicator variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2019, Statistics & Computing, 29, 631-643) draws posterior samples of the stationary distribution to (a) assess the uncertainty in the estimated posterior model probabilities and (b) estimate the effective sample size of the MCMC output. Package: r-cran-mcmcsae Architecture: arm64 Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2240 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_arm64.deb Size: 1559320 MD5sum: e39596dea05e563377e11e29414070b3 SHA1: ce873bf213251f209638b527c06380e79085a0d5 SHA256: 864cf59df846e7cae0c828f1cce311571f527438614ca9423bc9ce6c74a1e315 SHA512: 3c38a874403226fdbc1e5aba1604e1f8545c3ea0fb832f9bc69f53c72e3b19545cb4df75494edec44fe0ffa853535567926781eb48d9a6c39d4f009dc8b58059 Homepage: https://cran.r-project.org/package=mcmcsae Description: CRAN Package 'mcmcsae' (Markov Chain Monte Carlo Small Area Estimation) Fit multi-level models with possibly correlated random effects using Markov Chain Monte Carlo simulation. 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Package: r-cran-mcmcse Architecture: arm64 Version: 1.5-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 724 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 436784 MD5sum: 55e3d20631bba6531010894419ca55ad SHA1: 344c0d1041f3ae0fbebca84e201d4a741015a7b5 SHA256: 5fb170df7c2e46671089ada111aaa1b899087af677039ec2cf148249126048b4 SHA512: 98e12c32eec6d8595d2e997f1ee32c6e38c0d603beed70dddf84d46a891b794dafd70b8ef64ede30a0c89bd12f2c61417bfb57ad622efaa2f1e11866f987b6ab 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: arm64 Version: 1.17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 153 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-scatterplot3d, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mco_1.17-1.ca2604.1_arm64.deb Size: 66162 MD5sum: deecd7e8e15b4434bf3134271e015d04 SHA1: 3e64b1dff5ea7ca6721c97a65ef76dfaa0dc6035 SHA256: d88b8e3232bcba59ff8f354ee59c75f155e5545512c9f8f10a4268ba98673188 SHA512: 7f91b2d4cbeca3077ce15a453042e68c945320c98a6aa10c339d3c9e9eb14ab70b424cbf2d154b9eefa6cec3bfcca373647e84478e68b5e77073e62d569cdfef 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: arm64 Version: 1.3.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1004 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 608240 MD5sum: a436a04d698c994619d01b984d686ecb SHA1: 6d07472cf8e8a4a05c013f9af6a6918e1499e5c1 SHA256: 8aa2633abce6318a3507748a1c7731abe55b11755a90dd2ba1e5ad21f903f3f9 SHA512: a9608cceb7470f02b90f8a65719505708d1b810071296f22ec9b176199171a365088e9b5948131248fb41c82c8fb1c4b1da6c95db354268c3cb6e4a43f3c06b6 Homepage: https://cran.r-project.org/package=mcr Description: CRAN Package 'mcr' (Method Comparison Regression) Regression methods to quantify the relation between two measurement methods are provided by this package. In particular it addresses regression problems with errors in both variables and without repeated measurements. It implements the CLSI recommendations (see J. A. Budd et al. (2018, ) for analytical method comparison and bias estimation using patient samples. Furthermore, algorithms for Theil-Sen and equivariant Passing-Bablok estimators are implemented, see F. Dufey (2020, ) and J. Raymaekers and F. Dufey (2022, ). A comprehensive overview over the implemented methods and references can be found in the manual pages "mcr-package" and "mcreg". Package: r-cran-mcrpioda Architecture: arm64 Version: 1.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1066 Depends: libc6 (>= 2.17), libgfortran5 (>= 10), libgsl28 (>= 2.8+dfsg), r-base-core (>= 4.5.0), r-api-4.0, r-cran-robslopes, r-cran-rrcov, r-cran-mixtools Filename: pool/dists/resolute/main/r-cran-mcrpioda_1.3.4-1.ca2604.1_arm64.deb Size: 658244 MD5sum: e8c6fe6586dc62df0551753782ff6f4a SHA1: 1415e85ab702dbc1ecaa98e8c798d353349eec8e SHA256: 5e7a56b0e42fade883d0e5fb0c18301fefd5da5976e3282ba6eb7de6f2f5c9aa SHA512: 75cea484cf2d57af7ffaea59cdce89c684d474d528df6fc303a921156b8c50901b24c2c8c1731e169b2249f848f604ba96bfb27dd9dc0ea69d8c66102df0fb42 Homepage: https://cran.r-project.org/package=mcrPioda Description: CRAN Package 'mcrPioda' (Method Comparison Regression - Mcr Fork for M- And MM-DemingRegression) Regression methods to quantify the relation between two measurement methods are provided by this package. In particular it addresses regression problems with errors in both variables and without repeated measurements. It implements the Clinical Laboratory Standard International (CLSI) recommendations (see J. A. Budd et al. (2018, ) for analytical method comparison and bias estimation using patient samples. Furthermore, algorithms for Theil-Sen and equivariant Passing-Bablok estimators are implemented, see F. Dufey (2020, ) and J. Raymaekers and F. Dufey (2022, ). Further the robust M-Deming and MM-Deming (experimental) are available, see G. Pioda (2021, ). A comprehensive overview over the implemented methods and references can be found in the manual pages 'mcrPioda-package' and 'mcreg'. 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This package allows one to perform simulations for ODE models that are encoded in the GNU 'MCSim' model specification language (Bois, 2009) using ODE solvers from the 'R' package 'deSolve' (Soetaert et al., 2010) . Package: r-cran-md2sample Architecture: arm64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1289 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-fnn, r-cran-copula, r-cran-ade4, r-cran-gtests, r-cran-igraph, r-cran-lsa, r-cran-microbenchmark, r-cran-mvtnorm, r-cran-ball Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-ggplot2, r-cran-egg, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-md2sample_1.2.1-1.ca2604.1_arm64.deb Size: 727790 MD5sum: 634a859b5d56dcdbee48fc7c4516a831 SHA1: 753871fba508e151eb0f931499d9ca3172e1d30d SHA256: 85e964cd478e2a4493826b5f4bd098e34ab7386f64fa711e57c715e042496f39 SHA512: 47e8c77b5be43796d13871238f50f7d25484860b68b29efb89f1c6bf4d3d319d6c1cfe872373e84edd334c85c61603f13dd2c416bfd63a94f7903c39e9a7ea34 Homepage: https://cran.r-project.org/package=MD2sample Description: CRAN Package 'MD2sample' (Various Methods for the Two Sample Problem in D>1 Dimensions) The routine twosample_test() in this package runs the two-sample test using various test statistic for multivariate data. The user can also run several tests and then find a p value adjusted for simultaneous inference. The p values are found via permutation or via the parametric bootstrap. The routine twosample_power() allows the estimation of the power of the tests. The routine run.studies() allows a user to quickly study the power of a new method and how it compares to those included in the package. For details of the methods and references see the included vignettes. 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Hastie, Tibshirani and Friedman (2009) "Elements of Statistical Learning (second edition, chap 12)" Springer, New York. 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The methods are based on Shao and Zhang (2014) . Additionally, introduces a novel hypothesis test for evaluating covariate effects on the cure rate in mixture cure models, using MDC-based statistics. The methodology is described in Monroy-Castillo et al. (2025, manuscript submitted). 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Package: r-cran-mdendro Architecture: arm64 Version: 2.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1949 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_arm64.deb Size: 820124 MD5sum: 9f59201835dfa2c722883e19b355acf7 SHA1: 417ab52fe718a9a55c0fc5ebaea83f267e0d5085 SHA256: 88dcb2d27469d05338dec53e8f2b5f256252ce71ef5e4e2bf2811f88ee58990e SHA512: b6f68b6d261321c352ac81921009fd075e6880cb0b925c0d761cdf7ddf59a4dd5e54b1d366b7e2dc15b857539f0cfc573efffc78c4df0d737efad05171e33117 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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This package is a spinoff from 'epiworldR' focusing specifically on measles transmission dynamics. It includes models for school settings with quarantine and isolation policies, mixing models with population groups, and risk-based quarantine strategies. The models use Agent-Based Models (ABM) with a fast 'C++' backend from the 'epiworld' library. Ideal for studying measles outbreaks, vaccination strategies, and intervention policies. Package: r-cran-measr Architecture: arm64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5079 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-cli, r-cran-dcm2, r-cran-dcmstan, r-cran-dplyr, r-cran-dtplyr, r-cran-fs, r-cran-glue, r-cran-lifecycle, r-cran-loo, r-cran-posterior, r-cran-psych, r-cran-rcpp, r-cran-rdcmchecks, r-cran-rlang, r-cran-rstan, r-cran-rstantools, r-cran-s7, r-cran-tibble, r-cran-tidyr, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-dcmdata, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-spelling, r-cran-testthat, r-cran-withr Filename: pool/dists/resolute/main/r-cran-measr_2.0.1-1.ca2604.1_arm64.deb Size: 1884578 MD5sum: b12fb423675d7f8cbd0d55c6a94ae0ff SHA1: f201528d18dae5204e4e6379f8414d8ae00caa0e SHA256: dbcbf6ad7f5c6555d5c04804411877dfe6977f81247d65ab210736832664d035 SHA512: 0781d66f455065459c489f49d956e865dff223ab9144789288b75e4f4b3bd8ae18b8bf2acd48c4016e4259065812ff3ac239b654ae03295dbc21a4e36e6e5fb5 Homepage: https://cran.r-project.org/package=measr Description: CRAN Package 'measr' (Bayesian Psychometric Measurement Using 'Stan') Estimate diagnostic classification models (also called cognitive diagnostic models) with 'Stan'. Diagnostic classification models are confirmatory latent class models, as described by Rupp et al. (2010, ISBN: 978-1-60623-527-0). Automatically generate 'Stan' code for the general loglinear cognitive diagnostic diagnostic model proposed by Henson et al. (2009) and other subtypes that introduce additional model constraints. Using the generated 'Stan' code, estimate the model evaluate the model's performance using model fit indices, information criteria, and reliability metrics. 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Song W.-M., Zhang B. (2015) Multiscale Embedded Gene Co-expression Network Analysis. PLoS Comput Biol 11(11): e1004574. . 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Summarized data on genetic associations with the exposure and with the outcome can be obtained from large consortia. These data can be used for obtaining causal estimates using instrumental variable methods. Package: r-cran-mergetrees Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.17), 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-mergetrees_0.1.3-1.ca2604.1_arm64.deb Size: 64202 MD5sum: 1beb1d024b8a5ca9e122c999816282e3 SHA1: 2d07996dd24311ae7bb18775b65e712dd81f02d6 SHA256: 53ba8f0f000b1682c3191e49a77d22c9467e0ce7449e23421f9539c0185b14c5 SHA512: 1a0d05bb9570b710ed85f01c1289ce6ee3de126cf2ae80218dd355fbe3762c68943f8c04e571633544477f9661a92d6850a1a6fa4d28565aee79711ef4298396 Homepage: https://cran.r-project.org/package=mergeTrees Description: CRAN Package 'mergeTrees' (Aggregating Trees) Aggregates a set of trees with the same leaves to create a consensus tree. The trees are typically obtained via hierarchical clustering, hence the hclust format is used to encode both the aggregated trees and the final consensus tree. The method is exact and proven to be O(nqlog(n)), n being the individuals and q being the number of trees to aggregate. 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See Ekstrøm, C. T. (2016). The R Primer. 2nd edition. Chapman & Hall. Package: r-cran-metabma Architecture: arm64 Version: 0.6.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6883 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), 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-bridgesampling, r-cran-coda, r-cran-laplacesdemon, r-cran-logspline, r-cran-mvtnorm, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-metabma_0.6.9-1.ca2604.1_arm64.deb Size: 1637820 MD5sum: 88f3eb73ad500c768018845ad372ba42 SHA1: 98fadcd8b2034f0d9fc2647855317e9d0b56bc52 SHA256: 6b875a35b8b93008e94ddfcd923849e9768fb86ff1d06b73694a90d09ee2227d SHA512: 8efa328d0e14aa42da8f95ef09d8ac15ec302b126dfd963f159853ff3daec536fde64cba1485e1c043420e330fd9527c0f3b931ddd94b9fc9cbb69b0f46a9085 Homepage: https://cran.r-project.org/package=metaBMA Description: CRAN Package 'metaBMA' (Bayesian Model Averaging for Random and Fixed EffectsMeta-Analysis) Computes the posterior model probabilities for standard meta-analysis models (null model vs. alternative model assuming either fixed- or random-effects, respectively). These posterior probabilities are used to estimate the overall mean effect size as the weighted average of the mean effect size estimates of the random- and fixed-effect model as proposed by Gronau, Van Erp, Heck, Cesario, Jonas, & Wagenmakers (2017, ). The user can define a wide range of non-informative or informative priors for the mean effect size and the heterogeneity coefficient. Moreover, using pre-compiled Stan models, meta-analysis with continuous and discrete moderators with Jeffreys-Zellner-Siow (JZS) priors can be fitted and tested. This allows to compute Bayes factors and perform Bayesian model averaging across random- and fixed-effects meta-analysis with and without moderators. For a primer on Bayesian model-averaged meta-analysis, see Gronau, Heck, Berkhout, Haaf, & Wagenmakers (2021, ). Package: r-cran-metacart Architecture: arm64 Version: 3.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 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-gridextra, r-cran-rpart, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-metacart_3.0.4-1.ca2604.1_arm64.deb Size: 361128 MD5sum: dfa217d3b96cc62efafe882e50ac6050 SHA1: dafc27740fe925760ffc2d02d5cf9b0fe6b41bdc SHA256: f141ef3333b89c73d0efb71cd833b9e41ab6a87099d7d24d17940d67ea36560e SHA512: ff8c0830810174f3cf814ba5a83e22982f0d9926058829f84fb83ed7dc8c1f103b43883fee2303c1bc3fb7bc3d29d6344d64256df1b8f23217860b68866d7053 Homepage: https://cran.r-project.org/package=metacart Description: CRAN Package 'metacart' (Meta-CART: A Flexible Approach to Identify Moderators inMeta-Analysis) Meta-CART integrates classification and regression trees (CART) into meta-analysis. Meta-CART is a flexible approach to identify interaction effects between moderators in meta-analysis. The method is described in Dusseldorp et al. (2014) and Li et al. (2017) . Package: r-cran-metacoder Architecture: arm64 Version: 0.3.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2867 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2068922 MD5sum: e1e04969f9d0363883aa42ca05d15fd4 SHA1: adb67c739c2b08db96812354d694f745cd5a8ad1 SHA256: 826f9e07fd79b56d7ec18cc51a73975c09f5f18c2b5566d5287187351a13bf33 SHA512: 80737d8a6bb71a386fdded7c9a6e343be0c736a4863827a9cb9ebc6788fc7704d1e182c72f38482a6740f4829bc55c270a0cf3cbc86d3e780b80b4b139c53498 Homepage: https://cran.r-project.org/package=metacoder Description: CRAN Package 'metacoder' (Tools for Parsing, Manipulating, and Graphing TaxonomicAbundance Data) Reads, plots, and manipulates large taxonomic data sets, like those generated from modern high-throughput sequencing, such as metabarcoding (i.e. amplification metagenomics, 16S metagenomics, etc). It provides a tree-based visualization called "heat trees" used to depict statistics for every taxon in a taxonomy using color and size. It also provides various functions to do common tasks in microbiome bioinformatics on data in the 'taxmap' format defined by the 'taxa' package. The 'metacoder' package is described in the publication by Foster et al. (2017) . Package: r-cran-metadynminer3d Architecture: arm64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2267 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_arm64.deb Size: 2044118 MD5sum: 9c258eebbedc1e711e0afb9c157e8840 SHA1: 15828713aadb2803246a9583b9d9bf5534d5b4fb SHA256: 447b2d11f561446f3dbb71af2c2c6c674a1ba1fa1b5b85978303754504384a4f SHA512: 97aaa6b9c77a3bae37a1bd19f635ad6bc7a051a96c732f302d9c87e196fc405887c66ef0cc8d7ef017a06c217ffa692075cbf475ac93b23154503ffe55591637 Homepage: https://cran.r-project.org/package=metadynminer3d Description: CRAN Package 'metadynminer3d' (Tools to Read, Analyze and Visualize Metadynamics 3D HILLS Filesfrom 'Plumed') Metadynamics is a state of the art biomolecular simulation technique. 'Plumed' Tribello, G.A. et al. (2014) program makes it possible to perform metadynamics using various simulation codes. The results of metadynamics done in 'Plumed' can be analyzed by 'metadynminer'. The package 'metadynminer' reads 1D and 2D metadynamics hills files from 'Plumed' package. As an addendum, 'metadynaminer3d' is used to visualize 3D hills. It uses a fast algorithm by Hosek, P. and Spiwok, V. (2016) to calculate a free energy surface from hills. Minima can be located and plotted on the free energy surface. Free energy surfaces and minima can be plotted to produce publication quality images. Package: r-cran-metadynminer Architecture: arm64 Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2797 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_arm64.deb Size: 2589824 MD5sum: adadbf5b39a9a61ce2bcfd71e1e66121 SHA1: e20364e8d84db988d1366b3c728f3005fc53a808 SHA256: db5214a4cab4b736a62f2ba8e5555ca16864daeb98125bb66d43c0d73e32e059 SHA512: 479818b49e46ceddb433093aa105c47e7c36fb8a6ee0fc70c53d5c45696064871586cd7f1173243174e3bc3ad2097d8fc671618f79d553edb06d5803ab9fa280 Homepage: https://cran.r-project.org/package=metadynminer Description: CRAN Package 'metadynminer' (Tools to Read, Analyze and Visualize Metadynamics HILLS Filesfrom 'Plumed') Metadynamics is a state of the art biomolecular simulation technique. 'Plumed' Tribello, G.A. et al. (2014) program makes it possible to perform metadynamics using various simulation codes. The results of metadynamics done in 'Plumed' can be analyzed by 'metadynminer'. The package 'metadynminer' reads 1D and 2D metadynamics hills files from 'Plumed' package. It uses a fast algorithm by Hosek, P. and Spiwok, V. (2016) to calculate a free energy surface from hills. Minima can be located and plotted on the free energy surface. Transition states can be analyzed by Nudged Elastic Band method by Henkelman, G. and Jonsson, H. (2000) . Free energy surfaces, minima and transition paths can be plotted to produce publication quality images. Package: r-cran-metafolio Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1255 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 980786 MD5sum: 68e8fa8244d7cb5b38e651bf170812c3 SHA1: 06fad2efee510c838cfc2691d72083144bf0b2d2 SHA256: f15025918a00509c9044581bfe3c15691be5ed85b13a560f1baadb386c39c524 SHA512: ee52dabc8aaf97a224bfb864af47ed403cd101e17f3e95e82da7b5b812ce8bf31db7662010c09d16827f1ce5c912ffb5bf06cd72e94117ca27b382576074084d Homepage: https://cran.r-project.org/package=metafolio Description: CRAN Package 'metafolio' (Metapopulation Simulations for Conserving Salmon ThroughPortfolio Optimization) A tool to simulate salmon metapopulations and apply financial portfolio optimization concepts. The package accompanies the paper Anderson et al. (2015) . 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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 . 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The focus of the package is simulating populations of one or multiple species in a grid-based landscape and studying the meta-population dynamics and emergent patterns that arise from the interaction of species under complex environmental conditions. It provides functions for common ecological processes such as negative exponential, kernel-based dispersal (see Nathan et al. (2012) ), calculation of the environmental suitability based on cardinal values ( Yin et al. (1995) , simplified by Yan and Hunt (1999) see eq: 4), reproduction in form of an Ricker model (see Ricker (1954) and Cabral and Schurr (2010) ), as well as metabolic scaling based on the metabolic theory of ecology (see Brown et al. (2004) and Brown, Sibly and Kodric-Brown (2012) ). Package: r-cran-metarep Architecture: arm64 Version: 1.2.1-1.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_arm64.deb Size: 189036 MD5sum: 6820b5ce5d26947b9389c4a4a9844ef8 SHA1: 228f4ec8fb600cc4bc9f63344adc33f9709485e6 SHA256: 717cc43a286e2012252755db1a482918d06938e38d5cbb28309859aaebf09351 SHA512: c94b73096558142feb19ac6d5a294d314a40e171a7c9ce81b51a90a708f9e10eb06280f775883fd3fadb48cbc225508e3a5cf7b19b296f26d532c665cccec3db 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. 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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: arm64 Version: 0.4-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2434 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_arm64.deb Size: 800890 MD5sum: 021932022a79a00f636ecb4aabc61a73 SHA1: 93ac529e1203513b0724b4fec4eb10c5b66d15f9 SHA256: 878589365dd350858365374c73e43e4936306984718b9c879467919120131894 SHA512: d9c3bb8124ed69b90d9bb48e336698d1a1a17020f7a3c88489f0e470cdcb74d81c302f1d4c04351f43ab6f4462e55633a482a79426e0b224ddcb5a2a854e0311 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: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4433 Depends: libc6 (>= 2.29), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-bioc-bsseq, r-bioc-methrix, r-bioc-beachmat, r-bioc-genomicranges, r-bioc-summarizedexperiment, r-bioc-delayedarray, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-methfuse_1.1.0-1.ca2604.1_arm64.deb Size: 4279428 MD5sum: 3a0d99abd47ecc4a738a65c2cf5fca85 SHA1: 4734522e5fd65d8b0d9aa8745612f2425ea2b63e SHA256: ad478d1180132840bb45f68cce7dc8b99000e3e87269bcedd30925195e1e8bf6 SHA512: a39c7871f23c5581c198b11818ef16dcb25841d839a73dc82a68276409a9b2006d1480e6284c4924c7f22adc54d2c36ad0755b4764cbeb431e09ab875dd9045f Homepage: https://cran.r-project.org/package=methFuse Description: CRAN Package 'methFuse' (Functional Segmentation of the Methylome) Implements FUSE (Functional Segmentation of DNA methylation data), a data-driven method for identifying spatially coherent DNA methylation segments from whole-genome bisulfite sequencing (WGBS) count data. The method performs hierarchical clustering of CpG sites based on methylated and unmethylated read counts across multiple samples and determines the optimal number of segments using an information criterion (AIC or BIC). Resulting segments represent regions with homogeneous methylation profiles across the input cohort while allowing sample-specific methylation levels. The package provides functions for clustering, model selection, tree cutting, segment-level summarization, and visualization. Input can be supplied as count matrices or extracted directly from 'BSseq' and 'methrix' objects. Package: r-cran-methscope Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7163 Depends: libc6 (>= 2.38), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-xgboost, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-caret, r-cran-doparallel, r-cran-ggplot2, r-cran-uwot, r-cran-magrittr, r-cran-fnn, r-cran-data.table, r-cran-nnls Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling Filename: pool/dists/resolute/main/r-cran-methscope_1.0.1-1.ca2604.1_arm64.deb Size: 7124864 MD5sum: 17c79d75bad88027e05e56cce9fbe246 SHA1: 2279e4f0b8fbf0a3c55324850dbd534d4955ad95 SHA256: ed33b8b8db8d0d6f4d43074ee668b408d61c7cf1badbe10eabb2e520039db133 SHA512: 6e5dc29343f5b96eabb7b6b8ad7647e95437454c15991f6ecf98d72dd41b6f6969e801744736e9ec6ed4c0214750dc209e0a92319bba9196c8573b4013c608d5 Homepage: https://cran.r-project.org/package=MethScope Description: CRAN Package 'MethScope' (Ultra-Fast Analysis of Sparse DNA Methylome via RecurrentPattern Encoding) Methods for analyzing DNA methylation data via Most Recurrent Methylation Patterns (MRMPs). Supports cell-type annotation, spatial deconvolution, unsupervised clustering, and cancer cell-of-origin inference. Includes C-backed summaries for YAME “.cg/.cm” files (overlap counts, log2 odds ratios, beta/depth aggregation), an XGBoost classifier, NNLS deconvolution, and plotting utilities. Scales to large spatial and single-cell methylomes and is robust to extreme sparsity. Package: r-cran-metricgraph Architecture: arm64 Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2496 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2004358 MD5sum: ae873c70e22427c74a65b3b3349208e9 SHA1: 3a6d0eb155eee6277f0d16938c07a28a80034d1e SHA256: 723ada09e4de70f537da8a4322c52aea676eb99aaa66ae380ebe1b292088521b SHA512: dc70350e89fedfc6df939f20b2d39b5ca1e55fe9ce8fb518ecd93be1cc4177dbb799e8a6bd3f74a5bf15265df8595cebcf498971eba95408b18f8ed7fc1b6a2d 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 . 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Exact simulation from max-stable processes (Dombry, Engelke and Oesting, 2016, , R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, ) and Extremal Student (Thibaud and Opitz, 2015, ). Threshold selection methods, including Wadsworth (2016) , and Northrop and Coleman (2014) . Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) . 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Package: r-cran-mexhaz Architecture: arm64 Version: 2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 739 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 595686 MD5sum: a9735b83478c4294af90105716427990 SHA1: aa190879ec709bbec6b1e45c00f21e84b44f37a1 SHA256: cc883afbef03704590c8dc50d63efd067e6e623563875462dfa9c983d5e1a6cd SHA512: 3cc5a4aa54485f785cae870af40671742e4d9bf763dd2f00c182692fd1f0cb0efb3e6ffd4502862d285381b354659ed1616b23817d75529e7993b3467d252122 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: arm64 Version: 1.3-11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 Depends: libfftw3-double3 (>= 3.3.10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fundata, r-cran-abind, r-cran-foreach, r-cran-irlba, r-cran-matrix, r-cran-mgcv, r-cran-plyr Suggests: r-cran-covr, r-cran-fda, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mfpca_1.3-11-1.ca2604.1_arm64.deb Size: 255176 MD5sum: e4e35869bd379214f2f661cf36ac1001 SHA1: 30b1850000ac204cfb496d357b5bd14cc52d9e3b SHA256: 9cb819768dc7560c5df772306e9c3cc41c09f114026f2ef9967bfd59e818dbb9 SHA512: 82e69cdc480d2bd223139a9d815c13277232da7ae30b6d817a0c032f55fc860b713ce88d8360bde18deafa3bff3416a251e749f6ec047735164fdedbf81f7fa0 Homepage: https://cran.r-project.org/package=MFPCA Description: CRAN Package 'MFPCA' (Multivariate Functional Principal Component Analysis for DataObserved on Different Dimensional Domains) Calculate a multivariate functional principal component analysis for data observed on different dimensional domains. The estimation algorithm relies on univariate basis expansions for each element of the multivariate functional data (Happ & Greven, 2018) . Multivariate and univariate functional data objects are represented by S4 classes for this type of data implemented in the package 'funData'. For more details on the general concepts of both packages and a case study, see Happ-Kurz (2020) . 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The package provides a fit / diagnose / report pipeline covering anchoring, linking, bias and differential-functioning screening, and publication-oriented reporting summaries, with reproducibility manifests for replay. See 'Andrich' (1978) , 'Masters' (1982) , and 'Muraki' (1992) for the underlying ordered-response models. Package: r-cran-mfsd Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2450 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 2348696 MD5sum: d1084615e0f277c3e9fa3e95439f6376 SHA1: 46c4684e98ca3d2498058abadd01edada94f71a8 SHA256: 9e5e8ecb741fd942f35c450f7edc1225eb7275caa1d728e46a3e31a62c9d63c2 SHA512: 973728de384caf96a93fee1e2b0f10b780c828741d121b0013eb32888afe26e9ecb222ca8d3f7962f10aeefb6333fc015f22768ca054a526f4b0ce355aafb30b Homepage: https://cran.r-project.org/package=MFSD Description: CRAN Package 'MFSD' (Multivariate Functional Spatial Data) Analysis of multivariate functional spatial data, including spectral multivariate functional principal component analysis and related statistical procedures (Si-Ahmed, Idris, et al. "Principal component analysis of multivariate spatial functional data." Big Data Research 39 (2025) 100504). (Kuenzer, T., Hörmann, S., & Kokoszka, P. (2021). "Principal component analysis of spatially indexed functions." Journal of the American Statistical Association, 116(535), 1444-1456.) (Happ, C., & Greven, S. (2018). "Multivariate functional principal component analysis for data observed on different (dimensional) domains." Journal of the American Statistical Association, 113(522), 649-659.) Package: r-cran-mgarchbekk Architecture: arm64 Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 166 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 77700 MD5sum: a9f1c82aa029829de3a967353d8ee384 SHA1: 1f9a2d6ef931c6edfde9148ae555e236b1cf9feb SHA256: 835cb9ae16819f25be51771e6f18f28d1b28936bd1bd8a016b123279c849ce50 SHA512: 6879e8b860531217a1ae1995d918c48cba4de866d4973aeb618c8f099805a98f8821fba6936d1cb48686404fb02a2ee574f91190122a153ba77ab01f1c4d03f9 Homepage: https://cran.r-project.org/package=mgarchBEKK Description: CRAN Package 'mgarchBEKK' (Simulating, Estimating and Diagnosing MGARCH (BEKK and mGJR)Processes) Procedures to simulate, estimate and diagnose MGARCH processes of BEKK and multivariate GJR (bivariate asymmetric GARCH model) specification. Package: r-cran-mgc Architecture: arm64 Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1156 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-abind, r-cran-boot, r-cran-energy, r-cran-raster Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-reshape2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mgc_2.0.2-1.ca2604.1_arm64.deb Size: 779338 MD5sum: fa219626815818c1ff10ee5d166b933e SHA1: 7839918b005d5749a04bfe39e8a9a2e151055ab0 SHA256: 7e765cfb530a45687d6ed17f9abad8ef2f16df6856a184ef5bd947172b59ad69 SHA512: a972b063558335fec8e2c14fe569707c945068e34eac02c34ca2ca8b8536ceea0eb0c741cabd4c580a6d711793433311021a0eb8d7cd33c9177927225accf21f Homepage: https://cran.r-project.org/package=mgc Description: CRAN Package 'mgc' (Multiscale Graph Correlation) Multiscale Graph Correlation (MGC) is a framework developed by Vogelstein et al. (2019) that extends global correlation procedures to be multiscale; consequently, MGC tests typically require far fewer samples than existing methods for a wide variety of dependence structures and dimensionalities, while maintaining computational efficiency. Moreover, MGC provides a simple and elegant multiscale characterization of the potentially complex latent geometry underlying the relationship. Package: r-cran-mgcv Architecture: arm64 Version: 1.9-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3997 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgomp1 (>= 6), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-matrix Suggests: r-cran-survival, r-cran-mass Filename: pool/dists/resolute/main/r-cran-mgcv_1.9-4-1.ca2604.1_arm64.deb Size: 3548046 MD5sum: 55cfe7949ff15d69ffeec5e69eab7f9a SHA1: bd245499f75f4e9dba8a089bd133407861d00e5c SHA256: bca863d780f605c0e0bf9228e32dc432fbffa423901f05c58d0cc46056598ee4 SHA512: 4bdf941fb194d51d8a5fd4cd642aff72d4911fe00e017213eb1eae36178c7782bdf6e306045fad77e0d27306403497227da1a9aa225a7a87a50b6f494b1bfd92 Homepage: https://cran.r-project.org/package=mgcv Description: CRAN Package 'mgcv' (Mixed GAM Computation Vehicle with Automatic SmoothnessEstimation) Generalized additive (mixed) models, some of their extensions and other generalized ridge regression with multiple smoothing parameter estimation by (Restricted) Marginal Likelihood, Cross Validation and similar, or using iterated nested Laplace approximation for fully Bayesian inference. See Wood (2025) for an overview. Includes a gam() function, a wide variety of smoothers, 'JAGS' support and distributions beyond the exponential family. Package: r-cran-mgdrive Architecture: arm64 Version: 1.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1920 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1177422 MD5sum: 0ec66cb40341ea2a04f61fef1f0514c0 SHA1: 2faaae7ab91a58182f4cd656217445b15713e9cf SHA256: d0dae26957e284264860fa93d319cb5ead2129da51ee5f7a50b4d400a6d3d475 SHA512: 2a0e55ca96ca251c8e0d940700ea582ee85ba1e9faea82872662c87ba4e09ec2ca4d0b61cfa73aaa932b6526a792df1e04e8ef06247300c29c787827f5819c7a 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: arm64 Version: 0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 212044 MD5sum: 1a055ec229d0a9edafc90f8913425d16 SHA1: 2a840425f1567ef72d891382c63b851e29776589 SHA256: be68b07235b3769fdc7c83eceb00720dd294ff85e249ef015a13c3a7862e1167 SHA512: 273581afa491754f7e6d203a8b8a5f66141099f3a9e4b6c94f5e6fdd536a463beb3c12ea7c24917c3a8cb77c5a86b34c7cc809aa329b2909bdbd2ea5ed4e6ea4 Homepage: https://cran.r-project.org/package=mgee2 Description: CRAN Package 'mgee2' (Marginal Analysis of Misclassified Longitudinal Ordinal Data) Three estimating equation methods are provided in this package for marginal analysis of longitudinal ordinal data with misclassified responses and covariates. The naive analysis which is solely based on the observed data without adjustment may lead to bias. The corrected generalized estimating equations (GEE2) method which is unbiased requires the misclassification parameters to be known beforehand. The corrected generalized estimating equations (GEE2) with validation subsample method estimates the misclassification parameters based on a given validation set. This package is an implementation of Chen (2013) . Package: r-cran-mgl Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mgl_1.1-1.ca2604.1_arm64.deb Size: 17564 MD5sum: 5a9c3ec4e06ad1c4508571346efbc436 SHA1: 43b728b24dd199db0531135400c22477c7bf89d9 SHA256: e4733f67a8876ca7887153a2bb7ba901f488ad98303a8eba93a5ca0e84a536f5 SHA512: e9daea244820a09c27ec9cdbe75aded7dc249c1580f5c2dc285198deab8f450e551994759e24893bff2551555a07839dbf538a2cf1a49b01cac8aae1685dce78 Homepage: https://cran.r-project.org/package=MGL Description: CRAN Package 'MGL' (Module Graphical Lasso) An aggressive dimensionality reduction and network estimation technique for a high-dimensional Gaussian graphical model (GGM). Please refer to: Efficient Dimensionality Reduction for High-Dimensional Network Estimation, Safiye Celik, Benjamin A. Logsdon, Su-In Lee, Proceedings of The 31st International Conference on Machine Learning, 2014, p. 1953--1961. Package: r-cran-mgmm Architecture: arm64 Version: 1.0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 885 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-cluster, r-cran-glue, r-cran-mvnfast, r-cran-plyr, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-withr Filename: pool/dists/resolute/main/r-cran-mgmm_1.0.1.3-1.ca2604.1_arm64.deb Size: 633620 MD5sum: 946d46e365a35863c1b08c3d62be740f SHA1: f198de13165cdd15eea3b1f267c9daf2a5eee8e9 SHA256: 20b51847d1442d071ba5b446ba7dc4a50209ef5674c38888fcc0e9723fe9e79b SHA512: 810cb436eee4a42c277e1bb7e50555781c5dd8bd089c327bbe0d808aae0ea392ebca47d42d98b8efef11267485be933817c4e0022181210738120dc2a5372204 Homepage: https://cran.r-project.org/package=MGMM Description: CRAN Package 'MGMM' (Missingness-Aware Gaussian Mixture Models) Parameter estimation and classification for Gaussian Mixture Models (GMMs) in the presence of missing data. This package complements existing implementations by allowing for both missing elements in the input vectors and full (as opposed to strictly diagonal) covariance matrices. Estimation is performed using an expectation conditional maximization algorithm that accounts for missingness of both the cluster assignments and the vector components. The output includes the marginal cluster membership probabilities; the mean and covariance of each cluster; the posterior probabilities of cluster membership; and a completed version of the input data, with missing values imputed to their posterior expectations. For additional details, please see McCaw ZR, Julienne H, Aschard H. "Fitting Gaussian mixture models on incomplete data." . 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Package: r-cran-mgsfpca Architecture: arm64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2121 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_arm64.deb Size: 1857426 MD5sum: a0ffc857d86fe10c05732b115ed951d5 SHA1: caf31794dbf1f3a0c411e663a9a632254d388de1 SHA256: 5c113b8789c2171588300b8a228a0d8370293c6e06642aa2996db8315b009987 SHA512: 0988984e9e08e43ba3c9dbe8eb38680cccdc6924e49fcd4a25d0b3d3d2344947eabfc5a7fdfac4dbb409c3e929a20fce42b5913dfeb85f4377cd3f62ec9c0f25 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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Implements Multiscale Geographically Weighted Regression with Top-Down Scale approaches (Geniaux 2026) . 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For nonparametric survival function estimates, the Volterra, Dabrowska, and Prentice-Cai estimates for bivariate failure time data may be computed as well as the Dabrowska estimate for the trivariate failure time data. Bivariate marginal hazard rate regression can be fitted for the bivariate failure time data. Functions are also provided to compute (bootstrap) confidence intervals and plot the estimates of the bivariate survival function. For details, see "The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach", Prentice, R., Zhao, S. (2019, ISBN: 978-1-4822-5657-4), CRC Press. Package: r-cran-mhd Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 279 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 153520 MD5sum: c912cb8e78c3fe6f3fb2fed2e56268f3 SHA1: a983252cd79df6c1d947ff26668c168bfa10e873 SHA256: 299a39440f0ad6f805ba4269362bd966739258723706c724f8ecebbb1e90fa8c SHA512: fdc064fddd8182e99271bec7fba27419b01e3682f236268cda6c520830ffb33fb3d16b02c85f9a48f3be5041782da63431f95eb2665138e830b0a9886518fcc1 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. Package: r-cran-mhorseshoe Architecture: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 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-ggplot2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mhorseshoe_0.1.5-1.ca2604.1_arm64.deb Size: 118280 MD5sum: 88c442ea8ee747879fff949a14a9324e SHA1: f1daac9b59c1bc0ace0e3835169957855c999b82 SHA256: 6f8f676d61cf51922727a3972145db5a2cdafbad24849de8c66fd887566e91e7 SHA512: 27c0f87394118af4696ed28729b21d811cb459471ca565555768f1b3de419796f2004d0d50385421d45649d9bd7f7bdac1f7f3b57af0e1f44e493ee3e89e7a1f Homepage: https://cran.r-project.org/package=Mhorseshoe Description: CRAN Package 'Mhorseshoe' (Approximate Algorithm for Horseshoe Prior) Provides exact and approximate algorithms for the horseshoe prior in linear regression models, which were proposed by Johndrow et al. (2020) . Package: r-cran-mhpfilter Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 972 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-data.table, r-cran-collapse, r-cran-ggplot2 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyverse, r-cran-fastverse Filename: pool/dists/resolute/main/r-cran-mhpfilter_0.1.0-1.ca2604.1_arm64.deb Size: 528714 MD5sum: 054e476034509310818f9cd2193545e7 SHA1: 8a4c41525ca8db893e4ccf761cd2e085103e6450 SHA256: 43e95e4da900d19bf217ee9504e171ecc9729716521fdca467bd57a481600105 SHA512: 414e6cbfbb1f4e1cdc28d2def95bde1d3f2d587bd30dce1d44c3c19619d0ee5c3d799d0c70128ceed61d5e093a219d12cd8b26dcee449e5153f3e69dd8619f50 Homepage: https://cran.r-project.org/package=mhpfilter Description: CRAN Package 'mhpfilter' (Modified Hodrick-Prescott Filter with Optimal SmoothingParameter Selection) High-performance implementation of the Modified Hodrick-Prescott (HP) Filter for decomposing macroeconomic time series into trend and cyclical components. Based on the methodology of Choudhary, Hanif and Iqbal (2014) "On smoothing macroeconomic time series using the modified HP filter", which uses generalized cross-validation (GCV) to automatically select the optimal smoothing parameter lambda, following McDermott (1997) "An automatic method for choosing the smoothing parameter in the HP filter" (as described in Coe and McDermott (1997) ). Unlike the standard HP filter that uses fixed lambda values (1600 for quarterly, 100 for annual data), this package estimates series-specific lambda values that minimize the GCV criterion. Implements efficient C++ routines via 'RcppArmadillo' for fast computation, supports batch processing of multiple series, and provides comprehensive visualization tools using 'ggplot2'. Particularly useful for cross-country macroeconomic comparisons, business cycle analysis, and when the appropriate smoothing parameter is uncertain. Package: r-cran-mhsmm Architecture: arm64 Version: 0.4.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-mhsmm_0.4.21-1.ca2604.1_arm64.deb Size: 479798 MD5sum: a87ed44389226e80448a6e254fb7aa3e SHA1: d91828d01c6e60eae66ac3a65b1f9f222787f2f1 SHA256: 7e0ebeabf5fbdf4566970b83f6b972553ccc60057195817defddfd8b41e98381 SHA512: a37ee421300c1bd0b9a8de3e578a3684839aac7fa84ecbf031763c1aaa1e33e0dd379f5fc4292f7f6da42ef2af3ec2a9678165341c37438c0eea2977b619df1d Homepage: https://cran.r-project.org/package=mhsmm Description: CRAN Package 'mhsmm' (Inference for Hidden Markov and Semi-Markov Models) Parameter estimation and prediction for hidden Markov and semi-Markov models for data with multiple observation sequences. Suitable for equidistant time series data, with multivariate and/or missing data. Allows user defined emission distributions. Package: r-cran-mhtmult Architecture: arm64 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_arm64.deb Size: 52292 MD5sum: bb958839cd90e36ec6c849f24e1c1e94 SHA1: 737843bccf16109626f3470650db10b5ef17b075 SHA256: 228ad36105b616b769e1c16ca7f700d4578cdcd0b5a137a6cd175239c2a9b9dd SHA512: 9fa54a0e91dcfb9486a1632975015635c06a35ef571d54afac7280f25386353f77e001f7cf25ed7894507dd3794d26638a5cf9c775192a7d3531e3d9183d12f5 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: arm64 Version: 1.3-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 718 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_arm64.deb Size: 527542 MD5sum: a875c0c54e4d0737f65b4a8820a9be54 SHA1: 30ca69eac943b4e2cada3873721c66235c353a79 SHA256: 49114bfcdaf1a85fd9fb0d0be627a745ec06f7f72a520989e18d6a44f9624c57 SHA512: 2ed9a89606ac25bb81d324890d92cec93ea5d80528b3ce424c39a1c01433107f87f9f5de637349017102af3f1ef21e7eb21f4748129c1918cfb6a34824256a65 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: arm64 Version: 3.19.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1729 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1470648 MD5sum: c6d5420e9668ab8a94a255cf706f67ca SHA1: 243bf754d25957c3cd119a140c3927236eafe371 SHA256: 40c3e31337f1ffbf73ebfb5052221555dbe526988c3d7558ecefc30d62c200ad SHA512: 9509ec2505c2566e21e999e29039b115d6e8f9aeee606f13efef74b5bc349d38794794287c31fd218bbdc55ee55e241fa76e2a2b5ee29ef8f25651da67affbf8 Homepage: https://cran.r-project.org/package=mice Description: CRAN Package 'mice' (Multivariate Imputation by Chained Equations) Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) . Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations. Package: r-cran-miceadds Architecture: arm64 Version: 3.19-16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2135 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_arm64.deb Size: 1572434 MD5sum: 85a2894271b198f3f43a235dc6032afb SHA1: 33d30e0c0876ca4d647919bfbb3dbf3c12ec41e5 SHA256: 20e38cf7f26158686f726188471487877a3f9441a4ebc17fa353d1ecd66b9ca7 SHA512: 56a74616c1c91c2cc972833729b659b029292146028fe93efb66c0e9ae86ff63ded83e5a143191afefa28184580d14f032f804b028ca613f33f546318af870ae Homepage: https://cran.r-project.org/package=miceadds Description: CRAN Package 'miceadds' (Some Additional Multiple Imputation Functions, Especially for'mice') Contains functions for multiple imputation which complements existing functionality in R. In particular, several imputation methods for the mice package (van Buuren & Groothuis-Oudshoorn, 2011, ) are implemented. Main features of the miceadds package include plausible value imputation (Mislevy, 1991, ), multilevel imputation for variables at any level or with any number of hierarchical and non-hierarchical levels (Grund, Luedtke & Robitzsch, 2018, ; van Buuren, 2018, Ch.7, ), imputation using partial least squares (PLS) for high dimensional predictors (Robitzsch, Pham & Yanagida, 2016), nested multiple imputation (Rubin, 2003, ), substantive model compatible imputation (Bartlett et al., 2015, ), and features for the generation of synthetic datasets (Reiter, 2005, ; Nowok, Raab, & Dibben, 2016, ). Package: r-cran-micefast Architecture: arm64 Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2669 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 879454 MD5sum: 3b692fbfb3a2b855ec0b7fca9a7c2d68 SHA1: fc53f34b024ad492d418a2cc1e4e8e2192596c47 SHA256: 8a74a343c6968c788f5ca4a531bfa34564b8cbc12f92f0e87799e2f26e72ed2c SHA512: e04194fcff18838dcf2e9c0559ccd5f1ad48fb1981f2152f8ef8bbeecebdd1965274460c3731fce2b74688633ed8f4abdb2f6406ebe13290cd70082054bafab4 Homepage: https://cran.r-project.org/package=miceFast Description: CRAN Package 'miceFast' (Fast Imputations Using 'Rcpp' and 'Armadillo') Fast imputations under the object-oriented programming paradigm. Moreover there are offered a few functions built to work with popular R packages such as 'data.table' or 'dplyr'. The biggest improvement in time performance can be achieved for a calculation where a grouping variable is used. A single evaluation of a quantitative model for the multiple imputations is another major enhancement. A new major improvement is one of the fastest predictive mean matching in the R world because of presorting and binary search. Package: r-cran-microbenchmark Architecture: arm64 Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 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_arm64.deb Size: 66074 MD5sum: dafcb1d018c66f46624e05af420ac0d3 SHA1: 157ebfaf8e431c8e2bd02d766429002cac2b56b6 SHA256: 1d8a65ca5c36cda97e591eb0dfd259793dd1c3cb60e06f59e877f2c8b1e2c2bc SHA512: e2dd760a816adbae1819694326e3c4139ca0ce31e673d941341b826739b7569bd93d42383cf05020fe68ad1bce482a9b258854c8e3c9bcb628ca842f543d9bad Homepage: https://cran.r-project.org/package=microbenchmark Description: CRAN Package 'microbenchmark' (Accurate Timing Functions) Provides infrastructure to accurately measure and compare the execution time of R expressions. Package: r-cran-microbiomestat Architecture: arm64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 507 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_arm64.deb Size: 372928 MD5sum: 8316ebf42a1954dc40244422f92ca5c9 SHA1: 655b047804e5642621dc857f52fe6c3a4c8614d4 SHA256: 2aea4385c83586fceb1aa39648db7929c3cb1cd5e0f816cb13f60ca5898cda97 SHA512: cfafc5c0e108f121d337802442a75d2f742cfa90eeceec604608d3433d45e2bc3323728ff097d4da2b2bcc32d244b1991b770301a6ec4d63dd61284ae484ff31 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: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4430 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2915274 MD5sum: c9cd1f3e95034062505fee450ce5c35a SHA1: f383982375acedab70df5136ce14abaaaf94bb6c SHA256: b10fcb761e0aa6eecbd4070b2426682b790ad7a76fae1cc3b72d2b11befc2348 SHA512: 6f47475498bf5f2736a07d7c4e92a0f516fbe173f91dbc4bd470b26452ba4fe4200359c4849253ebf076849507d1fb44ee0e5d1fac2379981a911d952c302139 Homepage: https://cran.r-project.org/package=MicroMoB Description: CRAN Package 'MicroMoB' (Discrete Time Simulation of Mosquito-Borne Pathogen Transmission) Provides a framework based on S3 dispatch for constructing models of mosquito-borne pathogen transmission which are constructed from submodels of various components (i.e. immature and adult mosquitoes, human populations). A consistent mathematical expression for the distribution of bites on hosts means that different models (stochastic, deterministic, etc.) can be coherently incorporated and updated over a discrete time step. Package: r-cran-microseq Architecture: arm64 Version: 2.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 396 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 181014 MD5sum: 29236e788e4378d59dfd771440e9c8a1 SHA1: 7ea5cfb33d296c23fd7275a0e448cf26734d55f9 SHA256: 0ac939588e2e462cfa4dc11fab20bcd8473145bc1ad2ca2f37257e9404ee5db9 SHA512: 2bfb580bc4bb32c9bde0efe43e9c8c842b7aad963ae0d0569352b494d3e422594d9b48ecc42db5bd67f506bbbe45068cbb49866b077f891b080cec2498621d98 Homepage: https://cran.r-project.org/package=microseq Description: CRAN Package 'microseq' (Basic Biological Sequence Handling) Basic functions for microbial sequence data analysis. The idea is to use generic R data structures as much as possible, making R data wrangling possible also for sequence data. Package: r-cran-microsimulation Architecture: arm64 Version: 1.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7366 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_arm64.deb Size: 1029358 MD5sum: 7dd34be1536fd51fe29ec5a0a8aa1b5c SHA1: 7c46108893709a761061172ad751d5eb8aaf5739 SHA256: f42362478cbf758ac3e2e4bbc3328a0dd9e40c4f320e80d20335812b97fb6b18 SHA512: cbb1ee98ee2dc63e0244c86f0d986ba51fac5a6ddd54c69e00e81d7ef7193995116036d963f4f2fd53db8451bcfdc89e894ddb635aaf204cc1e91d79750e829c Homepage: https://cran.r-project.org/package=microsimulation Description: CRAN Package 'microsimulation' (Discrete Event Simulation in R and C++, with Tools forCost-Effectiveness Analysis) Discrete event simulation using both R and C++ (Karlsson et al 2016; ). The C++ code is adapted from the SSIM library , allowing for event-oriented simulation. The code includes a SummaryReport class for reporting events and costs by age and other covariates. The C++ code is available as a static library for linking to other packages. A priority queue implementation is given in C++ together with an S3 closure and a reference class implementation. Finally, some tools are provided for cost-effectiveness analysis. Package: r-cran-micsplines Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-micsplines_1.0-1.ca2604.1_arm64.deb Size: 28338 MD5sum: 351c2ca16548c970a11512300c2bab58 SHA1: 8ab6f5bc7417d1617ef0b4817d8b4e6a62605eb1 SHA256: d3a910f7a8d5f4b51b92cf3d12ff986d0c53d2d661ca5b4b6db771a227942763 SHA512: 5c8410e2b4cdd9f914d901f8e19bba2ef1c355047095fb36a64d823d9257b0e7d6b1052255d5573e35c08c0c846b5f764c0d98a68ad9867efecaf9d399efcc1b Homepage: https://cran.r-project.org/package=MICsplines Description: CRAN Package 'MICsplines' (The Computing of Monotonic Spline Bases and ConstrainedLeast-Squares Estimates) Providing C implementation for the computing of monotonic spline bases, including M-splines, I-splines, and C-splines, denoted by MIC splines. The definitions of the spline bases are described in Meyer (2008) . The package also provides the computing of constrained least-squares estimates when a subset of or all of the regression coefficients are constrained to be non-negative. Package: r-cran-micsr Architecture: arm64 Version: 0.1-4-1.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_arm64.deb Size: 1725654 MD5sum: 780e91bf3a1445cbe445447ac9a0fdb7 SHA1: cf58f1fc5a26daafa86d1f3786e8439e4c2e980e SHA256: 136db73b63cc7f9da401314988bfab0dc20d2a483a8d21cb38a629a0df5308ae SHA512: db48fc23967631b6ef96073b26dee04e55e093d2dcf24b343edcda6cd881e37374e6e152bd5f2d338af3fe770eb2a39c0fd650886f4817be59d51c021024abf8 Homepage: https://cran.r-project.org/package=micsr Description: CRAN Package 'micsr' (Microeconometrics with R) Functions, data sets and examples for the book: Yves Croissant (2025) "Microeconometrics with R", Chapman and Hall/CRC The R Series . The package includes a set of estimators for models used in microeconometrics, especially for count data and limited dependent variables. Test functions include score test, Hausman test, Vuong test, Sargan test and conditional moment test. A small subset of the data set used in the book is also included. Package: r-cran-midasml Architecture: arm64 Version: 0.1.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1012 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-dorng, r-cran-doparallel, r-cran-foreach, r-cran-randtoolbox, r-cran-snow, r-cran-lubridate Filename: pool/dists/resolute/main/r-cran-midasml_0.1.11-1.ca2604.1_arm64.deb Size: 935462 MD5sum: 612dacec7e0c1491679acde6e35578eb SHA1: 579b55aeb30defbc19c531215e3844dee8bbb3fc SHA256: 625f7015140224062d9422a9888fd172ed18953613ac6827d7aab0e69cd31c3e SHA512: 9e898a70e4c9c1169ff87f03a317c613e3d96b9d2a6d6065d67abbdd038c6a6069b3026f7006f03bad160c26c822ea9da7d1433f5bf6927b617c1c02328df14c Homepage: https://cran.r-project.org/package=midasml Description: CRAN Package 'midasml' (Estimation and Prediction Methods for High-Dimensional MixedFrequency Time Series Data) The 'midasml' package implements estimation and prediction methods for high-dimensional mixed-frequency (MIDAS) time-series and panel data regression models. The regularized MIDAS models are estimated using orthogonal (e.g. Legendre) polynomials and sparse-group LASSO (sg-LASSO) estimator. For more information on the 'midasml' approach see Babii, Ghysels, and Striaukas (2021, JBES forthcoming) . The package is equipped with the fast implementation of the sg-LASSO estimator by means of proximal block coordinate descent. High-dimensional mixed frequency time-series data can also be easily manipulated with functions provided in the package. Package: r-cran-midnight Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 851 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 548502 MD5sum: d7cd9c20bdc06f169471c2efe9e4dc89 SHA1: 48c75197e9d8d7ca7968e16cdc85277f901b9224 SHA256: df1605e9c053c9d1b0344fb8b3a8188d31fa2786d68904216b8a6fc45f1934da SHA512: 11e86985de2280f2d112b2f7780e9d24e38a1ff35967854863c3d1f07f29f79eec6a0c55245d8865a99601b099a72db1025f13a90cf15e472e4f5808b91e99fd Homepage: https://cran.r-project.org/package=midnight Description: CRAN Package 'midnight' (A 'tidymodels' Engine and Other Extensions for the 'midr'Package) Provides a 'parsnip' engine for the 'midr' package, enabling users to fit, tune, and evaluate Maximum Interpretation Decomposition (MID) models within the 'tidymodels' framework. Developed through research by the Moonlight Seminar 2025, a study group of actuaries from the Institute of Actuaries of Japan, to enhance practical applications of interpretable modeling. Detailed methodology is available in Asashiba et al. (2025) . Package: r-cran-midr Architecture: arm64 Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 947 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 779626 MD5sum: d0d3ff40e804289db56320498d5aa191 SHA1: f090ceae47ad3cfc9e11231846e3dc804de75a0a SHA256: 29af94fed83e367f969c6a229cf36562f850b5666a98a119cbf28c19c3dfc052 SHA512: 876dacd338436d6c8a8de41ca1c9dfdcb8abbdd048e36b4a3bd6699c82ed252e09e2e4c94202fcae7a2b80cc488e6bef2a74a04e84189244b65026eda753c44d Homepage: https://cran.r-project.org/package=midr Description: CRAN Package 'midr' (Learning from Black-Box Models by Maximum InterpretationDecomposition) The goal of 'midr' is to provide a model-agnostic method for interpreting and explaining black-box predictive models by creating a globally interpretable surrogate model. The package implements 'Maximum Interpretation Decomposition' (MID), a functional decomposition technique that finds an optimal additive approximation of the original model. This approximation is achieved by minimizing the squared error between the predictions of the black-box model and the surrogate model. The theoretical foundations of MID are described in Iwasawa & Matsumori (2025) [Forthcoming], and the package itself is detailed in Asashiba et al. (2025) . Package: r-cran-mig Architecture: arm64 Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 416 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 216646 MD5sum: df2219c2a7984c52b86fcbc6406821d9 SHA1: b4a985e4b5d105469111bcf29572d8b19a656f61 SHA256: 6229f7c196857f3316c09dc58735e8e6f78a5e643e6040a98843dc51a95b7a94 SHA512: 5bb170406acc72af13c4137151eb589ad153c9f4c389b4e1361ae22c3d4d2d47f81c02de65d8116316f0009e925b575c89c436678172908f91d5429df3fc09d3 Homepage: https://cran.r-project.org/package=mig Description: CRAN Package 'mig' (Multivariate Inverse Gaussian Distribution) Provides utilities for estimation for the multivariate inverse Gaussian distribution of Minami (2003) , including random vector generation and explicit estimators of the location vector and scale matrix. The package implements kernel density estimators discussed in Belzile, Desgagnes, Genest and Ouimet (2024) for smoothing multivariate data on half-spaces. Package: r-cran-miic Architecture: arm64 Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 768 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_arm64.deb Size: 498708 MD5sum: d11a0866be93c38719df35d6447bcfdf SHA1: d9571d20e7ecd80b84cbb4c7fdffe48c84506b07 SHA256: 2145ef237fb495a6e3dd6d9d29ef6ea13c9912c6e31831cc9de42876d6450cff SHA512: b5fd914c0e51408f5492ab84e179683eb962b26078c71afc52cf0a246b7e38f855bfef433df96f21dca5e22394aa9a4abd50a50fd8903a9065cb6de18665e0b2 Homepage: https://cran.r-project.org/package=miic Description: CRAN Package 'miic' (Learning Causal or Non-Causal Graphical Models Using InformationTheory) Multivariate Information-based Inductive Causation, better known by its acronym MIIC, is a causal discovery method, based on information theory principles, which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. The recent more interpretable MIIC extension (iMIIC) further distinguishes genuine causes from putative and latent causal effects, while scaling to very large datasets (hundreds of thousands of samples). Since the version 2.0, MIIC also includes a temporal mode (tMIIC) to learn temporal causal graphs from stationary time series data. MIIC has been applied to a wide range of biological and biomedical data, such as single cell gene expression data, genomic alterations in tumors, live-cell time-lapse imaging data (CausalXtract), as well as medical records of patients. MIIC brings unique insights based on causal interpretation and could be used in a broad range of other data science domains (technology, climatology, economy, ...). For more information, you can refer to: Simon et al., eLife 2024, , Ribeiro-Dantas et al., iScience 2024, , Cabeli et al., NeurIPS 2021, , Cabeli et al., Comput. Biol. 2020, , Li et al., NeurIPS 2019, , Verny et al., PLoS Comput. Biol. 2017, , Affeldt et al., UAI 2015, . Changes from the previous 1.5.3 release on CRAN are available at . Package: r-cran-milorgwas Architecture: arm64 Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1068 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_arm64.deb Size: 517058 MD5sum: a342d7daf667725e3a914af515281081 SHA1: fe6655345a6db5e9cd357b83837b0629544a66aa SHA256: b8b1d6d7319a682519b4941a9082ced122826a8eceadaea85dad78d33f4d7d46 SHA512: 7007b8b1a0daf42ca16bb59518444b3613074d267ab953af1bb2262281a2841861ad9046901344fd3a320883b0f335f10d10ec6a84502b98e6076d43b3f6334e Homepage: https://cran.r-project.org/package=milorGWAS Description: CRAN Package 'milorGWAS' (Mixed Logistic Regression for Genome-Wide Analysis Studies(GWAS)) Fast approximate methods for mixed logistic regression in genome-wide analysis studies (GWAS). Two computationnally efficient methods are proposed for obtaining effect size estimates (beta) in Mixed Logistic Regression in GWAS: the Approximate Maximum Likelihood Estimate (AMLE), and the Offset method. The wald test obtained with AMLE is identical to the score test. Data can be genotype matrices in plink format, or dosage (VCF files). The methods are described in details in Milet et al (2020) . Package: r-cran-milr Architecture: arm64 Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 408 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-piper, r-cran-numderiv, r-cran-glmnet, r-cran-rcpp, r-cran-rcppparallel, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-hmisc, r-cran-rmarkdown, r-cran-data.table, r-cran-ggplot2, r-cran-plyr Filename: pool/dists/resolute/main/r-cran-milr_0.4.1-1.ca2604.1_arm64.deb Size: 137358 MD5sum: 84e0b7a20f3a8953c1eca82340656bb7 SHA1: 0138ddc3cda182e9025f5bd2f13981d34e55d7b3 SHA256: 7b68221a98b42760d2718ec59803c103e8c03739263b354ad6cc1ffb85bb112e SHA512: fc1df3925f5d534ced5560c7999184826479ca350c40c9d07cc86157f580bddb5cd7f121cddaefbf635bbc6e70d4b26db230f57c1b31731a15552acb32a95acc Homepage: https://cran.r-project.org/package=milr Description: CRAN Package 'milr' (Multiple-Instance Logistic Regression with LASSO Penalty) The multiple instance data set consists of many independent subjects (called bags) and each subject is composed of several components (called instances). The outcomes of such data set are binary or categorical responses, and, we can only observe the subject-level outcomes. For example, in manufacturing processes, a subject is labeled as "defective" if at least one of its own components is defective, and otherwise, is labeled as "non-defective". The 'milr' package focuses on the predictive model for the multiple instance data set with binary outcomes and performs the maximum likelihood estimation with the Expectation-Maximization algorithm under the framework of logistic regression. Moreover, the LASSO penalty is attached to the likelihood function for simultaneous parameter estimation and variable selection. Package: r-cran-mime Architecture: arm64 Version: 0.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mime_0.13-1.ca2604.1_arm64.deb Size: 45646 MD5sum: ec0bc1f7812c472b734eb45680f87a05 SHA1: 3f34994e105a59a36e003f03572af03fc02ce6d0 SHA256: 8f4358cc78dbacb98b98bae166759bf61f7733450ab643b4dbee4c7e30d51fde SHA512: d8d69076bc76388a260cc62c8230c1e1626f0dbc12c9850f5de9dfc456d21109973ff1479cac9beb0b39f368869c886a83d42ec7c4dfd9b911300451db808829 Homepage: https://cran.r-project.org/package=mime Description: CRAN Package 'mime' (Map Filenames to MIME Types) Guesses the MIME type from a filename extension using the data derived from /etc/mime.types in UNIX-type systems. 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Detailed information of the ANSI C implementation of 'minepy' can be found at . Package: r-cran-minic Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 324 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-minic_1.0.3-1.ca2604.1_arm64.deb Size: 138446 MD5sum: 2ed5899209aadef30f7acc22b50f7052 SHA1: 02d6f61b07c5d70cc409294ab28005527b318dc2 SHA256: d62df6088e689a4582c6217a9b3c60a53c57f16416356d55d75ba8d62218af37 SHA512: a9dbe345fc39690a47cf3a59f8357f06fb08ffc22afd87474576f86026f0e80bd8f7b642aaea6754fc5a5aa12a6df54e0639789caa1a5685873124a381d00b4c Homepage: https://cran.r-project.org/package=minic Description: CRAN Package 'minic' (Minimization Methods for Ill-Conditioned Problems) Implementation of methods for minimizing ill-conditioned problems. Currently only includes regularized (quasi-)newton optimization (Kanzow and Steck et al. (2023), ). 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This package gives a simple implementation with a 30 line 'Stan' script. This lightweight implementation makes it an easy starting point for other projects, in particular for downstream tasks that require analysis of "compositional" data. It can be applied whenever a multinomial probability parameter is thought to depend linearly on inputs in a transformed, log ratio space. Additional utilities make it easy to inspect, create predictions, and draw samples using the fitted models. More about the LNM can be found in Xia et al. (2013) "A Logistic Normal Multinomial Regression Model for Microbiome Compositional Data Analysis" and Sankaran and Holmes (2023) "Generative Models: An Interdisciplinary Perspective" . Package: r-cran-minimaxapprox Architecture: arm64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest, r-cran-covr Filename: pool/dists/resolute/main/r-cran-minimaxapprox_0.5.0-1.ca2604.1_arm64.deb Size: 100858 MD5sum: e41aea6872b4395e0bfa7851f05af9f7 SHA1: ad4b38e851882b02d2b771686aed4b9c07668729 SHA256: 0038e33dd3e70f1fe6c41a9aa4c80ad96f4f2f278693487775082261b64178bd SHA512: 78bc644a8440d743814cf378e749d2b5f408918571009be067abe1ef80a4666b347012491f0ccaa3110cb9a70a2c7e6d5475a44c15b9d759b460b4742123c8d7 Homepage: https://cran.r-project.org/package=minimaxApprox Description: CRAN Package 'minimaxApprox' (Implementation of Remez Algorithm for Polynomial and RationalFunction Approximation) Implements the algorithm of Remez (1962) for polynomial minimax approximation and of Cody et al. (1968) for rational minimax approximation. Package: r-cran-minipch Architecture: arm64 Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 361 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-checkmate, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr, r-cran-withr, r-cran-vdiffr, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-minipch_0.4.1-1.ca2604.1_arm64.deb Size: 127994 MD5sum: d939c9567397b46d214e3f0e13a605d1 SHA1: 93956db35e797c3e34f8e23feb793d5837116ba0 SHA256: ed2143002401e10f115370b94e9c9e72cc5ee6f5a681b02e4220d609c6452f47 SHA512: 007f8bf913bfcf6c45a72ff5effd463d4a1aeadd2175e7f3645ba705d92b340bd70676fec2119956e3d3a1f88c6c2c06a8d328b05e952ed0b52c2b52d2462292 Homepage: https://cran.r-project.org/package=miniPCH Description: CRAN Package 'miniPCH' (Survival Distributions with Piece-Wise Constant Hazards) Density, distribution function, ... hazard function, cumulative hazard function, survival function for survival distributions with piece-wise constant hazards and multiple states and methods to plot and summarise those distributions. A derivation of the used algorithms can be found in my masters thesis . Package: r-cran-minmse Architecture: arm64 Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 233 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 98006 MD5sum: 049969893434e5231aa39a0221ace77c SHA1: 70e4322f6d31730e437d438aede3b1649280235e SHA256: 0505de9c0724174ef83afc30c9b36073b25d1b63ea16e98673e3767730ef4985 SHA512: 46649337054ad3d21bc4ed49d87f95f98dae76d806eb750f53f5cf7f4ab473e965937fe4eb7502b99fa61dfd0c49bc55e41d7979a42341e3aaabf20b485ddadd Homepage: https://cran.r-project.org/package=minMSE Description: CRAN Package 'minMSE' (Implementation of the minMSE Treatment Assignment Method for Oneor Multiple Treatment Groups) Performs treatment assignment for (field) experiments considering available, possibly multivariate and continuous, information (covariates, observable characteristics), that is: forms balanced treatment groups, according to the minMSE-method as proposed by Schneider and Schlather (2017) . Package: r-cran-minpack.lm Architecture: arm64 Version: 1.2-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 171 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-minpack.lm_1.2-4-1.ca2604.1_arm64.deb Size: 91936 MD5sum: f0ecbfdbd08672d2f2a6fb16e12cfd5b SHA1: 65277ced3e8f5e5fe2d29bcbe2223013ecd5f717 SHA256: d853e49a933f8f00b7803b270ef8aa0127c9d4450ac412900db7bebc87ab2078 SHA512: 524f89d78c863e847496947d007941859aa1e1bda1f53ad2d2257409ad3d9bad236310f88a4585a6823c8479ebde72b088991b71f6adf24421ec9222901812db Homepage: https://cran.r-project.org/package=minpack.lm Description: CRAN Package 'minpack.lm' (R Interface to the Levenberg-Marquardt Nonlinear Least-SquaresAlgorithm Found in MINPACK, Plus Support for Bounds) The nls.lm function provides an R interface to lmder and lmdif from the MINPACK library, for solving nonlinear least-squares problems by a modification of the Levenberg-Marquardt algorithm, with support for lower and upper parameter bounds. The implementation can be used via nls-like calls using the nlsLM function. Package: r-cran-minqa Architecture: arm64 Version: 1.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 269 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_arm64.deb Size: 110302 MD5sum: 4533573fadb5ce79f8f1647d5282c19c SHA1: a9a9a663cec8b895041b2c1405e57eb6fd97e87c SHA256: fec47890232b39008f2468b1f7c9a1126c688bdb47414be74fcdb1be895c1ef2 SHA512: 98d4330fdad547d528e5218853aca84784103a41cd452471f1625669960cf7dd2ae435d6f5dca745c275f2f3e923e6a0817d309d331d5618701fca1d6afbc21c Homepage: https://cran.r-project.org/package=minqa Description: CRAN Package 'minqa' (Derivative-Free Optimization Algorithms by QuadraticApproximation) Derivative-free optimization by quadratic approximation based on an interface to Fortran implementations by M. J. D. Powell. Package: r-cran-mintriadic Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 198 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-lolog, r-cran-bh Suggests: r-cran-network, r-cran-rmarkdown, r-cran-knitr, r-cran-sna, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mintriadic_1.0.0-1.ca2604.1_arm64.deb Size: 46554 MD5sum: d668c0d81cfde0877a0c9131faf72fe8 SHA1: 419bdb3dea362280b05c578cfe5214a4bdfec972 SHA256: 94ce3f63751cc7b7a70e1b7223be521555984b93fe5f9ebb65dfdc879eca6fe5 SHA512: 7f463c460cdb25ef09cd8616b484442e49b30a80e2bfa0bbe2a0ef12dc00e4f1243512e4c0773cf10c43b19da78856f695baac93085cd930d7d808d83b631ea2 Homepage: https://cran.r-project.org/package=MinTriadic Description: CRAN Package 'MinTriadic' (Extension to the 'Lolog' Package for 'Triadic' NetworkStatistics) Provides an extension to the 'lolog' package by introducing the minTriadicClosure() statistic to capture higher-order interactions among triplets of nodes. This function facilitates improved modelling of group formations and 'triadic' closure in networks. A smoothing parameter has been incorporated to avoid numerical errors. Package: r-cran-minty Architecture: arm64 Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 800 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tzdb, r-cran-cpp11 Suggests: r-cran-knitr, r-cran-stringi, r-cran-testthat, r-cran-withr, r-cran-hms, r-cran-readr Filename: pool/dists/resolute/main/r-cran-minty_0.0.6-1.ca2604.1_arm64.deb Size: 295212 MD5sum: 34aa8dc00a526887091b5a747d48b48d SHA1: bd820ba2c8658acdfd9d61e073d30e55d9b6bb54 SHA256: d3d2b8901deef111048bb706a9d3c8ca373e33257f5b17b3eaba78cb6458627e SHA512: 3b03fed45fdf85917bd7402565c505432e2fa3e3fdd384c80d1177102171c2a0a8cb24bf7c0661a73fd5fb7f7d4d44136fe5218d216969e1cde53fc9179f8bcc Homepage: https://cran.r-project.org/package=minty Description: CRAN Package 'minty' (Minimal Type Guesser) Port the type guesser from 'readr' (so-called 'readr' first edition parsing engine, now superseded by 'vroom'). Package: r-cran-mires Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6816 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_arm64.deb Size: 1732946 MD5sum: baebfd1f7da186bb2ec6e84b9d7db507 SHA1: a0bd7cf84aeda18d5ecd8d6de8bdf5720d49b966 SHA256: 5a1b0aee0fdbfc654924af914f8c808565a351843c4a3e2d3cdae535f833849b SHA512: cd67dfa6ee35d8127dead27fe81558dd3afa978c51c04084a65d40ce120aa4ec1ec25f1b3ec569ffb5d178017adecc93778dd0082ab014192783cdc58e257731 Homepage: https://cran.r-project.org/package=MIRES Description: CRAN Package 'MIRES' (Measurement Invariance Assessment Using Random Effects Modelsand Shrinkage) Estimates random effect latent measurement models, wherein the loadings, residual variances, intercepts, latent means, and latent variances all vary across groups. The random effect variances of the measurement parameters are then modeled using a hierarchical inclusion model, wherein the inclusion of the variances (i.e., whether it is effectively zero or non-zero) is informed by similar parameters (of the same type, or of the same item). This additional hierarchical structure allows the evidence in favor of partial invariance to accumulate more quickly, and yields more certain decisions about measurement invariance. Martin, Williams, and Rast (2020) . Package: r-cran-mirnass Architecture: arm64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 498 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 358368 MD5sum: b01e80c31a0258cd798f6556aee43f8d SHA1: f141af40078c4d234525269b7b507ff596894799 SHA256: 6d516f5543bfb84d316777e79f99ad4728837349b12f7561eade01218b80fdb3 SHA512: d53aea551f94426bda9d458b4bb6382ee5f63457e9f71572b35a7e59bd925e848cdd0ce0d85e2f924eebceaa3190dfc15fbd56a7f796b8ff33b727d0c405e2f7 Homepage: https://cran.r-project.org/package=miRNAss Description: CRAN Package 'miRNAss' (Genome-Wide Discovery of Pre-miRNAs with few Labeled Examples) Machine learning method specifically designed for pre-miRNA prediction. It takes advantage of unlabeled sequences to improve the prediction rates even when there are just a few positive examples, when the negative examples are unreliable or are not good representatives of its class. Furthermore, the method can automatically search for negative examples if the user is unable to provide them. MiRNAss can find a good boundary to divide the pre-miRNAs from other groups of sequences; it automatically optimizes the threshold that defines the classes boundaries, and thus, it is robust to high class imbalance. Each step of the method is scalable and can handle large volumes of data. Package: r-cran-mirt Architecture: arm64 Version: 1.46.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2954 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 2243392 MD5sum: a9baf686e41a9ed6f4e9367202c7bf4d SHA1: e6f7af9321380647bfcf6ea2706d035e6b1185bb SHA256: 557b2f3470caee91b658974e7f2d4a53fa8f83efdf9bbdf717c205fc48109c5c SHA512: 2f36d8f70739e53a2b508092a6302f6cd502190aa69c2d77fe5eb6bd03cf70f2a060a13bfa4dd60cc06ca58e05da04491054de80808d17a786d2f2ce6f0cb7dd 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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The models require side information from a secondary data set on the misclassification process, i.e. some sort of misclassification probabilities conditional on some common covariates. A detailed description of the algorithm can be found in Dlugosz, Mammen and Wilke (2015) . Package: r-cran-miscmath Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 151 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-randomforest, r-cran-numbers Filename: pool/dists/resolute/main/r-cran-miscmath_1.1-1.ca2604.1_arm64.deb Size: 57858 MD5sum: 2baddcf97d489198bd43a8ff3217537f SHA1: b700e1ec44020c8afd5c39b34a9d8712f5346c1b SHA256: 7ca6227046e3129ea85e1b407456974eb473e3db29431f4f8486ec75dd92cb5b SHA512: 4d6726fdcee4051604b05e4571ad36effcb6e58ccb98d9079beb759ee671632cf11d9a98b9b6d717dc64d4d137b937b1b7086c4ac14d86511c1d7bfa5e1a1c5b Homepage: https://cran.r-project.org/package=MiscMath Description: CRAN Package 'MiscMath' (Miscellaneous Mathematical Tools) Some basic math calculators for finding angles for triangles and for finding the greatest common divisor of two numbers and so on. 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The 'misha' package implements an efficient data structure for storing genomic data, and provides a set of functions for data extraction, manipulation and analysis. Some of the 2D genome algorithms were described in Yaffe and Tanay (2011) . Package: r-cran-mispu Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 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-vegan, r-cran-ape, r-cran-aspu, r-cran-cluster, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-ade4 Filename: pool/dists/resolute/main/r-cran-mispu_1.0-1.ca2604.1_arm64.deb Size: 156522 MD5sum: 06f789bf77f78e2bfb215e5de27a68ab SHA1: 065d34dd474beab4144a6e2be48f27bc812b882e SHA256: f4e3107a81069d76964058469384b29f7809f336194f1372a3a0f999a5d6c2fe SHA512: 0516094f50d9380e72454480d763f4ea6f188ecd2b3b1acd9355397838fd3f12c0c85c7d29aee8df431ef079eecb0bc9c06d19ff9c8b4ff2b08a76615c64c33a Homepage: https://cran.r-project.org/package=MiSPU Description: CRAN Package 'MiSPU' (Microbiome Based Sum of Powered Score (MiSPU) Tests) There is an increasing interest in investigating how the compositions of microbial communities are associated with human health and disease. In this package, we present a novel global testing method called aMiSPU, that is highly adaptive and thus high powered across various scenarios, alleviating the issue with the choice of a phylogenetic distance. Our simulations and real data analysis demonstrated that aMiSPU test was often more powerful than several competing methods while correctly controlling type I error rates. Package: r-cran-misscp Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 399 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_arm64.deb Size: 178474 MD5sum: 9c9ad7c43bf46526e54cee8bcdc27a82 SHA1: 880366eebad480176a7d64cc786d3411a3b9a2a2 SHA256: 6eaf32ad2e4277fc29374786ea71e1ab0f3e3bb4946dcb79c7ac13fa46c3587d SHA512: fd17812eaad7bd9802b1b4ff9632e20ae4159f588696b4f3af628d12d040215ed3700b8a5c917b5d13e874fbb9819d36083f25492af6564337a5666c5a8586c4 Homepage: https://cran.r-project.org/package=MissCP Description: CRAN Package 'MissCP' (Change Point Detection with Missing Values) A four step change point detection method that can detect break points with the presence of missing values proposed by Liu and Safikhani (2023) . Package: r-cran-missdeaths Architecture: arm64 Version: 2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 487 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 288206 MD5sum: 47c65578485ff158a627eb50feb5a270 SHA1: 5ae63d8bb694c83e574466d4976e5be4d559587c SHA256: d430859e519c8407d115f1a3c4b0539fd9bcb0c3e877f5eb411aa7044e730a3a SHA512: c1041b44a3e5f791f15283f8e0fe51d71bbf9471a207ccaf42eda81f5263eabcf86ec74483bfa9c5febfbf6933e4d02f2851014343eeff129239611bafc4e800 Homepage: https://cran.r-project.org/package=missDeaths Description: CRAN Package 'missDeaths' (Simulating and Analyzing Time to Event Data in the Presence ofPopulation Mortality) Implements two methods: a nonparametric risk adjustment and a data imputation method that use general population mortality tables to allow a correct analysis of time to disease recurrence. 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Package: r-cran-missonet Architecture: arm64 Version: 1.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1952 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1031794 MD5sum: 05c180929352e28b0f747d09789ae5ef SHA1: 24bdfc551b084bbff09c710cff17d398872782d0 SHA256: b8636b92096ed00a906422afe6c381a6d5fe434ea03435d51cbfaf6f2a2b0223 SHA512: 464843ce8d690ff1efec28248a51f25a1bdaad1c3c1cd9d8615a57c2fcb45fe2262364fb829a8f84a1d2c0112209754b2a1d95de34a24233b956773d2e5b492d Homepage: https://cran.r-project.org/package=missoNet Description: CRAN Package 'missoNet' (Joint Sparse Regression & Network Learning with Missing Data) Simultaneously estimates sparse regression coefficients and response network structure in multivariate models with missing data. Unlike traditional approaches requiring imputation, handles missingness natively through unbiased estimating equations (MCAR/MAR compatible). Employs dual L1 regularization with automated selection via cross-validation or information criteria. Includes parallel computation, warm starts, adaptive grids, publication-ready visualizations, and prediction methods. Ideal for genomics, neuroimaging, and multi-trait studies with incomplete high-dimensional outcomes. See Zeng et al. (2025) . Package: r-cran-misssbm Architecture: arm64 Version: 1.0.5-1.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_arm64.deb Size: 1952874 MD5sum: 62f614dfd408d02cf5bd4cc44709eb1d SHA1: 2d6e332bb3c9de9e67b8abc5b9c09b5c25f6b5c6 SHA256: 7fe3fcd8f13c34fd4180f28520a51e8d660a66adc0ed539d652a04598617d52c SHA512: 83352af36cf55abaec186eddf5e6a4afc292e7a2349be6f13b0bafcc1aeadebef5c3d10a6f89e5a55ac56909f4315373b248585d72a0d30543a250f79fc8b0e1 Homepage: https://cran.r-project.org/package=missSBM Description: CRAN Package 'missSBM' (Handling Missing Data in Stochastic Block Models) When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0 due to missing information between node pairs), it is possible to account for the underlying process that generates those NAs. 'missSBM', presented in 'Barbillon, Chiquet and Tabouy' (2022) , adjusts the popular stochastic block model from network data sampled under various missing data conditions, as described in 'Tabouy, Barbillon and Chiquet' (2019) . Package: r-cran-misssom Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 483 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_arm64.deb Size: 353290 MD5sum: f15919d88cddca967a8772b07716fd64 SHA1: 87bdafd084ff2afa7dd7da2a8567e00f8bebe1ce SHA256: 1776c155fb40ece1da8022003f0839c1fa910fa5ed781b490383168adbf59710 SHA512: 86cb6a5fbb51ddb5f2ccc80bcb4fe7b962966e1f2a5f9e195a69d8229100c8a055028be14bc15622335979b4c0fff6fe9e39c2579b0f3bcea4c1eea5a4d1a264 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) . 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Package: r-cran-mixak Architecture: arm64 Version: 5.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2112 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_arm64.deb Size: 1702704 MD5sum: d1c5f4d04d8386bf1d8c227c358156b7 SHA1: 642ad9d1124d8675fb9c6c762b8084b23c14eb64 SHA256: 1d61ab971c600cb1fa4278b3b6ddf554c40127c60faa5373515a3f93c71883f7 SHA512: 61a44ce7fadb40fde4655349a9b210d108f48707d2b910580482bdfa1c581bdab6371a3c0f8d1cf9622c5622c1e2c433199a37f6e805753ef9c60bbbdb50fe7d 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: arm64 Version: 1.0-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 70628 MD5sum: 1c3e408e3c994238690afa06d981c417 SHA1: f3563231d2a0f36944a0a2159545b027cf33cfaa SHA256: d096cc30464bc606d567e9a95118cd02d7623320d74a0b9e5dc104790468812d SHA512: 9711799253826948ad89c9ae1927c62adb082711fee8239c0c0315951d92bba557c9354177f1d4e9a5c6200e7b3c17133dac1883194ba30807f368b60d6f8e73 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. 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The package implements a hierarchical Dirichlet (Process) mixture of multinomial distributions. It is thus a probabilistic latent class model (LCM) and can be used to reduce the dimensionality of hierarchical data and cluster individuals into latent classes. It can automatically infer an appropriate number of latent classes or find k classes, as defined by the user. The model is based on a paper by Dunson and Xing (2009) , but implements a scalable variational inference algorithm so that it is applicable to large datasets. It is described and tested in the accompanying paper by Ahlmann-Eltze and Yau (2018) . Package: r-cran-mixedbayes Architecture: arm64 Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 952 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_arm64.deb Size: 293436 MD5sum: 9b606c288129e54abae850826866d2e7 SHA1: 270f8bbfc9b1144c3753582cafbe63dce1873d69 SHA256: 3a21ed020f815a5894f36e0ff2f561cf5b32e1e6f8a9391f4c3d65899ab3d904 SHA512: 89e2fc562b7709d59bfb10b23c3158f7302fcbaa4833cace714291f55a0c64585f039f1dfc23f2601332e3a3b2cf82b7e373968fcd8f5edb5c0ff41b500b4b4f Homepage: https://cran.r-project.org/package=mixedBayes Description: CRAN Package 'mixedBayes' (Bayesian Longitudinal Regularized Quantile Mixed Model) With high-dimensional omics features, repeated measure ANOVA leads to longitudinal gene-environment interaction studies that have intra-cluster correlations, outlying observations and structured sparsity arising from the ANOVA design. In this package, we have developed robust sparse Bayesian mixed effect models tailored for the above studies (Fan et al. (2025) ). An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in 'C++'. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University. Package: r-cran-mixedcca Architecture: arm64 Version: 1.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 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_arm64.deb Size: 142282 MD5sum: f755671858b23b161aace89d4bea4bbe SHA1: 8af920d983e67ad6bdea0a5bcf9869e0d80b4b6f SHA256: 5e66d66003ce21a512e5d9086bebd9e7a6f2d51534a1d94b0d58d579e14a6cce SHA512: 9fd1c7153967883db5e4fcb03c9f2cc60849dbbc9dbb81dbf3618d7e15f958c19993883a178fcd57a4b9715716913028fed2144a394e79227e4bbc977cfaaa17 Homepage: https://cran.r-project.org/package=mixedCCA Description: CRAN Package 'mixedCCA' (Sparse Canonical Correlation Analysis for High-Dimensional MixedData) Semi-parametric approach for sparse canonical correlation analysis which can handle mixed data types: continuous, binary and truncated continuous. Bridge functions are provided to connect Kendall's tau to latent correlation under the Gaussian copula model. The methods are described in Yoon, Carroll and Gaynanova (2020) and Yoon, Mueller and Gaynanova (2021) . Package: r-cran-mixedindtests Architecture: arm64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 143720 MD5sum: 7bddd78f57421803adcc6000d3f5227b SHA1: 1bb74ef63f4de92a814f58fc3e19d7a641e9b01f SHA256: 7e4798f72ddf9fd45f5ff9ac82f4ad753e64b934791d4fbde5e5fde815c1c7e3 SHA512: 74819c82f7e379705ceed6bf7408483204cbdf3c0230cfe82445365ece345270605e19a9fe398c75cc6fc07091e34b719c9ad35e59b35fd12ee56daf0889b184 Homepage: https://cran.r-project.org/package=MixedIndTests Description: CRAN Package 'MixedIndTests' (Tests of Randomness and Tests of Independence) Functions for testing randomness for a univariate time series with arbitrary distribution (discrete, continuous, mixture of both types) and for testing independence between random variables with arbitrary distributions. The test statistics are based on the multilinear empirical copula and multipliers are used to compute P-values. The test of independence between random variables appeared in Genest, Nešlehová, Rémillard & Murphy (2019) and the test of randomness appeared in Nasri (2022). Package: r-cran-mixedmem Architecture: arm64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 894 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 535492 MD5sum: bf94b8ab9b00646e38d3b9baf279bb0a SHA1: c18f25d4353ec126f91abea124cca7298eecb035 SHA256: 0ea4d75aa66695fdd586150c82f1849e978a98f817e7282509e9a90a29c45669 SHA512: 8c1c27ba8b2dadd431ecc758cde4d754bc8f9370393890ff040483de984c7361c0affb2a3dd43e8216c9aee784496b1ee43cf1bbea6ff9b9a49d4d229a0cb627 Homepage: https://cran.r-project.org/package=mixedMem Description: CRAN Package 'mixedMem' (Tools for Discrete Multivariate Mixed Membership Models) Fits mixed membership models with discrete multivariate data (with or without repeated measures) following the general framework of Erosheva et al (2004). This package uses a Variational EM approach by approximating the posterior distribution of latent memberships and selecting hyperparameters through a pseudo-MLE procedure. Currently supported data types are Bernoulli, multinomial and rank (Plackett-Luce). The extended GoM model with fixed stayers from Erosheva et al (2007) is now also supported. See Airoldi et al (2014) for other examples of mixed membership models. Package: r-cran-mixexp Architecture: arm64 Version: 1.2.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 171016 MD5sum: 7e2b43766603b3a387b81432aabe5332 SHA1: 2b6b2f514b0078ffe282dec2567758afb3e7c3d7 SHA256: 8856a6307a239196e4be681913f8b7dfc7ef5403b8525e795f28069bdab659e6 SHA512: d3274ca0ad288bedfa5323b98cb9c5d204d7df1273eaa25d597d04ee4062892567a8d38ea9e6118b58eddb54c1653b3381d87a3c58fc5c02c1ae57953a9edfba Homepage: https://cran.r-project.org/package=mixexp Description: CRAN Package 'mixexp' (Design and Analysis of Mixture Experiments) Functions for creating designs for mixture experiments, making ternary contour plots, and making mixture effect plots. Package: r-cran-mixfmri Architecture: arm64 Version: 0.1-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1848 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1338380 MD5sum: c3a7a0b8b97547642d99f00e845f07ad SHA1: bdcf9efd246c424f2707a94e14771ea33dfbb9e9 SHA256: 8feac88f85383eae36f3d804532ddca522be47cf6255b622b9dae45817a5f6ca SHA512: a98e0bcefee1e478d3b618541b170606fe7064b89919c5dff6b15899057a9b70a3fc59eed5ec3e779d25b75f2c6319f269bb2d065f4155740a7488be2d54fc48 Homepage: https://cran.r-project.org/package=MixfMRI Description: CRAN Package 'MixfMRI' (Mixture fMRI Clustering Analysis) Utilizing model-based clustering (unsupervised) for functional magnetic resonance imaging (fMRI) data. The developed methods (Chen and Maitra (2023) ) include 2D and 3D clustering analyses (for p-values with voxel locations) and segmentation analyses (for p-values alone) for fMRI data where p-values indicate significant level of activation responding to stimulate of interesting. The analyses are mainly identifying active voxel/signal associated with normal brain behaviors. Analysis pipelines (R scripts) utilizing this package (see examples in 'inst/workflow/') is also implemented with high performance techniques. Package: r-cran-mixgb Architecture: arm64 Version: 2.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1355 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 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_arm64.deb Size: 999678 MD5sum: f148bee111e5827b664b3bab01c1b28c SHA1: 933c6b48f98848ad6ea0008087b62a396d2fb346 SHA256: 307b0521d7b0b078dae5e68698d40fc4105805995f6560de921bc699dddfc64a SHA512: 37283b0fc197dd618b06978e4dcb498717a39e490f829eb8bf7958f4cccbc59085ba6f1b8d105d258fa64b25944f0279a452f375b3fa398bb76a85887aeb9057 Homepage: https://cran.r-project.org/package=mixgb Description: CRAN Package 'mixgb' (Multiple Imputation Through 'XGBoost') Multiple imputation using 'XGBoost', subsampling, and predictive mean matching as described in Deng and Lumley (2024) . The package supports various types of variables, offers flexible settings, and enables saving an imputation model to impute new data. Data processing and memory usage have been optimised to speed up the imputation process. Package: r-cran-mixl Architecture: arm64 Version: 1.3.5-1.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_arm64.deb Size: 104478 MD5sum: c0072cf3dc14b8901883f314332b9074 SHA1: f3b30496d3edfd9506bb5bac1ba3a1f0c74f92d0 SHA256: b94a91561b64dd07ffaeffa55019c9ef90434e964c32d8eb8e8e1fb7106abe34 SHA512: f5fe7bf72eb8368f4c29ce0fde34c2e67669ec6c0a945131142f19fd32782303cfb47ad6eceaa4ba646dfaa76acf81b823d6fb1f7ff0b94aeea45708b5af6455 Homepage: https://cran.r-project.org/package=mixl Description: CRAN Package 'mixl' (Simulated Maximum Likelihood Estimation of Mixed Logit Modelsfor Large Datasets) Specification and estimation of multinomial logit models. Large datasets and complex models are supported, with an intuitive syntax. Multinomial Logit Models, Mixed models, random coefficients and Hybrid Choice are all supported. For more information, see Molloy et al. (2021) . Package: r-cran-mixlfa Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 719 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_arm64.deb Size: 316764 MD5sum: 67c58107645bf5a2e16c9550b57c8fe6 SHA1: 0531242aed489d46b4777a08eda4640628c7d8dc SHA256: c5d9807a4b7554c0463b9b8e5d31286a453bf2e58670f1b0844e1acc8d929f05 SHA512: 3faa8616ae10c95a87c3fb3c2530e84bdc718b46981ff40e02654c9ccb519d92cf3ca47a4459c7eaee7c76c196c76946c36de3cecf0d6a6bab88286680c50841 Homepage: https://cran.r-project.org/package=MixLFA Description: CRAN Package 'MixLFA' (Mixture of Longitudinal Factor Analysis Methods) Provides a function for the estimation of mixture of longitudinal factor analysis models using the iterative expectation-maximization algorithm (Ounajim, Slaoui, Louis, Billot, Frasca, Rigoard (2023) ) and several tools for visualizing and interpreting the models' parameters. Package: r-cran-mixmatrix Architecture: arm64 Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 741 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_arm64.deb Size: 319014 MD5sum: b0111164e1085697c83807796e9b3ce2 SHA1: d3ee5d701a63aac01647331ccbc1f150c845dcd0 SHA256: cdfda62d6e1ea162116dfed5936658131840f1411dce07e5caae5da995129008 SHA512: f85f645cf68f51d2be0a7aded0104ffdc6d14d6bd5d0ee2d83405cdebe5f9e50627af0cca2396798730194ca36236b8bd66a4ebb3f3efe7550955d721413ef91 Homepage: https://cran.r-project.org/package=MixMatrix Description: CRAN Package 'MixMatrix' (Classification with Matrix Variate Normal and t Distributions) Provides sampling and density functions for matrix variate normal, t, and inverted t distributions; ML estimation for matrix variate normal and t distributions using the EM algorithm, including some restrictions on the parameters; and classification by linear and quadratic discriminant analysis for matrix variate normal and t distributions described in Thompson et al. (2019) . Performs clustering with matrix variate normal and t mixture models. Package: r-cran-mixr Architecture: arm64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 530 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_arm64.deb Size: 257852 MD5sum: f47c8d071e1175e4fb38c60959161b10 SHA1: cf5c1c8f3c77a16f8ea27eff66cfaef1282492db SHA256: f3b7f58f11752a9104af7424f3740a08833f4cfe0d28c37415d860a94c4e81c8 SHA512: f683758d24c392167f938db890e603814764fe19505b25be8d490fc7f50e55911c1197cb47f3f137b9c9d8077d78beaa00d253f4ac6b59f126a34b4f35192a45 Homepage: https://cran.r-project.org/package=mixR Description: CRAN Package 'mixR' (Finite Mixture Modeling for Raw and Binned Data) Performs maximum likelihood estimation for finite mixture models for families including Normal, Weibull, Gamma and Lognormal by using EM algorithm, together with Newton-Raphson algorithm or bisection method when necessary. It also conducts mixture model selection by using information criteria or bootstrap likelihood ratio test. The data used for mixture model fitting can be raw data or binned data. The model fitting process is accelerated by using R package 'Rcpp'. Package: r-cran-mixsim Architecture: arm64 Version: 1.1-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 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_arm64.deb Size: 113564 MD5sum: fced982fbf9387048dc473801220a7f3 SHA1: 0c4773eed31c40d8b90e5f4076477cfe990df1af SHA256: 1df3e433251a7fb32f90d3b717ece1b6ea2a0146830e6304531e54ca640fd373 SHA512: d154a25ac636bd5236e4e488dc32e54caf3793a99f91c7252549d739b1dfe5d170afca19954af92f8da17db87ba09e3664f731971e375cbf7433439b52193af9 Homepage: https://cran.r-project.org/package=MixSim Description: CRAN Package 'MixSim' (Simulating Data to Study Performance of Clustering Algorithms) The utility of this package is in simulating mixtures of Gaussian distributions with different levels of overlap between mixture components. Pairwise overlap, defined as a sum of two misclassification probabilities, measures the degree of interaction between components and can be readily employed to control the clustering complexity of datasets simulated from mixtures. These datasets can then be used for systematic performance investigation of clustering and finite mixture modeling algorithms. Among other capabilities of 'MixSim', there are computing the exact overlap for Gaussian mixtures, simulating Gaussian and non-Gaussian data, simulating outliers and noise variables, calculating various measures of agreement between two partitionings, and constructing parallel distribution plots for the graphical display of finite mixture models. Package: r-cran-mixsqp Architecture: arm64 Version: 0.3-54-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 192038 MD5sum: e4feb7b6e36540ee8956cdb4c4b06603 SHA1: d78df14210618ab77924a9f5d1274cabf7e2bd8d SHA256: 97820302760d9a74b9bd401f59a8813634fa0c1d86b2aba49b784d1853b8030c SHA512: af08eed3ea65ed860e76f36c5617342f3515d6ae172082a0acd41f311d14ac0120036dd7c1ce6f49a3520dee4ef460d2263156ea54b12fb86ca487c27ecabea3 Homepage: https://cran.r-project.org/package=mixsqp Description: CRAN Package 'mixsqp' (Sequential Quadratic Programming for Fast Maximum-LikelihoodEstimation of Mixture Proportions) Provides an optimization method based on sequential quadratic programming (SQP) for maximum likelihood estimation of the mixture proportions in a finite mixture model where the component densities are known. The algorithm is expected to obtain solutions that are at least as accurate as the state-of-the-art MOSEK interior-point solver (called by function "KWDual" in the 'REBayes' package), and they are expected to arrive at solutions more quickly when the number of samples is large and the number of mixture components is not too large. This implements the "mix-SQP" algorithm, with some improvements, described in Y. Kim, P. Carbonetto, M. Stephens & M. Anitescu (2020) . Package: r-cran-mixtime Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1422 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13), r-base-core (>= 4.6.0), r-api-4.0, r-cran-lifecycle, r-cran-vctrs, r-cran-rlang, r-cran-cli, r-cran-s7, r-cran-vecvec, r-cran-tzdb, r-cran-cpp11 Suggests: r-cran-tsibble, r-cran-testthat, r-cran-pillar, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mixtime_0.1.0-1.ca2604.1_arm64.deb Size: 846560 MD5sum: f9c5ff599745f6ad8cbbf10f61fe82f6 SHA1: 138730eea6bde577e9f02621ba419c1a04c508b5 SHA256: cc85c6196020d1da43b2aa7a855b83186c4f474c6b876d8a740436807b36b06a SHA512: 5318dd28773d0a1658f6318fb0b989d888f095bcec0ef922cd66723fce6afc1b69027107d0c4fa23e760d71a365a7db38dacce3d9c7370f51156d2e7ab3d6ec0 Homepage: https://cran.r-project.org/package=mixtime Description: CRAN Package 'mixtime' (Mixed Temporal Vectors and Operations) Flexible time classes for time series analysis and forecasting with mixed temporal granularities. Supports linear and cyclical time representations in discrete and continuous forms, with timezone support, across multiple calendar systems including Gregorian and ISO week date calendars. Time points are stored numerically relative to a chronon; an atomic time granule defined by time units of a calendar. Calendrical arithmetic enables conversion between time granules (e.g. days to months) and calendar systems. Multi-unit arithmetic allows for temporal analysis with other granules of common calendars (e.g. fortnights are 2-week units). Time vectors of different granularities (e.g. monthly and quarterly) can be combined in a single vector, making 'mixtime' ideal for data that changes observation frequency over time or requires temporal reconciliation across scales. The package is extensible, allowing users to define custom calendars that build upon civil and astronomical time systems. Package: r-cran-mixtools Architecture: arm64 Version: 2.0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1609 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1414488 MD5sum: 33291cb342face104812fcbcc8d3a70b SHA1: 01d1744780a1d3c0a8b8f08662484967bc85c4ca SHA256: 5b84deb4035db8b60f19304928323dcafa62c7ea8ff60ad906ea2fc761dcc372 SHA512: 3f69fa8c4d39b8fd09d8e17469ff1cb964fd1d160a021f82cd28cefcf8b5c79dfe878c01afe1dc649235b2a637d05322655fbfb85624bbcfc9e37670adeb67f2 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: arm64 Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1430 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 623458 MD5sum: 2b2a82a75ec4276462348cb85a20ba05 SHA1: b75d1e9a6db47d4f84d133ccc0cd284ce65056fa SHA256: 8edb9b3532a29ded77f0d49cc775c88840d249b65a494f8458359e77429f045b SHA512: 37264e9ed956ced1ba01b9377099cfaa08eeab233159f9bb13c9df322a1aaca0a0f5303b596021b91319e68304ec2362fd5684f131d816c98e702070fe84a900 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: arm64 Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 281074 MD5sum: 12978afef946002249073c6f69e15a42 SHA1: acf31f3e350c1d82b21c3cbee543b80fbe89fc01 SHA256: 0417ed4fc2d7e6c4e55bc4569148bdc350f2963b3638052ceab98d722177b293 SHA512: 3c9e91732d18cf0080314560ac51de8f4174bdd95e9666176c1781f30afc7c151a83de312c23121d3bcb81fe42d25553b12d66749c1f367e62cf91244b283460 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: arm64 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_arm64.deb Size: 3074740 MD5sum: 67f6032131368075bedddf9e12a7824d SHA1: 45f46e08936e9d37b2b13b86efc51bc948c2c328 SHA256: 6386ff41a693131581db7729459bd566ac2eab2857b679a5487be3cd0e63b55f SHA512: 72218dc026a9d31acb5c9c6ec362508388f6b6a128d5c937a2d3b5085bce04369341f974f737927769be2e2657403c46fb2dae7eead6f0c1fabffdedc400c95e Homepage: https://cran.r-project.org/package=mixvlmc Description: CRAN Package 'mixvlmc' (Variable Length Markov Chains with Covariates) Estimates Variable Length Markov Chains (VLMC) models and VLMC with covariates models from discrete sequences. Supports model selection via information criteria and simulation of new sequences from an estimated model. See Bühlmann, P. and Wyner, A. J. (1999) for VLMC and Zanin Zambom, A., Kim, S. and Lopes Garcia, N. (2022) for VLMC with covariates. 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Implementation for Volkmann, Umlauf, Greven (2023) . 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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: arm64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1130 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_arm64.deb Size: 558972 MD5sum: 16b2b566a1ee27f2879b17aced6b4ad8 SHA1: 0a0211d30d77744c0df85728a23bb5066170c8e6 SHA256: 6fdb3742b5d8db7ea17553c7b4cb86d5599f66f4cc296073ad5accb45ef23aa1 SHA512: 8b68cd5b04819bfdc0e9543eb108dbad0a6d97943093a898d36d54328d18e543e00475f6ac2f20b694a4b2a4d742572524770c6208f12d9406d1da1efa006e4d Homepage: https://cran.r-project.org/package=MLBC Description: CRAN Package 'MLBC' (Bias Correction Methods for Models Using Synthetic Data) Implements three bias-correction techniques from Battaglia et al. (2025 ) to improve inference in regression models with covariates generated by AI or machine learning. Package: r-cran-mlbench Architecture: arm64 Version: 2.1-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1181 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1070496 MD5sum: be13ad9fc9b5a40a9e0db62588ca21dc SHA1: 11f91247448f6f60ea90a15a977a9da0d856b48e SHA256: 60cc7f7c0de2b56b49b0fc45146b1d49c8c70758d08b2159622f89fad4f3cab3 SHA512: 4237da872ad61696e81102da96d5d9762bbb26179b5a419b627628cf607ef485167f879a5c77bd34c0147e892cc8f5612d8b657f26a20ead74322e3c1ab14f5f Homepage: https://cran.r-project.org/package=mlbench Description: CRAN Package 'mlbench' (Machine Learning Benchmark Problems) A collection of artificial and real-world machine learning benchmark problems, including, e.g., several data sets from the UCI repository. Package: r-cran-mlecens Architecture: arm64 Version: 0.1-7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 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_arm64.deb Size: 135894 MD5sum: bd384abf3b00e570647720401e07dcb6 SHA1: 9058695b90bd55c6ff68aa510002241a077bd926 SHA256: 1409fb068d21400eae65bbc04337a201798403db8a2f0377f639f7c1952c20d7 SHA512: 9c22dc5f4a61f6c4d35a6dafad1c08535b849ba0229ca6a0cac0019feae63f6b2cac5c787f52844a1012d3944ccbbf2b24838f87a27d5bace696e4c646559b32 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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The data is converted to binary logit using the Begg & Gray approximation. The package also contains functions for maximum likelihood estimation of MNL. 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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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Comes with helper functions for functional programming, for printing, to work with 'data.table', as well as some generally useful 'R6' classes. This package also supersedes the package 'BBmisc'. Package: r-cran-mlr3oml Architecture: arm64 Version: 0.12.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 436 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-backports, r-cran-bit64, r-cran-checkmate, r-cran-curl, r-cran-data.table, r-cran-jsonlite, r-cran-lgr, r-cran-mlr3, r-cran-mlr3misc, r-cran-paradox, r-cran-r6, r-cran-stringi, r-cran-uuid, r-cran-withr Suggests: r-cran-dbi, r-cran-duckdb, r-cran-mlr3db, r-cran-qs2, r-cran-rweka, r-cran-testthat, r-cran-xml2, r-cran-httr Filename: pool/dists/resolute/main/r-cran-mlr3oml_0.12.0-1.ca2604.1_arm64.deb Size: 302704 MD5sum: d7de1661ebb0b1057e1cd3387a44e1d3 SHA1: 18b22ac00718195bb50adab18d40e825b8b9dd36 SHA256: c37415b9c4b6d9a108f8f6732a9cf391139aa53096e8323191bad86ad31d7bc1 SHA512: 9dc4313cfe586037b2dcf71fa7eeb27dc026897f2bac3050b3a839ad8f1e7d89ed7dec2648a1418aace984d82fd6a44905e37d689388c8e34b92eea361888ca3 Homepage: https://cran.r-project.org/package=mlr3oml Description: CRAN Package 'mlr3oml' (Connector Between 'mlr3' and 'OpenML') Provides an interface to 'OpenML.org' to list and download machine learning data, tasks and experiments. The 'OpenML' objects can be automatically converted to 'mlr3' objects. For a more sophisticated interface with more upload options, see the 'OpenML' package. Package: r-cran-mlr3resampling Architecture: arm64 Version: 2026.5.19-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 868 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 522372 MD5sum: d3ae2de5a295c5ac1bc4438c1af3ef16 SHA1: 2da5ba374b36141b31c6d63959aefe4fa65d4330 SHA256: 0901db306190a43b114b63e9b3239679469a7ccdbcd9f8a2e0c403c96dd686f1 SHA512: 4bed78bf583540c2dfd480652f019325ea748d6fad493233f818dfeae52caacc03c02676e2e5f984d602e9484e3c99f618b629bad90f2b8940e1299c8cefaf08 Homepage: https://cran.r-project.org/package=mlr3resampling Description: CRAN Package 'mlr3resampling' (Resampling Algorithms for 'mlr3' Framework) A supervised learning algorithm inputs a train set, and outputs a prediction function, which can be used on a test set. If each data point belongs to a subset (such as geographic region, year, etc), then how do we know if subsets are similar enough so that we can get accurate predictions on one subset, after training on Other subsets? And how do we know if training on All subsets would improve prediction accuracy, relative to training on the Same subset? SOAK, Same/Other/All K-fold cross-validation, can be used to answer these questions, by fixing a test subset, training models on Same/Other/All subsets, and then comparing test error rates (Same versus Other and Same versus All). Also provides code for estimating how many train samples are required to get accurate predictions on a test set. Package: r-cran-mlr Architecture: arm64 Version: 2.19.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5076 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_arm64.deb Size: 4792250 MD5sum: c1f96dd1b7e3ca9e3101d64384c0d527 SHA1: 042e1c8e9d10765dff02b0614dbba7a4b19b0f5d SHA256: 33d48901e3928e3a066b6bd5cee9de927590b9b898dc41cf38a853a6badee83f SHA512: 971cc833a2c977faafb6cfef3099b69eee513c991d3be29024d0f75e3046ca28c627497d1f7c93d8c71542d180f8a25fa5d0c82e398b683c3724bf78f8702bf4 Homepage: https://cran.r-project.org/package=mlr Description: CRAN Package 'mlr' (Machine Learning in R) Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling. Most operations can be parallelized. Package: r-cran-mlrmbo Architecture: arm64 Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1598 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mlr, r-cran-paramhelpers, r-cran-smoof, r-cran-backports, r-cran-bbmisc, r-cran-checkmate, r-cran-data.table, r-cran-lhs, r-cran-parallelmap Suggests: r-cran-akima, r-cran-cmaesr, r-cran-covr, r-cran-dicekriging, r-cran-earth, r-cran-emoa, r-cran-ggally, r-cran-ggplot2, r-cran-gridextra, r-cran-interp, r-cran-kernlab, r-cran-kknn, r-cran-knitr, r-cran-mco, r-cran-nnet, r-cran-party, r-cran-randomforest, r-cran-reshape2, r-cran-rgenoud, r-cran-rmarkdown, r-cran-rpart, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-mlrmbo_1.1.6-1.ca2604.1_arm64.deb Size: 949894 MD5sum: 3f8ef026cfeea72cbbe365d2d57130c1 SHA1: d5ae601e3f1ab0130e9fca446030892c3da7e0e4 SHA256: 04e55f84f4a0e7140fb9978427e8ead893eb61e5449714f070138bfd9c168068 SHA512: 217cd3b6678ce3dbf2fa9666c66360f76854ada1d4d6043135c8b33f28cfa93dd9fb1286932acd7d1d480d23b7e19923c5854e12c9e0e1d11719e732fd720cec Homepage: https://cran.r-project.org/package=mlrMBO Description: CRAN Package 'mlrMBO' (Bayesian Optimization and Model-Based Optimization of ExpensiveBlack-Box Functions) Flexible and comprehensive R toolbox for model-based optimization ('MBO'), also known as Bayesian optimization. It implements the Efficient Global Optimization Algorithm and is designed for both single- and multi- objective optimization with mixed continuous, categorical and conditional parameters. The machine learning toolbox 'mlr' provide dozens of regression learners to model the performance of the target algorithm with respect to the parameter settings. It provides many different infill criteria to guide the search process. Additional features include multi-point batch proposal, parallel execution as well as visualization and sophisticated logging mechanisms, which is especially useful for teaching and understanding of algorithm behavior. 'mlrMBO' is implemented in a modular fashion, such that single components can be easily replaced or adapted by the user for specific use cases. Package: r-cran-mlrv Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 671 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_arm64.deb Size: 291458 MD5sum: 1294a12fc6ec6e90a57a9a69fbe47268 SHA1: de7ad833dc92a46f95060075b50e3c9dec483fed SHA256: 4948f035a376b60d002b646d220f93b0808fb8438581bae295fc0cf4690decb9 SHA512: f6224287a3dcac2f3bc2d3125558a734b91b1a985f056433d5639d7fb4d2ee082ee2d02f709c7a25ea3df5b4bd51d7350f941ffc7e60df309b21d320d60aefd7 Homepage: https://cran.r-project.org/package=mlrv Description: CRAN Package 'mlrv' (Long-Run Variance Estimation in Time Series Regression) Plug-in and difference-based long-run covariance matrix estimation for time series regression. Two applications of hypothesis testing are also provided. The first one is for testing for structural stability in coefficient functions. The second one is aimed at detecting long memory in time series regression. Lujia Bai and Weichi Wu (2024) Zhou Zhou and Wei Biao Wu(2010) Jianqing Fan and Wenyang Zhang Lujia Bai and Weichi Wu(2024) Dimitris N. Politis, Joseph P. Romano, Michael Wolf(1999) Weichi Wu and Zhou Zhou(2018). Package: r-cran-mlsbm Architecture: arm64 Version: 0.99.2-1.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-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-mlsbm_0.99.2-1.ca2604.1_arm64.deb Size: 97814 MD5sum: f407bf67cd3c5244568e5c399c52261a SHA1: 4bb96bd8f930d78bc363a6e852458ea8b7d4d404 SHA256: 4555eea44cde00a3070dad4d51ce5917856029a092d10cbf73a603a55f6e8d8d SHA512: e80a0302609599bc38650eaf57b8bb5a2a9a43a2a96cd02fa7040828df975f019f7e5838ba3f5388cc7450ef62aaa67397b432ff294281165e7a82f9bca3c0f1 Homepage: https://cran.r-project.org/package=mlsbm Description: CRAN Package 'mlsbm' (Efficient Estimation of Bayesian SBMs & MLSBMs) Fit Bayesian stochastic block models (SBMs) and multi-level stochastic block models (MLSBMs) using efficient Gibbs sampling implemented in 'Rcpp'. The models assume symmetric, non-reflexive graphs (no self-loops) with unweighted, binary edges. Data are input as a symmetric binary adjacency matrix (SBMs), or list of such matrices (MLSBMs). Package: r-cran-mlstm Architecture: arm64 Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 532 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_arm64.deb Size: 196968 MD5sum: ac8f8505b3701b414a4fe9afa0da5c34 SHA1: 7d1c9037640aeec8803a02211bd2fec5e2faad2c SHA256: 5011e2394795ee351078bed18d5f3e3f660df23ee7ec04561a7ebd683ceced1f SHA512: 9606aee42eabf9f1313a573a6366dcad4ae8743dbb8418023a6bd941d3ecab279e582090a2e09f5e753352401ee365e2faabb13d81694bc2c6fe810db27ed171 Homepage: https://cran.r-project.org/package=mlstm Description: CRAN Package 'mlstm' (Multilevel Supervised Topic Models with Multiple Outcomes) Fits latent Dirichlet allocation (LDA), supervised topic models, and multilevel supervised topic models for text data with multiple outcome variables. Core estimation routines are implemented in C++ using the 'Rcpp' ecosystem. For topic models, see Blei et al. (2003) . For supervised topic models, see Blei and McAuliffe (2007) . Package: r-cran-mlt Architecture: arm64 Version: 1.8-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 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_arm64.deb Size: 377458 MD5sum: 455d1f4bbf427245acd2d6232fc2e612 SHA1: e688327a35a482b8e869497379b1730e5f2aa7f7 SHA256: e731326cc6344d40b79c608eda46c564f7930e91b643409cadb3ec9ff18d7c7d SHA512: 0fd75e920088d9866115b1b7385279670ddeb9653a7bc9e3628717cbdbf436d16eca96bdd888c5515b763ee138c602de6fc0cb7e589dc1e8d6d7460c3f6bdf17 Homepage: https://cran.r-project.org/package=mlt Description: CRAN Package 'mlt' (Most Likely Transformations) Likelihood-based estimation of conditional transformation models via the most likely transformation approach described in Hothorn et al. (2018) and Hothorn (2020) . Shift-scale (Siegfried et al, 2023, ) and multivariate (Klein et al, 2022, ) transformation models are part of this package. A package vignette is available from and more convenient user interfaces to many models from . Package: r-cran-mlts Architecture: arm64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8057 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_arm64.deb Size: 2517284 MD5sum: 01a24f273f961b71f3a9e69dccb1c307 SHA1: d79e07494f128aa4a2ad737a1a2837db2d72c1e9 SHA256: 3e64e885dafbc99f5dd0827f5f2fa90c8ec243c88648ac3a4480a783d05414ff SHA512: a917a4c23115a4f741c2ef08da56cf7c27c864f1bc11c258ec10ac96392f1baaa427cc08c04368ef4b05623a1e3cbeb9b4ed81af461c22d87a90549b739fcd40 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: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6285 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_arm64.deb Size: 1758482 MD5sum: 98b6d7bc695d52184db55f40628bda41 SHA1: 23af1f6293eccd3db082cd7ebdb9c24958fe27a3 SHA256: a98f9dcc95d22698ab325febf3c17130fb3f918c1da38df989249ee46a510567 SHA512: dbb1128376b9e18e61814b73f09c7ea1a4c1477a60decc6b15a89918ecfc36a085dc87037b121390657036701c87f4981dce70738067f881f5781ff18b89aa87 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: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1751 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_arm64.deb Size: 811302 MD5sum: c6666c509718484178d8e81998bceadf SHA1: 4e3e1b3ea1fc493def74201b8eae4d85ad8366c2 SHA256: 994e262c8db18284297eae11123b4a157655878560ac8008792c10a35c02a8d6 SHA512: a05bf943e8e556bae1b6853c9b225f71063cb96a6c2d9690c06f41855de3db39c469cbdd5a20369054fd6b54c2a7f129686cba4b6c8c0b2e704bf97c7f75b720 Homepage: https://cran.r-project.org/package=MLZ Description: CRAN Package 'MLZ' (Mean Length-Based Estimators of Mortality using TMB) Estimation functions and diagnostic tools for mean length-based total mortality estimators based on Gedamke and Hoenig (2006) . Package: r-cran-mm4lmm Architecture: arm64 Version: 3.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1058 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_arm64.deb Size: 807414 MD5sum: fea19d2e61b029535cd14a94efc21f3d SHA1: dc2f1a261f4896ceda07aaa2f4a070e3a505323d SHA256: ba228ed5d2e40dc943dc85a41c24359630b68c5d1f8d47e3ba7b16746ec410a0 SHA512: 75806d3f47b913366ae30de4b7f428f46be38364336e46a66be89884364670c72e63e88e79ed40e5360513c210e209056a5357b374ed42b87769bc253c8d26b5 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: arm64 Version: 1.2.1-1.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-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_arm64.deb Size: 183202 MD5sum: f164021e5a012478ce243358b0ec8289 SHA1: f6a47d3b4bfb6693cb6426f889fa2fe8fd921e1c SHA256: 14361680e5692db16c669f6024dbaa90da73bdb132f0c2f33235827899e971c8 SHA512: d9bf2eca9a24d8a30c7de3daf93bd556359aba1c84ed7cea4f737ce8c4bca302c1acc4979e163623fd42baa71ca5695f8f32022c21978247223fbdb863a50e68 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) . Package: r-cran-mmrm Architecture: arm64 Version: 0.3.17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6503 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-generics, r-cran-lifecycle, r-cran-mass, r-cran-matrix, r-cran-nlme, r-cran-rcpp, r-cran-rdpack, r-cran-stringr, r-cran-tibble, r-cran-tmb, r-cran-rcppeigen, r-cran-testthat Suggests: r-cran-broom, r-cran-broom.helpers, r-cran-car, r-cran-cli, r-cran-clubsandwich, r-cran-clustergeneration, r-cran-dplyr, r-cran-emmeans, r-cran-estimability, r-cran-ggplot2, r-cran-glmmtmb, r-cran-hardhat, r-cran-knitr, r-cran-lme4, r-cran-lmertest, r-cran-microbenchmark, r-cran-mockery, r-cran-parallelly, r-cran-parsnip, r-cran-purrr, r-cran-rmarkdown, r-cran-sasr, r-cran-scales, r-cran-tidymodels, r-cran-withr, r-cran-xml2 Filename: pool/dists/resolute/main/r-cran-mmrm_0.3.17-1.ca2604.1_arm64.deb Size: 2155210 MD5sum: 29e2babdd3f22fe1e1499e90554f9f81 SHA1: 4636c622f629e6bf2046ea0d75733d9cc5748a1c SHA256: da00f8d7e2010272d9ab91442a4a3af29b82e4d6dfbd419446c000d7a0639121 SHA512: f413f311e639d33e2d3b4260e1b6bd8a984c615cb5387d7285770e4d42e3c1971ce02fb6751629d559d15de65f7222fd67ae96a1be614f483256e3fe8e9051c4 Homepage: https://cran.r-project.org/package=mmrm Description: CRAN Package 'mmrm' (Mixed Models for Repeated Measures) Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see Cnaan, Laird and Slasor (1997) for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008) for a review. This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using 'emmeans'. 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Based on methods in Rosenbaum and Rubin (1985). 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Package: r-cran-mnormt Architecture: arm64 Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 250 Depends: libc6 (>= 2.39), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-mnormt_2.1.2-1.ca2604.1_arm64.deb Size: 173482 MD5sum: 8564ba030f5578611e8e99c029e928a3 SHA1: f9e3c55296fa5514f9f78bea83f80df2ac0fdb42 SHA256: a418d081684c5999eb2850ba83df590b53e0de2711db97f2e51d7d4e33567093 SHA512: 879b13b62e4e6fcb6912b9a485cfcb109166708cb6e6a7fa13b187b02d5a0c2b401f79ba31a275c0f8c32b8798ac7c015d3c3dfa307074c62c38ab09487d70ce Homepage: https://cran.r-project.org/package=mnormt Description: CRAN Package 'mnormt' (The Multivariate Normal and t Distributions, and Their TruncatedVersions) Functions are provided for computing the density and the distribution function of multi-dimensional normal and "t" random variables, possibly truncated (on one side or two sides), and for generating random vectors sampled from these distributions, except sampling from the truncated "t". Moments of arbitrary order of a multivariate truncated normal are computed, and converted to cumulants up to order 4. Probabilities are computed via non-Monte Carlo methods; different routines are used in the case d=1, d=2, d=3, d>3, if d denotes the dimensionality. Package: r-cran-mnp Architecture: arm64 Version: 3.1-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1281 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_arm64.deb Size: 1112716 MD5sum: 1e79c15387e1da09ea670d6f3c410709 SHA1: 5528c9039de58e096144082fbe3797505e02f2b9 SHA256: c3cccf0354f1fbff167af56dd1a496d9e19e7ec3957f9a61b2e1ca2f68629302 SHA512: acdc2e2838ebd1d61f8681385bf214fb6560f84c6503133c02acee7118a1c097537547f2c55e517226e6896179a759e73f602f629d0cc109d1b27b25f7264657 Homepage: https://cran.r-project.org/package=MNP Description: CRAN Package 'MNP' (Fitting the Multinomial Probit Model) Fits the Bayesian multinomial probit model via Markov chain Monte Carlo. The multinomial probit model is often used to analyze the discrete choices made by individuals recorded in survey data. Examples where the multinomial probit model may be useful include the analysis of product choice by consumers in market research and the analysis of candidate or party choice by voters in electoral studies. The MNP package can also fit the model with different choice sets for each individual, and complete or partial individual choice orderings of the available alternatives from the choice set. The estimation is based on the efficient marginal data augmentation algorithm that is developed by Imai and van Dyk (2005). "A Bayesian Analysis of the Multinomial Probit Model Using the Data Augmentation." Journal of Econometrics, Vol. 124, No. 2 (February), pp. 311-334. Detailed examples are given in Imai and van Dyk (2005). "MNP: R Package for Fitting the Multinomial Probit Model." Journal of Statistical Software, Vol. 14, No. 3 (May), pp. 1-32. . Package: r-cran-mobsim Architecture: arm64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1267 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 760944 MD5sum: e196e9c57aeb55aaa7140f3c63f12ce6 SHA1: 140b7349245bcd8c6520795921be6eda9d524e5c SHA256: 92ba11c9683d46f980b0b2860d070459782b8c7bea4b95fe4f0e33875201e574 SHA512: a0a9e33011871f1410347dddd6821afb1a759f48906a81409beb5b078bebcd000c1a8fe2fd6b018c255ead070e9cddc443b98951584082ce984123aa39c2710b Homepage: https://cran.r-project.org/package=mobsim Description: CRAN Package 'mobsim' (Spatial Simulation and Scale-Dependent Analysis of BiodiversityChanges) Simulation, analysis and sampling of spatial biodiversity data (May, Gerstner, McGlinn, Xiao & Chase 2017) . In the simulation tools user define the numbers of species and individuals, the species abundance distribution and species aggregation. Functions for analysis include species rarefaction and accumulation curves, species-area relationships and the distance decay of similarity. Package: r-cran-modelltest Architecture: arm64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 433 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 186780 MD5sum: 7f6a207461c3071e88d51b69d24d18e0 SHA1: b60bc3e6bedacb50fd0801c90bab52b51185f2d1 SHA256: dbe0f3a44b9edde7ec548978dcaea6583b9ebb887a2e2f9758688834a4e97cf8 SHA512: f12c01c0201c50b22cdf1b316376c374ed41462707b49356cc9af5734cccf0be98f24c0ad4021c01ea7a98eb09f8c9e356aa3602c10fd626d625a6f527a40731 Homepage: https://cran.r-project.org/package=modeLLtest Description: CRAN Package 'modeLLtest' (Compare Models with Cross-Validated Log-Likelihood) An implementation of the cross-validated difference in means (CVDM) test by Desmarais and Harden (2014) (see also Harden and Desmarais, 2011 ) and the cross-validated median fit (CVMF) test by Desmarais and Harden (2012) . These tests use leave-one-out cross-validated log-likelihoods to assist in selecting among model estimations. You can also utilize data from Golder (2010) and Joshi & Mason (2008) that are included to facilitate examples from real-world analysis. Package: r-cran-modelmetrics Architecture: arm64 Version: 1.2.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 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_arm64.deb Size: 139434 MD5sum: 473139dfd5b4000c86d98ead741d4845 SHA1: 5949b2542deae3e28d42ee914a58a048ba121bc0 SHA256: 15536d09ffe4edbb474019907619e90803c16c80d9219a1930862309ebbdafdc SHA512: bc041a99c7bb5c5177d6774a3ea972b0670c7d00429b7ef114423016c4c5d89b1dfab9cbad53d1b018af7aa2c9222f8666c169b20e3d770a628aca5b4b808ff7 Homepage: https://cran.r-project.org/package=ModelMetrics Description: CRAN Package 'ModelMetrics' (Rapid Calculation of Model Metrics) Collection of metrics for evaluating models written in C++ using 'Rcpp'. Popular metrics include area under the curve, log loss, root mean square error, etc. Package: r-cran-modelselection Architecture: arm64 Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2294 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_arm64.deb Size: 1369048 MD5sum: b126c22724ee85c7db4740f3169041d9 SHA1: 7078dd4acc39cec3db7e5335b0a856e4187f820c SHA256: ec1fccc9fe90b197cef6d3b70f3e2dc172dd0b554d382bd42cc2fb085297c071 SHA512: 75031fb124a1f168b703339a8565c795d57ab11fb84f3b4e956f2568b7544e09b3ae6f3a648e0060121fe7223af10dd0431586a0b51c3e2642a7f6c529fe37a3 Homepage: https://cran.r-project.org/package=modelSelection Description: CRAN Package 'modelSelection' (High-Dimensional Model Selection) Model selection and averaging for regression, generalized linear models, generalized additive models, graphical models and mixtures, focusing on Bayesian model selection and information criteria (Bayesian information criterion etc.). See Rossell (2025) (see the URL field below for its URL) for a hands-on book describing the methods, examples and suggested citations if you use the package. Package: r-cran-modernva Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 131 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-modernva_0.1.3-1.ca2604.1_arm64.deb Size: 59160 MD5sum: 617535f6d02603d4bdfe2a936a942b22 SHA1: b562057b67ef6815df65dbad515686173872aec4 SHA256: bc23577a7be0e6b5d4bc76e686f1f9c096399d02e0a3ebb5fa327bfb530504c0 SHA512: 97ee9fffcc141f0d9be72b2be55d533ac29c401764fd379b09ad818d79284817b3fd74a55cf3660e79d48073a4d88ae4346f89e07aede10ba5642b8de4a023c4 Homepage: https://cran.r-project.org/package=modernVA Description: CRAN Package 'modernVA' (An Implementation of Two Modern Education-Based Value-AddedModels) Provides functions that fit two modern education-based value-added models. One of these models is the quantile value-added model. This model permits estimating a school's value-added based on specific quantiles of the post-test distribution. Estimating value-added based on quantiles of the post-test distribution provides a more complete picture of an education institution's contribution to learning for students of all abilities. See Page, G.L.; San Martín, E.; Orellana, J.; Gonzalez, J. (2017) for more details. The second model is a temporally dependent value-added model. This model takes into account the temporal dependence that may exist in school performance between two cohorts in one of two ways. The first is by modeling school random effects with a non-stationary AR(1) process. The second is by modeling school effects based on previous cohort's post-test performance. In addition to more efficiently estimating value-added, this model permits making statements about the persistence of a schools effectiveness. The standard value-added model is also an option. Package: r-cran-modesto Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 129 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 52446 MD5sum: 80fc88c31d1602b419d5ae26f3f7a8eb SHA1: f51dd1ac001be7d63fad2c0d736be65d71a92984 SHA256: f66151bb7e3368dfb75cf0173b0ef38bfdb65ce2514f8f3d5820145d9124e6fc SHA512: 3e6e77c6eb84ff8ba064014282391ca6be0102c8ccc72a580b1b3fc221e47f11fffa65672a49ca5c99034149473e98b2453f8111fdad13493f67a4b9f1851ee4 Homepage: https://cran.r-project.org/package=modesto Description: CRAN Package 'modesto' (Modeling and Analysis of Stochastic Systems) Compute important quantities when we consider stochastic systems that are observed continuously. Such as, Cost model, Limiting distribution, Transition matrix, Transition distribution and Occupancy matrix. The methods are described, for example, Ross S. (2014), Introduction to Probability Models. Eleven Edition. Academic Press. Package: r-cran-modsem Architecture: arm64 Version: 1.0.19-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3485 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 2570416 MD5sum: 8cd1e19517b5efdaa4ad39bcd92e7697 SHA1: 162095d2b87ed8f021037ec4a0ed75ac3d2557f9 SHA256: 4f232f6dfd60e7d05403fcc2699d07b1739210fad5df23db668dd8785f58d13e SHA512: ac16fba87b25dd0ba9041a0bff4f22004c80287fe574ad6b9e9fd0c1b09b7c54d20db57d130158cb4176f6cb6596c480270ba71daa28b564de07972ba903aca5 Homepage: https://cran.r-project.org/package=modsem Description: CRAN Package 'modsem' (Latent Interaction (and Moderation) Analysis in StructuralEquation Models (SEM)) Estimation of interaction (i.e., moderation) effects between latent variables in structural equation models (SEM). The supported methods are: The constrained approach (Algina & Moulder, 2001). The unconstrained approach (Marsh et al., 2004). The residual centering approach (Little et al., 2006). The double centering approach (Lin et al., 2010). The latent moderated structural equations (LMS) approach (Klein & Moosbrugger, 2000). The quasi-maximum likelihood (QML) approach (Klein & Muthén, 2007) The constrained- unconstrained, residual- and double centering- approaches are estimated via 'lavaan' (Rosseel, 2012), whilst the LMS- and QML- approaches are estimated via 'modsem' it self. Alternatively model can be estimated via 'Mplus' (Muthén & Muthén, 1998-2017). References: Algina, J., & Moulder, B. C. (2001). . "A note on estimating the Jöreskog-Yang model for latent variable interaction using 'LISREL' 8.3." Klein, A., & Moosbrugger, H. (2000). . "Maximum likelihood estimation of latent interaction effects with the LMS method." Klein, A. G., & Muthén, B. O. (2007). . "Quasi-maximum likelihood estimation of structural equation models with multiple interaction and quadratic effects." Lin, G. C., Wen, Z., Marsh, H. W., & Lin, H. S. (2010). . "Structural equation models of latent interactions: Clarification of orthogonalizing and double-mean-centering strategies." Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). . "On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables." Marsh, H. W., Wen, Z., & Hau, K. T. (2004). . "Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction." Muthén, L.K. and Muthén, B.O. (1998-2017). "'Mplus' User’s Guide. Eighth Edition." . Rosseel Y (2012). . "'lavaan': An R Package for Structural Equation Modeling." Package: r-cran-mokken Architecture: arm64 Version: 3.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 815 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_arm64.deb Size: 691528 MD5sum: c4c825c1a1cd739d66ea707d0cc0823f SHA1: ef794d30b2092f5ecb8d795017000222f4ddc523 SHA256: 83ef1613f3f0cc2c359b3adcf06ba0e22b7384437dbf49ccf38a445a6af11218 SHA512: e6403a5f7906d9fd60d483679bd715821748250e4bfc89b67fd8d9a513a6250aa63193cb57121121254327ffe2669564edbdba2741dcea884c30c107bd98bcc9 Homepage: https://cran.r-project.org/package=mokken Description: CRAN Package 'mokken' (Conducts Mokken Scale Analysis) Contains functions for performing Mokken scale analysis on test and questionnaire data. It includes an automated item selection algorithm, and various checks of model assumptions. Package: r-cran-molhd Architecture: arm64 Version: 0.2-1.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_arm64.deb Size: 95876 MD5sum: 39da1c2cedd49ef02552b42871e07256 SHA1: d9d8ca997df11aa0282ad24b44539efde9ace0bd SHA256: cdf2284cb7db01a090c0b1896eec517b3899dedee8c45b0db66d68b53cb4e3f9 SHA512: 5ec3bce6ff9575807ba350fce412d985462b780d040271522258f9232fda664f0f6c4b5d0981dfa19e90bf8a78ca3b2215b2cf11a8834519c992f8f2fae7f05c Homepage: https://cran.r-project.org/package=MOLHD Description: CRAN Package 'MOLHD' (Multiple Objective Latin Hypercube Design) Generate the optimal maximin distance, minimax distance (only for low dimensions), and maximum projection designs within the class of Latin hypercube designs efficiently for computer experiments. Generate Pareto front optimal designs for each two of the three criteria and all the three criteria within the class of Latin hypercube designs efficiently. Provide criterion computing functions. References of this package can be found in Morris, M. D. and Mitchell, T. J. (1995) , Lu Lu and Christine M. Anderson-CookTimothy J. Robinson (2011) , Joseph, V. R., Gul, E., and Ba, S. (2015) . Package: r-cran-mombf Architecture: arm64 Version: 3.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2435 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_arm64.deb Size: 1593406 MD5sum: 1d7b5f715d40b08fbc0351f476edb327 SHA1: 5eb66f69bf2b0c78262f9d9bc11e00d92c9cb96e SHA256: ffbb636bb743428fad60a963eda8b54a62ea23a9fce6c79dc52949490ad357e3 SHA512: 73d6c20212c0e9b4f8059997afd3300dd7520ddcf801febae4957b64bc7c1fba5815e05da6d3ae1a1d511539ef8edf26f16edfccc0470e1e184b1fb492b468fc Homepage: https://cran.r-project.org/package=mombf Description: CRAN Package 'mombf' (Model Selection with Bayesian Methods and Information Criteria) Model selection and averaging for regression and mixtures, inclusing Bayesian model selection and information criteria (BIC, EBIC, AIC, GIC). Package: r-cran-momentfit Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2581 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_arm64.deb Size: 1995116 MD5sum: 13154009f9958777c0f46e6fc033df62 SHA1: 6d77367fff07a475611ec49ee4c0d277d1e9fade SHA256: 619a41148de59ba9ec8cf00cab07437609b584bacde287b5664a3631f77ce108 SHA512: 5dc2d009d20eb9f9f7d68fc838ddcb1020781fa883b23e1d0200169223cd621df54edb5611587bd147f3a0cbebbc47a607199a563130d9608b24f6600c264d40 Homepage: https://cran.r-project.org/package=momentfit Description: CRAN Package 'momentfit' (Methods of Moments) Several classes for moment-based models are defined. The classes are defined for moment conditions derived from a single equation or a system of equations. The conditions can also be expressed as functions or formulas. Several methods are also offered to facilitate the development of different estimation techniques. The methods that are currently provided are the Generalized method of moments (Hansen 1982; ), for single equations and systems of equation, and the Generalized Empirical Likelihood (Smith 1997; , Kitamura 1997; , Newey and Smith 2004; , and Anatolyev 2005 ). Some work is being done to add tools to deal with weak and/or many instruments. This includes K-Class estimators (Limited Information Maximum Likelihood and Fuller), Anderson and Rubin statistic test, etc. Package: r-cran-momentuhmm Architecture: arm64 Version: 1.5.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4008 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_arm64.deb Size: 3620474 MD5sum: 069a3ebb906b45bb0fb6abff639ed086 SHA1: a5c50a1e619265ddb901cd86bf9b00bc72d6e8e2 SHA256: f6a2215e06ec71df08eeda5fc92df0b72f4543c6338f3b33e244eb594d234755 SHA512: f856e7ca16035c58624ceb10a4a14960631d4314d4923015eddafb47ab56357dc8b4f53deb1b4a645e07c5f2b0a40433cf0f81f2ee956ad2c08af4af730ae47a Homepage: https://cran.r-project.org/package=momentuHMM Description: CRAN Package 'momentuHMM' (Maximum Likelihood Analysis of Animal Movement Behavior UsingMultivariate Hidden Markov Models) Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) . Package: r-cran-momtrunc Architecture: arm64 Version: 6.1-1.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), 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_arm64.deb Size: 670138 MD5sum: 544d5d125a1f9f1cec929ddbed33bac9 SHA1: 9e4dcb4395123ec91fcc329eda19670796878f8c SHA256: 0912578896d1836cc3d6caf83af11111b79246ee038439e681f5f80b475a2ccc SHA512: 0c26ad55de079fcceaeb2f69dded42a8ae900c0985628ae643fea650cd0cfe3e9c3426bd8c1b065f989166d485f16a17c67a32c21b2a8485e19d2e8bbb12b088 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: arm64 Version: 4.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1623 Depends: libc6 (>= 2.38), libsasl2-2 (>= 2.1.28+dfsg1), libssl3t64 (>= 3.0.0), zlib1g (>= 1:1.2.0), r-base-core (>= 4.5.0), 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_arm64.deb Size: 546200 MD5sum: e3ad5ff0fdc02d18b359299c37902ef8 SHA1: 33f9e3d2efa8f2d950c73749432e4901276fb729 SHA256: 96478f8a2ff1bfbe331b7156ee1dbf8d5fe6c6ce024fa472090ce44e3f6fb432 SHA512: 8d9a58b7db8c37f0383a7d208dfadd9f51a05ce78f00f1db9d9c0626a488971137ea5705e7ed93fb3c81d5c3939051fb8aafc1838eb2fffd9de1bc1290cb2086 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: arm64 Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3915 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1009706 MD5sum: c441f8b5d0b314eb400b96a4c3c135ba SHA1: 0f01d949cf7ed55fda3e023b19e8b7d4f3d1c4af SHA256: f73b4244a856db1c018a991374cdadee39bdca9854455a0f2e5d6979585975a5 SHA512: 2ef020812c2f82a94264d14e7ef8278b844b1c0f555ecf720522534db9106482b2b93d17b220487d83ced714f8a7db066b2dc5a8f7cb9ebe9352c8b1414b6634 Homepage: https://cran.r-project.org/package=monolix2rx Description: CRAN Package 'monolix2rx' (Converts 'Monolix' Models to 'rxode2') 'Monolix' is a tool for running mixed effects model using 'saem'. This tool allows you to convert 'Monolix' models to 'rxode2' (Wang, Hallow and James (2016) ) using the form compatible with 'nlmixr2' (Fidler et al (2019) ). If available, the 'rxode2' model will read in the 'Monolix' data and compare the simulation for the population model individual model and residual model to immediately show how well the translation is performing. This saves the model development time for people who are creating an 'rxode2' model manually. Additionally, this package reads in all the information to allow simulation with uncertainty (that is the number of observations, the number of subjects, and the covariance matrix) with a 'rxode2' model. This is complementary to the 'babelmixr2' package that translates 'nlmixr2' models to 'Monolix' and can convert the objects converted from 'monolix2rx' to a full 'nlmixr2' fit. While not required, you can get/install the 'lixoftConnectors' package in the 'Monolix' installation, as described at the following url . When 'lixoftConnectors' is available, 'Monolix' can be used to load its model library instead manually setting up text files (which only works with old versions of 'Monolix'). Package: r-cran-monomvn Architecture: arm64 Version: 1.9-21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1266 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5), r-base-core (>= 4.5.0), 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_arm64.deb Size: 1174126 MD5sum: 77e45e1d279e56a1e5eda6fd5107e5aa SHA1: 0e35add8881023121f96a6bc7cd029cbbbfe9fbe SHA256: ffce7711878aafaeace6a2a1e59ebd1b9e3f64aa12fcc9e4e8a42b9fb4f15198 SHA512: a103c20fa99775e6df3984c930bda05fb98f9a358dff00f89b8aad24c8bd399ecf6b9240b0fc13f0effb8d7390ba8aecd18429db27c999fbb92d9de1b6f9e1e2 Homepage: https://cran.r-project.org/package=monomvn Description: CRAN Package 'monomvn' (Estimation for MVN and Student-t Data with Monotone Missingness) Estimation of multivariate normal (MVN) and student-t data of arbitrary dimension where the pattern of missing data is monotone. See Pantaleo and Gramacy (2010) . Through the use of parsimonious/shrinkage regressions (plsr, pcr, lasso, ridge, etc.), where standard regressions fail, the package can handle a nearly arbitrary amount of missing data. The current version supports maximum likelihood inference and a full Bayesian approach employing scale-mixtures for Gibbs sampling. Monotone data augmentation extends this Bayesian approach to arbitrary missingness patterns. A fully functional standalone interface to the Bayesian lasso (from Park & Casella), Normal-Gamma (from Griffin & Brown), Horseshoe (from Carvalho, Polson, & Scott), and ridge regression with model selection via Reversible Jump, and student-t errors (from Geweke) is also provided. Package: r-cran-monopoly Architecture: arm64 Version: 0.3-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 518 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_arm64.deb Size: 417168 MD5sum: 884d210a17864be4f95473e32d142619 SHA1: 04f8b1cb0338d2872f78e7b0b559c7f3b19dd626 SHA256: 1e377ae46a13bf0ef79f7b15b079aa2dabee98f5badffd1eb7e3bc1f3309a026 SHA512: 82cb50ec6a58babba1befca9932fe51e7f376c5334927ebc3870347481c3dc96c60d79d70e996dadf680a2bb9d2d630c1822e1333a90be2b7b029db84a731399 Homepage: https://cran.r-project.org/package=MonoPoly Description: CRAN Package 'MonoPoly' (Functions to Fit Monotone Polynomials) Functions for fitting monotone polynomials to data. Detailed discussion of the methodologies used can be found in Murray, Mueller and Turlach (2013) and Murray, Mueller and Turlach (2016) . Package: r-cran-monoreg Architecture: arm64 Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 896 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_arm64.deb Size: 784186 MD5sum: 668a269bfb863ec87bde5607b95d66fe SHA1: 3ae0e0c6adaf519f97714e2b4729c2e53c4abb0e SHA256: d6b28a6444707334034861ab518fc9b8026d9f7a41a55619453a355d4f6f1a25 SHA512: c86e5cd102a3ea51b015d1ec88826a9897ce01e9fe6827f74e828d30ae3fd7c9c67487e2d63ec1cabd5e4ec26b3ae978e6551d915b8b7f5182dccfcb8b0e1ced Homepage: https://cran.r-project.org/package=monoreg Description: CRAN Package 'monoreg' (Bayesian Monotonic Regression Using a Marked Point ProcessConstruction) An extended version of the nonparametric Bayesian monotonic regression procedure described in Saarela & Arjas (2011) , allowing for multiple additive monotonic components in the linear predictor, and time-to-event outcomes through case-base sampling. The extension and its applications, including estimation of absolute risks, are described in Saarela & Arjas (2015) . The package also implements the nonparametric ordinal regression model described in Saarela, Rohrbeck & Arjas . Package: r-cran-monotone Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-monotone_0.1.2-1.ca2604.1_arm64.deb Size: 42986 MD5sum: 69671ad28ef606f4fc2b8fd920051183 SHA1: 1d653a55f0557b78246706cb093f3c9dd6ac3430 SHA256: bfbfedd4d9c2e546ac4e1ae6e3da0e5333a5c377f9df0d97a8a5af7b94e111d1 SHA512: 53579fe9c969337228b49a6c7fe4f57eb11df294919c856b8704d7d159c17aeef773103e91a5902c37d433da20738344fd7c17136058ee97779fb80adbae4da6 Homepage: https://cran.r-project.org/package=monotone Description: CRAN Package 'monotone' (Performs Monotone Regression) The monotone package contains a fast up-and-down-blocks implementation for the pool-adjacent-violators algorithm for simple linear ordered monotone regression, including two spin-off functions for unimodal and bivariate monotone regression (see ). Package: r-cran-monotonicitytest Architecture: arm64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 95198 MD5sum: de4465883f74415b9a64a7644b86c2ad SHA1: 3f6ac433562b66d6c321c6f195eea34f7a76fa57 SHA256: 2a25991ed7812dfc440cabb6dfeb7c67a6b5fd7436e771e2226ecafdc48242b9 SHA512: 404ece50b42f5f92047579c04cbb493e78c54bee69280c9b7d37778897ddda42ecaa9ee07f47fe10f4a7f0f4a6cb4a295a3b6dd0e2a82657a9bf6e2b76209867 Homepage: https://cran.r-project.org/package=MonotonicityTest Description: CRAN Package 'MonotonicityTest' (Nonparametric Bootstrap Test for Regression Monotonicity) Implements nonparametric bootstrap tests for detecting monotonicity in regression functions from Hall, P. and Heckman, N. (2000) Includes tools for visualizing results using Nadaraya-Watson kernel regression and supports efficient computation with 'C++'. Tutorials and shiny application demo are available at and . Package: r-cran-monreg Architecture: arm64 Version: 0.1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 137 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 46508 MD5sum: 07767b60466c3a96c1c641afc96c2d7a SHA1: a83e8eba7148f6bfabb7034586e0aa432ec3884c SHA256: 2ea4d19f2cc52822abfed71e8cfdd17ab27e5fa4c8d59198b58728b8e723f091 SHA512: 59b3bbfce6fe60211dbbf0dbe312ab0a087fdc6ca2b589d1ef9c84d8a1784a894d126b28d9b4f14442e944137b8aebcb16a83903d1323d283053e75b110b2071 Homepage: https://cran.r-project.org/package=monreg Description: CRAN Package 'monreg' (Nonparametric Monotone Regression) Estimates monotone regression and variance functions in a nonparametric model, based on Dette, Holger, Neumeyer, and Pilz (2006) . Package: r-cran-moocore Architecture: arm64 Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2367 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_arm64.deb Size: 1391976 MD5sum: 5b61321b775ebd6a944f3cc5c2e027a9 SHA1: 56af4dadf708be248163846466525535a9d0c162 SHA256: 70d7150ef91d86fea0bc3f96e1d65c443251a516b8e918d88602a6717fa7ed19 SHA512: ecda0b15e5536753cd8aea9962c8f7644887c7d9db7cf0b8ea8f7714636eed05fdfbc226eebd80ac7b097214cd2b870dd35b25c8a7e5e2c8df14f09310742923 Homepage: https://cran.r-project.org/package=moocore Description: CRAN Package 'moocore' (Core Mathematical Functions for Multi-Objective Optimization) Fast implementations of mathematical operations and performance metrics for multi-objective optimization, including filtering and ranking of dominated vectors according to Pareto optimality, hypervolume metric, C.M. Fonseca, L. Paquete, M. López-Ibáñez (2006) , epsilon indicator, inverted generational distance, computation of the empirical attainment function, V.G. da Fonseca, C.M. Fonseca, A.O. Hall (2001) , and Vorob'ev threshold, expectation and deviation, M. Binois, D. Ginsbourger, O. Roustant (2015) , among others. Package: r-cran-mop Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 232380 MD5sum: 045c6fd478acac9a9632b463231f59f7 SHA1: 1272a04a8b8ae266751b6cb0bf2c6aac71ba7983 SHA256: 185e115b0d370247f71cdeb38c97460d2c8b9afb183d5d31e50af5631b3e7850 SHA512: 7a74f5fd68375d69fd2485b3fd12f48bdc4f29d557377c188715ee89ac6182d483db8ec933266f223f455199b415b3ca0c336dba9233a3199f419ecd86123063 Homepage: https://cran.r-project.org/package=mop Description: CRAN Package 'mop' (Mobility Oriented-Parity Metric) A set of tools to perform multiple versions of the Mobility Oriented-Parity metric. This multivariate analysis helps to characterize levels of dissimilarity between a set of conditions of reference and another set of conditions of interest. If predictive models are transferred to conditions different from those over which models were calibrated (trained), this metric helps to identify transfer conditions that differ substantially from those of calibration. These tools are implemented following principles proposed in Owens et al. (2013) , and expanded to obtain more detailed results that aid in interpretation as in Cobos et al. (2024) . 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Package: r-cran-mpactr Architecture: arm64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7501 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_arm64.deb Size: 2769042 MD5sum: 7957df901e8fe9f2cd0c518ec8a2c118 SHA1: 3838628c9296bcf7006627a78ddeb8466765a7ed SHA256: 793ed5f520fad5d2797a278f97d0bf84e0b3ebcaf99ffaef2296e0b61e372c97 SHA512: b9819a2861f3280475fa293e118d90fd504a20c6b00c2671ae0e5d5e425207f2b63f23f42ca3b895949c68f664c7e5278dd0351a803bd3d402cf9e2bdf1584ae 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: arm64 Version: 0.4-2.26-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2567 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_arm64.deb Size: 2248486 MD5sum: 2e406d8ba892910f6eef27302dc31932 SHA1: 75b55f7d9f81a11bfdea9350bc1e1ada066d1eb3 SHA256: 35cc8c54f9cc1126f7a667b9a57ffe8230b9f677d4244acf10192115533dbbdd SHA512: 8c4f97521cf8603c9b760b695ff09340bab66e9ff6e888b52e687d9052a64bc8ae13deb63fe198ab436a61ad932b15f11396b36cc8a2de103219fc3ef78383ef 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: arm64 Version: 0.1-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: libc6 (>= 2.17), libgcc-s1 (>= 4.2), 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_arm64.deb Size: 274052 MD5sum: a13c93c749134b9b988c574fad6db078 SHA1: 2eb272a01e8d2118d881c0c4ac0ea584b68421fd SHA256: 57411deba83cd599b8996c6477e4f539fe8dd7f6031a064d3fe9f11ac692e89e SHA512: f8d12fe2478188a94e09a8dbb0aeb941ccc66dea205326bf72a8bd2f18ac0b872b2e21208e1ccd008e625e5f9a52659c1b6238cea8a8e9115cf8839f51f01b87 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: arm64 Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1778 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_arm64.deb Size: 695328 MD5sum: 95e17395c4062e8d005ad2b03bca1d7e SHA1: c579abb1d5b1fd346c8b8f6288a9b9dde6840a87 SHA256: b704482bae57d7535c3548c3ff8c4c0482f03a8de8668c5d50f3fc3a516ea583 SHA512: 75f7085caff4f5f0705fefba843914ea27c1e526b357547cf310b70c292b084c28c9b2f72e7ea3ee298072f6bd80cde697723f00e84485b06b818e52d6e7df2e 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: arm64 Version: 0.43.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: libc6 (>= 2.29), 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_arm64.deb Size: 225278 MD5sum: e749432deef11068c33c98907d9efa7c SHA1: f077eadbedbcba74c452580a3f1670da69a1c374 SHA256: 9036cd9b4f7e68bc6ceb622ebb3414dfa1acf57ff3d957e445f07ac4fff9bbe3 SHA512: 6cd4e3fe0ceff66e322f9b3b89ccc537f43a10076f06891e4feb831cdd14888290398db7bfa1981820e2a910e5253f3b05178bebf1d75179218436c84dbdc1e4 Homepage: https://cran.r-project.org/package=mpmi Description: CRAN Package 'mpmi' (Mixed-Pair Mutual Information Estimators) Uses a kernel smoothing approach to calculate Mutual Information for comparisons between all types of variables including continuous vs continuous, continuous vs discrete and discrete vs discrete. Uses a nonparametric bias correction giving Bias Corrected Mutual Information (BCMI). Implemented efficiently in Fortran 95 with OpenMP and suited to large genomic datasets. Package: r-cran-mpsem Architecture: arm64 Version: 0.6-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-magrittr, r-cran-mass Suggests: r-cran-knitr, r-cran-caper, r-cran-xfun Filename: pool/dists/resolute/main/r-cran-mpsem_0.6-1-1.ca2604.1_arm64.deb Size: 192614 MD5sum: bb4fc749c7517a75deceec0110a844c0 SHA1: 2a7a2af532260333d30ac5fe4b3efdb3ccec6ade SHA256: 3859195e891d57e0a297bfcb791aa7853e3206dde4cfe937bce4c1ca1994954f SHA512: 688dfe44432f7c628dcd0cb4ee99dd2d5a820c7e83412f91deffaa2db263fa78fdab120471acceb8d0cdb9975163687969244575a8dcdb983bb9ffad01f1716b Homepage: https://cran.r-project.org/package=MPSEM Description: CRAN Package 'MPSEM' (Modelling Phylogenetic Signals using Eigenvector Maps) Computational tools to represent phylogenetic signals using adapted eigenvector maps. Package: r-cran-mptinr Architecture: arm64 Version: 1.14.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1075 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_arm64.deb Size: 810280 MD5sum: 27a440aa477b6c93c46f7ed97e2620d1 SHA1: ef51f67b4fe46e970c391573e0709b04ec4e0990 SHA256: 47bed65110c16f3a913aac34c279e8cca0fc8160b8fd5b5b81f73bfdad1cc623 SHA512: dc87307d445dd6da88ea24de95fa16a9857e0dcf9b3ab63b6c34a0eb2a3a111750ec5643e39b399ce2603eb96aef55f687b2233e29f9230f66cf7d56f2c59a51 Homepage: https://cran.r-project.org/package=MPTinR Description: CRAN Package 'MPTinR' (Analyze Multinomial Processing Tree Models) Provides a user-friendly way for the analysis of multinomial processing tree (MPT) models (e.g., Riefer, D. M., and Batchelder, W. H. [1988]. Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318-339) for single and multiple datasets. The main functions perform model fitting and model selection. Model selection can be done using AIC, BIC, or the Fisher Information Approximation (FIA) a measure based on the Minimum Description Length (MDL) framework. The model and restrictions can be specified in external files or within an R script in an intuitive syntax or using the context-free language for MPTs. The 'classical' .EQN file format for model files is also supported. Besides MPTs, this package can fit a wide variety of other cognitive models such as SDT models (see fit.model). It also supports multicore fitting and FIA calculation (using the snowfall package), can generate or bootstrap data for simulations, and plot predicted versus observed data. Package: r-cran-mr.mashr Architecture: arm64 Version: 0.3.44-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1816 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 812256 MD5sum: 9ba81108896e4eb7594acfe371ff4439 SHA1: 4e5b4fe1859a2b647b3bc290c3a023410d06f595 SHA256: 692aed7a7bb3aa43e897eefc78b93733ce585a6e2af0f18098710817fefbf22d SHA512: 88fcb444040a5ca2f93cdfa0aa8ba387f22c7fb4ff6ab0be42ae02235c73dc7c611940776fc2089d7ddba23d136fca349fe1b71d92c8ba27fbc590b5b8841696 Homepage: https://cran.r-project.org/package=mr.mashr Description: CRAN Package 'mr.mashr' (Multiple Regression with Multivariate Adaptive Shrinkage) Provides an implementation of methods for multivariate multiple regression with adaptive shrinkage priors as described in F. Morgante et al (2023) . Package: r-cran-mr.rgm Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2344 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_arm64.deb Size: 866180 MD5sum: 4fc5e4e7335c47966266c24a5335388d SHA1: a615242ab16c2ab6e5ac3d5e0f78bbe92f4ae525 SHA256: 5ee02fd55caf3aa92f825e7436b3a3d42d1da84be14ae036f95f9c0ce3a3c91d SHA512: bd3ba1842e025a2d55ae456920731ade91025008317c0eb5b5e03e0f9420ff6b6a471f21ef0ef0c7883edfc217d6eb2217693399bae0104e4384750e3834c8b5 Homepage: https://cran.r-project.org/package=MR.RGM Description: CRAN Package 'MR.RGM' (Fitting Multivariate Bidirectional Mendelian RandomizationNetworks Using Bayesian Directed Cyclic Graphical Models) Addressing a central challenge encountered in Mendelian randomization (MR) studies, where MR primarily focuses on discerning the effects of individual exposures on specific outcomes and establishes causal links between them. Using a network-based methodology, the intricacy involving interdependent outcomes due to numerous factors has been tackled through this routine. Based on Ni et al. (2018) , 'MR.RGM' extends to a broader exploration of the causal landscape by leveraging on network structures and involves the construction of causal graphs that capture interactions between response variables and consequently between responses and instrument variables. The resulting Graph visually represents these causal connections, showing directed edges with effect sizes labeled. 'MR.RGM' facilitates the navigation of various data availability scenarios effectively by accommodating three input formats, i.e., individual-level data and two types of summary-level data. The method also optionally incorporates measured covariates (when available) and allows flexible modeling of the error variance structure, including correlated errors that may reflect unmeasured confounding among responses. In the process, causal effects, adjacency matrices, and other essential parameters of the complex biological networks, are estimated. Besides, 'MR.RGM' provides uncertainty quantification for specific network structures among response variables. Parts of the Inverse Wishart sampler are adapted from the econ722 repository by DiTraglia (GPL-2.0). Package: r-cran-mrbayes Architecture: arm64 Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3994 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_arm64.deb Size: 978298 MD5sum: e7e0eb86091c8a704fb65271b6bf3faa SHA1: 8e5dd25a4cb7e42d8800d5423bba014e5c5ad105 SHA256: 1b59d12f6baf62e627316dfc124a68eb961e5f1fc15d45c1443573221a94dce5 SHA512: 9bcf2aa8aae4ea014e60316b0a75f7f42e6050d12747747106c0e0bd039e6b1795007768215040ccccb83651d6dd4478d5f681514f0eb94ed1ba517e436e20da 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: arm64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1363 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1184594 MD5sum: b97cf1622709815adabc050bca910289 SHA1: b4847b99c67fa5fab9b75f3e6aa742d71e74bdf7 SHA256: 16c765caa6b975f73c487146372e01d11c44c9e625e812283c2901c0626f2ba5 SHA512: cf74edfad7bce84076c66366dc73d0650e5d2abe89afd620ffb656399e608d05b92f9554f9f74cdb3f4da90861150ba160ed59cd55921569782fff8fbc4a1686 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: arm64 Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 142 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-glasso Filename: pool/dists/resolute/main/r-cran-mrce_2.4-1.ca2604.1_arm64.deb Size: 45194 MD5sum: e7350c86d1309ad8189ec71e8d981317 SHA1: 2600eb8ff0bc3e82fc39ee6f1854fd39afa4ec51 SHA256: 125f4e1b397237bfc9e7d8793437909741a6f2095cb91847e21d557e24ee1965 SHA512: ab7ad1ee7eb81d2f4084317a97530f212dbd3940e96e46f4055563b8fbe4ee9f93c6033a0b5e2533ece9b56ef2c8d13ae20b6b2d8af9a86e378daf35e5421a6b 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: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 738 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 415540 MD5sum: 3d1fd8f558f20ebf0a494343b1df7e6e SHA1: f04276f1bdaa6582942659b4775608931f49f273 SHA256: b461cf5a3177b57a5bffeb3c43a4d788d71460f5da5d317b58f78140f845c89c SHA512: 48b0dfadcd226d86cd11ff5007a1216a17607318c297d8d3a770860e6499307f559e2ba3a4e44e183629793a66adc779af690e996a6112de79b73c55c4204bcb Homepage: https://cran.r-project.org/package=mrddGlobal Description: CRAN Package 'mrddGlobal' (Global Testing for Multivariate Regression Discontinuity Designs) Global testing for regression discontinuity designs with more than one running variable. The function cef_disc_test() is used for testing whether there exist non-zero treatment effects along the boundary of the treated region. The function density_disc_test() is used for testing whether there exist discontinuities in the joint density of the running variables along the boundary of the treated region. The methodology follows Samiahulin (2026), "Global Testing for Regression Discontinuity Designs with Multiple Running Variables" . Package: r-cran-mrf2d Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1146 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_arm64.deb Size: 819800 MD5sum: 6675d8bc8014da205a5a1af2859087ed SHA1: 6c0d6def2983678c59b329ff187bcd48e7f4598b SHA256: badc506b3fca0aedbffef6c991977577ce1c56f912af2e173e1b6c40c1127d0f SHA512: c371e668182059a05162972affe6c6f3ba3812c6e51f5628f9c67710eed0b45edcfbdea71092d3e9495998967c9abaa9ab33cc840e2dac2d9618c9e0a55cf05b 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: arm64 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_arm64.deb Size: 2199764 MD5sum: e69091dd7b88b40abb5a8734e8b27ef9 SHA1: 6c52580820b14dad36a85c2436fa2b2ace0402b7 SHA256: f02df6273f3c3f402f74985526352fc1a4dfcd202a031b09e22acad69e11041d SHA512: 5d4159299a570a53d0f0a6848139523abdd2dbdb41989a8034c8c2b8a17e1890f44982eba495c555b7d364852d7dcefda7d04704fc374e131938c2635437bc0f 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: arm64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 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_arm64.deb Size: 97590 MD5sum: 9d2adce5416f3686b6e1488348e3a5a1 SHA1: a7849a11ef0b16f99176eae48dde8f7a8052e469 SHA256: 848931e0d7749a486baa21f212b15bd9234d1eb6a4b725470fcb5c2a96082d35 SHA512: 13c29949244ed0b609fd76a25c79ae2af3c71d4837f6a93131b95dff40054265df120399b8ba5d177ee4254692a2d34749c1fa5a7459eb5b340ba8f90b507e96 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: 2619 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_arm64.deb Size: 1483906 MD5sum: caec915ac7bf010fe652888818033f06 SHA1: 6697d0a42648d8ce545239069c36219374fb5952 SHA256: 487a1ddd9de7f3a76b64e5266d5dae8fa20d71fcbe823099d2d9fbb05011e55f SHA512: a4c0349f7dcb091af92d55809354288b4f5d0c17d1c04d2d1b9401cfa530f5e43d4b16f555ad03b5d65289e9adbd81f8b6f0cda6ffed6388a5bd907f70daa68d 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: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1367 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 745020 MD5sum: 14c7f4bc9e240c1dc234cce67993d8cf SHA1: 4b8b015dc3f96f342df69329f7b5c9876bc1bcf9 SHA256: 206a4b61f05ca4e1f80ae3b52f49f94f181e9649b336760607cb35c4ae5136ca SHA512: da3ccfc7479815894a7c62f98eaa01f09c234b6e5e01b692197687f1b0201a5b820fed47234e28c5218331712f1e3f23aee66b4482806a76d575b3f48dde64cf 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: arm64 Version: 0.5-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1407 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1257484 MD5sum: 1439bd97a7012cdc67fbb399ae027887 SHA1: d43d23a665ffb18e07cc604535dd8d5bedb8299f SHA256: 41d3ba2b9c41c342cb688e206a9e1c5c29aa9d34ea3e3eeeac8972bd266b3514 SHA512: 3d08b63e8f639b74c992733a0e7957c6ef0b1afe9ea45d58b9b44f98dca62a368fd72d79222d2fe3883c7a11244711e7984ebce102baa5b6f239201864ff7861 Homepage: https://cran.r-project.org/package=mritc Description: CRAN Package 'mritc' (MRI Tissue Classification) Implements various methods for tissue classification in magnetic resonance (MR) images of the brain, including normal mixture models and hidden Markov normal mixture models, as outlined in Feng & Tierney (2011) . These methods allow a structural MR image to be classified into gray matter, white matter and cerebrospinal fluid tissue types. Package: r-cran-mrmlm.gui Architecture: arm64 Version: 4.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2979 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1284138 MD5sum: 03d890e3f0910df72324078081070ced SHA1: 709a79c1a1713191e16e5e77a2997c4b1bfc86cd SHA256: a957ab34bb00572ddc2a72d350722b1785ce8d79d8ddadb8a9d91e4e46776cfb SHA512: 0f654f4b9bd7df5aaac2ebbad0c3ce356c26f8fb34ff3a1174cea91abde81e992ee0b738256f07cb963a38a34249af4bc7bfc4ce650aa1c9be97d5584a1ac337 Homepage: https://cran.r-project.org/package=mrMLM.GUI Description: CRAN Package 'mrMLM.GUI' (Multi-Locus Random-SNP-Effect Mixed Linear Model Tools forGenome-Wide Association Study with Graphical User Interface) Conduct multi-locus genome-wide association study under the framework of multi-locus random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes and all the nonzero effects were further identified by likelihood ratio test for true QTL. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018) . Package: r-cran-mrmlm Architecture: arm64 Version: 5.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3023 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1312434 MD5sum: 7db5fd7736b0ecd0e4bdb1f2e2cca2e5 SHA1: 929791c3e592d83b00df1385c5f1ac4e10282b80 SHA256: 0d3684b90707d5a1965c8de7e04d132e9c947a6c411c2a1e8427e05f41bb1819 SHA512: 8aa4ae61dafb2e3b4a48e598b260ace5e9505d7db89a3563cf66deecffeb069fd8dc4e4c3ac527ddf16b563a5386e8ddb87c6928dc43708567d440a046458916 Homepage: https://cran.r-project.org/package=mrMLM Description: CRAN Package 'mrMLM' (Multi-Locus Random-SNP-Effect Mixed Linear Model Tools for GWAS) Conduct multi-locus genome-wide association study under the framework of multi-locus random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes, and all the nonzero effects were further identified by likelihood ratio test for significant QTL. The program may run on a desktop or laptop computers. If marker genotypes in association mapping population are almost homozygous, these methods in this software are very effective. If there are many heterozygous marker genotypes, the IIIVmrMLM software is recommended. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018, ), and Li M, Zhang YW, Zhang ZC, Xiang Y, Liu MH, Zhou YH, Zuo JF, Zhang HQ, Chen Y, Zhang YM (2022, ). Package: r-cran-mrmre Architecture: arm64 Version: 2.1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1907 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_arm64.deb Size: 1774440 MD5sum: 50f4654b184f9f9f4b42a17ac8b272aa SHA1: c9167653e54a9c7ff8ebc3d821753212437d96ce SHA256: 7a59364ea916b3e3e99ab360047d674f11c16f712f9d2d7ea728a61eefeb65e4 SHA512: 2245bfc94a12ca3455f42b63a594f3fe7b81f4385d5f8cb567677ae89cbfca9c8282d9909036e6e9d0ec6fc99804243ed3fff7a8fb47fb33e0f6b48a52962cc0 Homepage: https://cran.r-project.org/package=mRMRe Description: CRAN Package 'mRMRe' (Parallelized Minimum Redundancy, Maximum Relevance (mRMR)) Computes mutual information matrices from continuous, categorical and survival variables, as well as feature selection with minimum redundancy, maximum relevance (mRMR) and a new ensemble mRMR technique. Published in De Jay et al. (2013) . Package: r-cran-mrtssphere Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2305 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_arm64.deb Size: 1967424 MD5sum: 59d7201b5b0f7398fe49f06545787b4a SHA1: d43503908bbd57f350fbe443cf2618226f5c8aef SHA256: e0f01f6671be7eaf2e38f6f4dd92d67bc3550632974f9815bfeb4941c825ab26 SHA512: fa60f58427f05808770affd1ce72bc322bc21c5924fab5ece1fa59b724d81abb31582ee29e3ed0483345e819aae8d1cd78e7029e9d860ef113c7f5b6257e9cda 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: arm64 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_arm64.deb Size: 161050 MD5sum: d9f207a9b9fc74882e63605b01e44635 SHA1: 73165f35147cb705e46e2236b90235a0518305af SHA256: 99840ef4e0dbcc602ad20a4046c3e1ff2b359f142e75cfbd62f42e1d84c6a722 SHA512: ff827c4072d1f35195c71460d19e112e4160cc9854854e61cc24104183489d4c78bd490ee4e1547b5de918dedff1387f4c86833da0271c8fadf3e3b34d1660af Homepage: https://cran.r-project.org/package=msce Description: CRAN Package 'msce' (Hazard of Multi-Stage Clonal Expansion Models) Functions to calculate hazard and survival function of Multi-Stage Clonal Expansion Models used in cancer epidemiology. For the Two-Stage Clonal Expansion Model an exact solution is implemented assuming piecewise constant parameters, see Heidenreich, Luebeck, Moolgavkar (1997) . Numerical solutions are provided for its extensions, see also Little, Vineis, Li (2008) . Package: r-cran-msclassifr Architecture: arm64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3818 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 3772078 MD5sum: 60c6b2186f27871b9aea249ffaf75f41 SHA1: 02146412eb294fdae193bbf42ee46613c1352b19 SHA256: d5e470344e107c93b32a62bd25d6f0993978fa96aa565404927645fecfebf28a SHA512: 7bc15f5a026aede9324d20895f710e874e144ca1d1b0ea0ab9153c699a7a22d244c7ae9cb7b60cec7df7ca0415d744aa35acc231fd878908e72b3656dc74e197 Homepage: https://cran.r-project.org/package=MSclassifR Description: CRAN Package 'MSclassifR' (Automated Classification of Mass Spectra) Functions to classify mass spectra in known categories and to determine discriminant mass-to-charge values (m/z). Includes easy-to-use preprocessing pipelines for Matrix Assisted Laser Desorption Ionisation - Time Of Flight Mass Spectrometry (MALDI-TOF) mass spectra, methods to select discriminant m/z from labelled libraries, and tools to predict categories (species, phenotypes, etc.) from selected features. Also provides utilities to build design matrices from peak intensities and labels. While this package was developed with the aim of identifying very similar species or phenotypes of bacteria from MALDI-TOF MS, the functions of this package can also be used to classify other categories associated to mass spectra; or from mass spectra obtained with other mass spectrometry techniques. Parallelized processing and optional C++-accelerated functions are available (notably to deal with large datasets) from version 0.5.0. If you use this package in your research, please cite the associated publication (). For a comprehensive guide, additional applications, and detailed examples, see . Package: r-cran-mscmt Architecture: arm64 Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1174 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.6.0), r-api-4.0, r-cran-lpsolve, r-cran-ggplot2, r-cran-lpsolveapi, r-cran-rglpk, r-cran-rdpack Suggests: r-cran-synth, r-cran-deoptim, r-cran-rgenoud, r-cran-deoptimr, r-cran-gensa, r-cran-ga, r-cran-soma, r-cran-cmaes, r-cran-rmalschains, r-cran-nmof, r-cran-nloptr, r-cran-pso, r-cran-lowrankqp, r-cran-kernlab, r-cran-reshape, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-mscmt_1.4.2-1.ca2604.1_arm64.deb Size: 800096 MD5sum: db6a8b8273313ed168c0e44ad113143d SHA1: 5658b5b0a26181dad1eb2ca44ccd8dd2d6dacf8e SHA256: 3885a2a9b96d0cc0257dae4e3b87bcf34ba6c34a5b860acc28237e5fe211f6c5 SHA512: d560f008a8c2c129920f5252a39b4846e6beeb9bc3e9c30a1e328d5d83aae3437d008f0eaa99a3a3f210c998425531b5c34075c3719d28622fd83e9e7d0dd9b0 Homepage: https://cran.r-project.org/package=MSCMT Description: CRAN Package 'MSCMT' (Multivariate Synthetic Control Method Using Time Series) Three generalizations of the synthetic control method (which has already an implementation in package 'Synth') are implemented: first, 'MSCMT' allows for using multiple outcome variables, second, time series can be supplied as economic predictors, and third, a well-defined cross-validation approach can be used. Much effort has been taken to make the implementation as stable as possible (including edge cases) without losing computational efficiency. A detailed description of the main algorithms is given in Becker and Klößner (2018) . Package: r-cran-mscquartets Architecture: arm64 Version: 3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4781 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2354082 MD5sum: 960905c3d144ad0d89512fede51f239c SHA1: 3f59ab54c71a9f62f40bd02ed7aea62f8dee70de SHA256: 079fb0f9808d86192c99e4bd085dd03ea02ad2abe5d20d894e24e92366a4de40 SHA512: 2ec80dd493c21af7b281e31be69877e34579c55161cb0af74116ad4f4a2d17aa1894259c62debf363ea0f10a33d3695708714a8e382552a919dd063277528e6a Homepage: https://cran.r-project.org/package=MSCquartets Description: CRAN Package 'MSCquartets' (Analyzing Gene Tree Quartets under the Multi-Species Coalescent) Methods for analyzing and using quartets displayed on a collection of gene trees, primarily to make inferences about the species tree or network under the multi-species coalescent model. These include quartet hypothesis tests for the model, as developed by Mitchell et al. (2019) , simplex plots of quartet concordance factors as presented by Allman et al. (2020) , species tree inference methods based on quartet distances of Rhodes (2019) and Yourdkhani and Rhodes (2019) , the NANUQ algorithm for inference of level-1 species networks of Allman et al. (2019) , the TINNIK algorithm for inference of the tree of blobs of an arbitrary network of Allman et al.(2022) , and NANUQ+ routines for resolving multifurcations in the tree of blobs to cycles as in Allman et al.(2024) (forthcoming). Software announcement by Rhodes et al. (2020) . Package: r-cran-msda Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 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_arm64.deb Size: 181150 MD5sum: 3c227f6c23501c376d409f5dd3c19426 SHA1: 605e85d458950c91b119dc6c2a45f344d75df84a SHA256: 3d203e764bdf64428296a8954f1164c208f855acf78eb9a9a5c5ab58ac97131c SHA512: 268374e3a88cccb611a2b4e7956c7200326656f5a7e1d2f614b7be5100fd2df6e811302298f3f1ec7175aea256d4b52008fb2edfd858f7c58faa25e6b08db41f Homepage: https://cran.r-project.org/package=msda Description: CRAN Package 'msda' (Multi-Class Sparse Discriminant Analysis) Efficient procedures for computing a new Multi-Class Sparse Discriminant Analysis method that estimates all discriminant directions simultaneously. It is an implementation of the work proposed by Mai, Q., Yang, Y., and Zou, H. (2019) . Package: r-cran-msde Architecture: arm64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1212 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_arm64.deb Size: 409258 MD5sum: 8b897ece8916cd635214976e3f82f5a0 SHA1: 2a9a21e578275358b8c97383ed5dc1a30df51a58 SHA256: e1312f116b6f01721512d44936b8840f2a6745c675c85443b54b5db9f2eea929 SHA512: 2fb82ef47eb32743ad0d694863e8abe85b5a3feab0ac5162d38c3d1b07d87290cd0a90621524f9cce8538cb94e07d0f1f8490add7a1f0b2e000455f2f478f6fc Homepage: https://cran.r-project.org/package=msde Description: CRAN Package 'msde' (Bayesian Inference for Multivariate Stochastic DifferentialEquations) Implements an MCMC sampler for the posterior distribution of arbitrary time-homogeneous multivariate stochastic differential equation (SDE) models with possibly latent components. The package provides a simple entry point to integrate user-defined models directly with the sampler's C++ code, and parallelizes large portions of the calculations when compiled with 'OpenMP'. Package: r-cran-msentropy Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 187 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_arm64.deb Size: 59622 MD5sum: af605ca210d98c3e9a8eba1197d53f96 SHA1: 8a4d3afc911af0d8c6b4063de30affc36710fc71 SHA256: 437d5ff50c1deab718546518ebf14608d0bb27eb1208f783ac766dbcc0f263c0 SHA512: 100debd8b414dcc9533cbda45605374acba8fdaef55dd0fddbb75b9296d30643953f094739890e43ed2b4c3356e28dc23f849f358e9e738405208231d0c36d56 Homepage: https://cran.r-project.org/package=msentropy Description: CRAN Package 'msentropy' (Spectral Entropy for Mass Spectrometry Data) Clean the MS/MS spectrum, calculate spectral entropy, unweighted entropy similarity, and entropy similarity for mass spectrometry data. The entropy similarity is a novel similarity measure for MS/MS spectra which outperform the widely used dot product similarity in compound identification. For more details, please refer to the paper: Yuanyue Li et al. (2021) "Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification" . Package: r-cran-msetool Architecture: arm64 Version: 3.7.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7923 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_arm64.deb Size: 7440094 MD5sum: eb27fa5419dd8565ce8380b51f7ee633 SHA1: c30faae7cda9c6e4a68f98deef0721e94b17e7a1 SHA256: 85e5315ff18f79b9930b146fd558f4e71dd4807028043646bf608def25174d8a SHA512: f2e8321ee471bf0b4506e3f5587b86c9189e777d3b6c39f1cde39ed75c0b3381d4dad99180dfaaea852e534d9f39fb34ae633bc924ee9bcfdaa8af9af252a0d0 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: arm64 Version: 1.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 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_arm64.deb Size: 98928 MD5sum: 42010768db8264e78879aeeea92a62e1 SHA1: dfa437f4313d84b48410dd0eeedf5770350ccf80 SHA256: 10b65c74d5cf1d6cbd6f620541b7a83e99e655210dc2686030f68bd5d9760de3 SHA512: f98982243248b10be23e956824e9cdc190b30c2958c2f84c9f34da16861877c9ba1009d475949b6583b0d862fb0a17464f009612f920d1a7d45e1ca80c41c4f7 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: arm64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 132828 MD5sum: 6782f428cd4ad4261b86fc20f4ed3ec3 SHA1: 23cc2f8d51ec123838050c96831c9da55df8e80b SHA256: dec2e7802568d456d1052564da814777752d0a0b4f5f8aee02d9e9ca306ca788 SHA512: da070f7555e99c56d308cc5f2228ec048d0a8e24ff911a866b6abf3d62c27c850af947ed7ab32fa351d6d5998abf34a5651b6e5bb1b14d58bf5e176314836851 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. Package: r-cran-msimst Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-rcpp, r-cran-mvtnorm, r-cran-fields, r-cran-truncnorm, r-cran-rdpack, r-cran-rcpparmadillo Suggests: r-cran-lattice, r-cran-hdinterval, r-cran-latex2exp, r-cran-posterior Filename: pool/dists/resolute/main/r-cran-msimst_1.1-1.ca2604.1_arm64.deb Size: 368934 MD5sum: fb74eb936260396291cb718abead44ff SHA1: 2412b35f44b06a74a1018af9b526cfb5ecf3f3cc SHA256: f24c549b51c2df83925a5fd03f3071c30ebf66a239ae7f2c386a3fb921043163 SHA512: a2baab1ed5f8c86ff597e7f36ead992dd1315f8fa0560388dacb1039f3719ff447041b52a3255cb3f5fcd6dabecf5b15ee67fa7bd63e5358f64af23263662185 Homepage: https://cran.r-project.org/package=MSIMST Description: CRAN Package 'MSIMST' (Bayesian Monotonic Single-Index Regression Model with the Skew-TLikelihood) Incorporates a Bayesian monotonic single-index mixed-effect model with a multivariate skew-t likelihood, specifically designed to handle survey weights adjustments. 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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-mspca Architecture: arm64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 262 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), 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-mspca_0.4.0-1.ca2604.1_arm64.deb Size: 101856 MD5sum: e084600ae0123002c74f799157ea454b SHA1: 22bbdb2c3f4c21854ed64737fe53db9abe987741 SHA256: 548bcc3cb3181440c121807d01feecd07819fa0ccb6233d6450bd07731352076 SHA512: 42929a8169b5cb570d382777dd8854c54bfda08199229c4d05044de948c0912525cc5836bc31ab1aa27a126a64469b23b8f08bd0f64af5852ed82049df602e53 Homepage: https://cran.r-project.org/package=msPCA Description: CRAN Package 'msPCA' (Sparse Principal Component Analysis with Multiple PrincipalComponents) Implements an algorithm for computing multiple sparse principal components of a dataset. 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Package: r-cran-mssearchr Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 108974 MD5sum: b4bc992358fdaa929f9653ddb265a36f SHA1: cc1dc98ad0696406810406726fa807417638342b SHA256: 630bc99fa4f3ec6859b6c972890adfab721d079abcf97abe6c71bfe7c85d3933 SHA512: 57db6c67438bff3d4d1f95bbea8d98ffade57f296443d005b2469bd95414f71bbda021f16d5858af3386a001f12f9a6d188f291781775a79dc2dc00cb6469488 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-mtdesign Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 516 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 277826 MD5sum: 66bb2657de213ca935fe88e1db2b2c0a SHA1: 3f53ba08e1a6d368e1a1cc231cf8c16d598c1727 SHA256: 3910bae42aa3c4ca93f245e0d03ad46dc63981a06e559279e21f43c49ea10eff SHA512: cde491e4fc34c1215c3e648f0877b9e8a9ec398219594f0274f56c6c8af428893a1f4080b93a13e0f428320c4f66cdd9e2616ef62dcbc1d58e56f6ed4280864c 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: arm64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1077 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_arm64.deb Size: 729760 MD5sum: ee87b99179b9032af9b69ea261e712b7 SHA1: 4f0ee9df63b685c04bbf4ac3e10c6d4e08524846 SHA256: 5d4db468102cf6dc8dd8c78f6b1d5fe46db761c6e2b1397e65da2d5943a66acc SHA512: 727084ccfdc81d192996a2b6336485b93d9b5c3420989570d45db5ac8e1d8e531b4d2f9b96c6f14adb0450c6bbf4a8ff834fdce0915c5badf9c830c26ddb7353 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: arm64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1090 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 904308 MD5sum: 837027137fa79b6500cf4f49bb190498 SHA1: abb4cba2b1ab1fd5453c560331bb5ef5c7f5be84 SHA256: 62c8d2632d9a35244250a0355d50285434518883b8f3d73cc53deb07232b1fd5 SHA512: cac4ab44d3fceb2e2c54cc313fe632bcc0dcb0a63d86073be9aa41058e467bcd42ea93e40b336bb5467f9f8d3a5c249498ca3fd9b97128f822a31410cb2fc1b3 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: arm64 Version: 0.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 481 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 245928 MD5sum: 47f679d8fee280423a84f2c9ce70058d SHA1: 7493006426c551f6c4d877413e980d4f80d34965 SHA256: 42b714c47bf36789a01b0305dbccd5da53483037373cfe45423f9a7bd884baba SHA512: 6c70a58535ee9ca0894f24e52007f84ae7f2efd30b6a86792137002b78f6fec6456c1d5fc49c061e566be0ac8c16bd56a497e9eae74dd9fd33e30a95195b7425 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: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 438 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_arm64.deb Size: 222792 MD5sum: 7d6491ceffe0b648b936a497d9e7dfc2 SHA1: 87d7bc3dd4aa0b1a68e51588556e225f1874e2b1 SHA256: 03932ff43a1b496cc9a7b5c1971c8885b891be2806a5e526bee46ef091ebbfd2 SHA512: ec692f3bbd280582958a5f23698fe20df5b3c6a9a14ea786d49bf9838a102e4ca548b706fd3ac10d27d1455a0e7aab8684bbb3e71d45ca01508a10cb975f1aea 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: arm64 Version: 1.2.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-muhaz_1.2.6.4-1.ca2604.1_arm64.deb Size: 66046 MD5sum: 75ddc4cd08b5e4523781623c1dc42e81 SHA1: d82cc8eb54307498af16c0fca14b04ddefe72134 SHA256: f9102b970c7642cbb426c774189520408a449c04da0e166d89a45604cf5f9570 SHA512: c2d7f36947cde16ce1366c22cab56bf0d546fe343cbddc745b3f90826a2ba24355fc3967f9f82658be6a6a0619cd1f459d3766be1c1449f5c844bb741ccf5c65 Homepage: https://cran.r-project.org/package=muhaz Description: CRAN Package 'muhaz' (Hazard Function Estimation in Survival Analysis) Produces a smooth estimate of the hazard function for censored data. Package: r-cran-mulea Architecture: arm64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2439 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 911510 MD5sum: 7688265f3516375d3dcd3dc7ebcd0035 SHA1: 23b2a440d322fddec4e1177dfcaa30ba382c9c6b SHA256: 8d61e8737f84e96a1c6ef2863625387f376b92b09279599894e71014a08b46f2 SHA512: 11d4447af3b9b0280f9e748acd2aec4593b6d95e2a168ab7eb45e6033561e0f1d61dfc350e755d8e491e3505b66c9ab9a9a18762d93028d72ff238176f13f9a4 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: arm64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2654 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 757236 MD5sum: 098991b0c29e06ee08fad9833536be1e SHA1: fd8d5f0ea3e32a5a2b37e7843887beab63b6fb74 SHA256: fba786ddc81308cfb04561398dfab38f7d54cf3d6d44af25fcb245cecb0007f7 SHA512: 1f8398c1c354b5a7f5c2f6aee6de5f6c7b3f6a1ce17bba4e2c38eef0a01f8d6361ae61b72cdcaa576ee41dc8824fe057edbdf96117402cb7d67c5340c5d4beb5 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: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 406 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 164318 MD5sum: 496eac54673982d46d34bda307eca290 SHA1: 3595bae2ac1d33440d099a43326ab5fc652284c1 SHA256: f2d20a07722623dcbd1c0716f94241ca7c4026a389afc2c13966d7101541f468 SHA512: ec7f9404f56add627451f9831957a1faa5fda0cf6e26ea41dbe34f00d3c62c9094bc4d164f446375457f0a8300614273edf3a0f80d22787177d9cc59138e02bf 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: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 102436 MD5sum: 3def4f2338ba7e30a0ecf214fd23aba7 SHA1: 06c3da68d080467818b97d88a2f13a5231b64869 SHA256: 744733911363332b2110ca46f9a6d5660d1f7de44ab8f1232b50722276ed54c2 SHA512: 99813baf785b893465861bb451112f7df6b609616d8045c5c0818d14a717706f339135fafd7450ddbf8a5ce14dbf4428be27bf327226ea94714e6603956bc50e 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: arm64 Version: 1.1.1-1.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-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_arm64.deb Size: 425668 MD5sum: 39a28dc8fe3a193886ba86f2d5690ca4 SHA1: d7e34254b63bd82bb1b900a024b46d3c431d692e SHA256: d744cd68431fc57c389d9e8b1885faf6df5a52eaaeace7a0134fcf0298a8966e SHA512: 097f0932e62f18606abf43ad546b74aa8f47fe162345a8fc0d3e0a72e0f6a6f191c0589cdcefda9965eac629e4d6a027c8f49ca002b5ec3ec28be92079c6cb3c 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: arm64 Version: 2.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2181 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_arm64.deb Size: 1278246 MD5sum: 60a5d82ef9bbc59c5a3f0647b01c2234 SHA1: ca86bf8d20d3807b93d29f377bcf1ad274bde0a5 SHA256: 512083f81e1d98a4cbf4f6e87dc4ecc5209920fbe4fe94c29905cb9a00f23cbf SHA512: 8ec7e166b2608704d409397d29ab569a48c4cf36ae911fbe44a09d7098bfcdcc0dafd8d93b64f2ecb0d8c5756c2d17dcdfef83a1cd376f173d508124859ebfc9 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) . Package: r-cran-multilink Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 526 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-recordlinkage, r-cran-rcpp, r-cran-mcclust, r-cran-geosphere, r-cran-stringr, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-multilink_0.1.1-1.ca2604.1_arm64.deb Size: 291738 MD5sum: 9831fcb7f837f37a5b634cdcb7e9cfff SHA1: 47253c4e73613c541de6967781491f8da8d21bd9 SHA256: 0910411d03c1252ff80283f8ade14a0c707db971dd8b79833fa375742cbe9007 SHA512: 8c148c223bf0bad3f765c59c0140f54a851a35a4bbbb91bf4f4abf822ffc6dd375b6054393e6d6456eda6f3688c80b5c2be37f55eb87301a95538a18fafe3b8d Homepage: https://cran.r-project.org/package=multilink Description: CRAN Package 'multilink' (Multifile Record Linkage and Duplicate Detection) Implementation of the methodology of Aleshin-Guendel & Sadinle (2022) . 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: arm64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 229498 MD5sum: 3213e52293c957f4d9bb80cca640b0c7 SHA1: d2ffa488e76f383c933a508509584b81a30f9152 SHA256: 0de1e6030ff0e91caf5d08fce7cf10d8259895b45d4e380c2d0daf68f1a32b4c SHA512: 630c5ecc1ce0c06b40d32b707c9cebb98131bdb34146fb0724f756662a03654d2833a91d5fc006a12087a8f3d95cda81a67f8abd129360b692a4db550a11e65d Homepage: https://cran.r-project.org/package=multimode Description: CRAN Package 'multimode' (Mode Testing and Exploring) Different examples and methods for testing (including different proposals described in Ameijeiras-Alonso et al., 2019 ) and exploring (including the mode tree, mode forest and SiZer) the number of modes using nonparametric techniques . Package: r-cran-multinet Architecture: arm64 Version: 4.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2906 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_arm64.deb Size: 797118 MD5sum: 1bcd13e2b41c0fc36ccf5aef32e622a6 SHA1: 7af2e66cd0540447fa8737261af05cf0995235be SHA256: bd205f74b1e2051e954d9c86e221675014b06daa6c26bb104b97b33b011d9e39 SHA512: a25d8b1a211ea3698bf1ebdea06009807854b8e7168217f35517431e048eb3ef532f44a8fef86ec6a86fb8891a44b3c5aded52e3a7ac4912d675da15bac330bb Homepage: https://cran.r-project.org/package=multinet Description: CRAN Package 'multinet' (Analysis and Mining of Multilayer Social Networks) Functions for the creation/generation and analysis of multilayer social networks . Package: r-cran-multinets Architecture: arm64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 129454 MD5sum: 088b23964b31c972e119822f11aa4e39 SHA1: 449b092647294783985bc1099879da3d712947a3 SHA256: 28ea846ef9d661ec65e8d0962fdeacfb1d491ed9346054408c7a413c4e9c4c94 SHA512: 561fa1f533fc993443415847e4e7d25b950b4c1ae1cadf88956d55d4fd2f5806525cf5f71ceb49d418ea430625876f786f3d8f71cf0d45e1d15d4b56a8517d05 Homepage: https://cran.r-project.org/package=multinets Description: CRAN Package 'multinets' (Multilevel Networks Analysis) Analyze multilevel networks as described in Lazega et al (2008) and in Lazega and Snijders (2016, ISBN:978-3-319-24520-1). The package was developed essentially as an extension to 'igraph'. Package: r-cran-multinma Architecture: arm64 Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18938 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 6925820 MD5sum: c50dcb726812164f1b306e8acb3dbd93 SHA1: cb3cdb35ad5923f4b702619ba5375e168b3c8add SHA256: 130ba55352f25664e38923e1a8870167142caa55676bb8b1067fb88d08879fe6 SHA512: f78fb4ad0db957970af077f667e2274bf1f83c174e1b58f7aa777290cb40d6098e177a2d9ac47031a36bc277c433a293ce2bfb7665cac15c30cb23daf57df2a1 Homepage: https://cran.r-project.org/package=multinma Description: CRAN Package 'multinma' (Bayesian Network Meta-Analysis of Individual and Aggregate Data) Network meta-analysis and network meta-regression models for aggregate data, individual patient data, and mixtures of both individual and aggregate data using multilevel network meta-regression as described by Phillippo et al. (2020) . Models are estimated in a Bayesian framework using 'Stan'. Package: r-cran-multinomiallogitmix Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 307 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_arm64.deb Size: 184948 MD5sum: e0d9cf19a877b6c76509f98a235e8630 SHA1: 74f5023c00bffa9a5e30effa5b379a19126ceaaf SHA256: 5a6186dd30ede6ca8716f7ceba0bc702b05388c23af21707f83a169cc0f07783 SHA512: 775ebce53a0ef4a4244d907d773af83d9e4056ee35652da89f3d8928cfd478d9dcab96c8c977701e7b32fa745a8ba3562d19d70614a73254de8a01510a64bcce Homepage: https://cran.r-project.org/package=multinomialLogitMix Description: CRAN Package 'multinomialLogitMix' (Clustering Multinomial Count Data under the Presence ofCovariates) Methods for model-based clustering of multinomial counts under the presence of covariates using mixtures of multinomial logit models, as implemented in Papastamoulis (2023) . These models are estimated under a frequentist as well as a Bayesian setup using the Expectation-Maximization algorithm and Markov chain Monte Carlo sampling (MCMC), respectively. The (unknown) number of clusters is selected according to the Integrated Completed Likelihood criterion (for the frequentist model), and estimating the number of non-empty components using overfitting mixture models after imposing suitable sparse prior assumptions on the mixing proportions (in the Bayesian case), see Rousseau and Mengersen (2011) . In the latter case, various MCMC chains run in parallel and are allowed to switch states. The final MCMC output is suitably post-processed in order to undo label switching using the Equivalence Classes Representatives (ECR) algorithm, as described in Papastamoulis (2016) . Package: r-cran-multinomineq Architecture: arm64 Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1733 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 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_arm64.deb Size: 1336022 MD5sum: 89ac4cf43278998b346a2dc89b6b4368 SHA1: 4a80529d23d96c9932bfdd40f1c1d896f9662476 SHA256: e19b5db9e56b8c0c103abf5462a0db05d05d5729649a29a854d6285899195647 SHA512: 1f4da9b84e0be6b935eb00a163cae34db24a492913d5953378358d2dd7122c10191225ab2e1a776459ec03f8b3b18517789ca1bc7368f878838b0b230621e292 Homepage: https://cran.r-project.org/package=multinomineq Description: CRAN Package 'multinomineq' (Bayesian Inference for Multinomial Models with InequalityConstraints) Implements Gibbs sampling and Bayes factors for multinomial models with linear inequality constraints on the vector of probability parameters. As special cases, the model class includes models that predict a linear order of binomial probabilities (e.g., p[1] < p[2] < p[3] < .50) and mixture models assuming that the parameter vector p must be inside the convex hull of a finite number of predicted patterns (i.e., vertices). A formal definition of inequality-constrained multinomial models and the implemented computational methods is provided in: Heck, D.W., & Davis-Stober, C.P. (2019). Multinomial models with linear inequality constraints: Overview and improvements of computational methods for Bayesian inference. Journal of Mathematical Psychology, 91, 70-87. . Inequality-constrained multinomial models have applications in the area of judgment and decision making to fit and test random utility models (Regenwetter, M., Dana, J., & Davis-Stober, C.P. (2011). Transitivity of preferences. Psychological Review, 118, 42–56, ) or to perform outcome-based strategy classification to select the decision strategy that provides the best account for a vector of observed choice frequencies (Heck, D.W., Hilbig, B.E., & Moshagen, M. (2017). From information processing to decisions: Formalizing and comparing probabilistic choice models. Cognitive Psychology, 96, 26–40. ). Package: r-cran-multipledl Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1503 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_arm64.deb Size: 550586 MD5sum: 495e178356590908bd9aba34173d9c5d SHA1: a528ece76e165374ef2e207ce8477718cb2a23bf SHA256: 252153f27382dd7ea91f31c43c41afc0f49ab79a6e19626ca17d89ced66256f3 SHA512: 7d52e44866eeadb8c986769dbc999e1bdef44f86eb583944d578882d90ada674c1cea0a9a25fd72a39f40ea486b4ec935be40d10a2d2762597ee64e0755ee3b7 Homepage: https://cran.r-project.org/package=multipleDL Description: CRAN Package 'multipleDL' (Addressing Detection Limits by Cumulative Probability Models(CPMs)) Build CPMs (cumulative probability models, also known as cumulative link models) to account for detection limits (both single and multiple detection limits) in response variables. Conditional quantiles and conditional CDFs can be calculated based on fitted models. The package implements methods described in Tian, Y., Li, C., Tu, S., James, N. T., Harrell, F. E., & Shepherd, B. E. (2022). "Addressing Detection Limits with Semiparametric Cumulative Probability Models". . Package: r-cran-multipleoutcomes Architecture: arm64 Version: 0.16.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 551 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.6.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggpubr, r-cran-mmrm, r-cran-mvtnorm, r-cran-rlang, r-cran-sandwich, r-cran-stringr, r-cran-survival, r-cran-tidyr, r-cran-tidyselect Suggests: r-cran-asaur, r-cran-coin, r-cran-ibst, r-cran-invgauss, r-cran-jm, r-cran-joint.cox, r-cran-knitr, r-cran-momentfit, r-cran-numderiv, r-cran-pec, r-cran-randomforestsrc, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-multipleoutcomes_0.16.2-1.ca2604.1_arm64.deb Size: 419412 MD5sum: 7c431d10ec0937d57c43c083522f6a6d SHA1: 4d3461fc94dd62057b9a6c095d64cb12a9d569f6 SHA256: 7ff363ee7f609b40998186202fea0c28106864da6fe38ab66de2d4139f864da7 SHA512: b1cdcd2968a60b3f4b759b4a9f9e930acd0a2c485d1c72adb3e133d95404374736f125250a1238431bb47a11dde6a60c8ab778a1b4bb6c210a60c66735efc273 Homepage: https://cran.r-project.org/package=multipleOutcomes Description: CRAN Package 'multipleOutcomes' (Joint Covariance and Treatment-Effect Tests for MultipleOutcomes) Fits generalized linear models, Cox proportional-hazards models, log-rank tests, generalized estimating equations, mixed models with repeated measures, Kaplan-Meier curves, and quantile differences jointly across multiple endpoints, and returns the full asymptotic covariance matrix linking them. Implements PATED (Prognostic Assisted Treatment Effect Detection), a randomized-trial method that exploits balanced prognostic covariates to tighten standard errors and increase statistical power without introducing bias. Package: r-cran-multirfm Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 428 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 132426 MD5sum: 1a38ddaa8bed31be7523b70f582d5c91 SHA1: 7fe701b77d7dd86246aa802a35afe4904d990da2 SHA256: 41c33243ee73d4759b4f80a8f8de7ca0e756baaa69b8049c2601701578e1d682 SHA512: 0be28b0c4094e4d35c3cd5d65ff496e92966a732b3abfcf469921291423ec98b7fe706a8d134b511ebf2cb501bf70c98f0da55feb1b5af0388b1a9961db3686d Homepage: https://cran.r-project.org/package=MultiRFM Description: CRAN Package 'MultiRFM' (High-Dimensional Multi-Study Robust Factor Model) We introduce a high-dimensional multi-study robust factor model, which learns latent features and accounts for the heterogeneity among source. It could be used for analyzing heterogeneous RNA sequencing data. More details can be referred to Jiang et al. (2025) . Package: r-cran-multirl Architecture: arm64 Version: 0.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 946 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 747102 MD5sum: 7246204f825f34e991adca9d509b7d69 SHA1: 9f32c20ff78a2347255eaec01947e18ee8aad316 SHA256: f9f356239c154d520753c27f51058712126b310ba2e99a7000ce82abac35050e SHA512: f109396860c1f443822fd1194b0fbe662a13b4cb91ffe9d4db917996eebff4cd9f3507b247b41b3fc89158811f6e4b1dd3d476924098632065d9aa8ddb248ed1 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: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6291 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_arm64.deb Size: 5058176 MD5sum: e80b87107734b9537c0334389b4585ca SHA1: 4602a11079cc1629580e686903c97b2f9d761f46 SHA256: f7d017e56f10a97d75f1ad9d5d1b188a184006109aecf3be01b4201c32a62454 SHA512: 4dc1e0aa91d0e78b1f01dc9281386ce542910aa739a3d506c1b6fd1fc7fb41a085b4f6b7f9185e3ee3f4fc247bebe6cc49c2e69ae4b3b8c3b5bdd0b7e55047da Homepage: https://cran.r-project.org/package=MultiscaleDTM Description: CRAN Package 'MultiscaleDTM' (Multi-Scale Geomorphometric Terrain Attributes) Calculates multi-scale geomorphometric terrain attributes from regularly gridded digital terrain models using a variable focal windows size (Ilich et al. (2023) ). 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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: arm64 Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8112 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 7049678 MD5sum: 2a082a3c7d95b352fbe96992293d7b49 SHA1: a2ed92bd159081313a7bce9f373edbd5b3e565db SHA256: b21917fc12e05da11f46f1da4f0ae0cef72a17a6b753f3402d2fab2475949716 SHA512: 4478d6b62bd5279057850a1acdf6f577152df9f9eaf0d559a2d7b61f151ad1391c6c5a0c28f4eac5a493ff400df248d7c3ae3d2898dc7c1caa936c84d22a87ae 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: arm64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-multispatialccm_1.3-1.ca2604.1_arm64.deb Size: 53258 MD5sum: 6769e80f136721721f7ea807b04a7938 SHA1: fbf82157356a9335ff6971605f43ae5a2ff33492 SHA256: 9504b93d526bbfa6380f780e1ae59847fcc0df3b33454b33101060cc69775b79 SHA512: 0ac04a6780070e593922da41efc79b7d727364665cb0a3f9e18cf2c6e20ac18e9cbfaef2aee1a003177b9e1e8ae658210bce9b9e0f0e4e31b5fa1a2ec487e8a7 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: arm64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 629 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 373560 MD5sum: 6bcdfd8c0e6d1a53b5f5dd51e3d357f2 SHA1: 4c6fcc0d21c75551c5cf33a78fd4270a19d6d4b9 SHA256: e3c59f05b974eb0a0430a772f9466eea0193d39dc42f1dfe05a720cfd7719fe1 SHA512: 076aae3f61157895cdac93aa14ca0da0fd2277f0a3b24b874d4e9a06729f222f343b2141b31300cd1012afa5f199a311b6947e60c5ab114f48587f0dc2e2f7b8 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: arm64 Version: 1.0-17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 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_arm64.deb Size: 284298 MD5sum: 17e9368f07ad84320f7a0b014ebff7b7 SHA1: cde35997f4062fbbcac04ff1ca6b71e737b56048 SHA256: bed05841c7ade6b60d8b66fb359443ee8f62d7e3147aa2fd6c2466b313e3964a SHA512: 4ae2cbe81409a19a4dda8ff27efeb952a0cdff6ea55df8c0a7e37b3c7025fe02069b9e04c13b53ba3803e1f26d9f85241ee82dac313804299e56b3206eb62f3e 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: arm64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 994 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_arm64.deb Size: 744422 MD5sum: f28d418b000648fefaf0dc3a8d36bf01 SHA1: f937f4c5d82d653a531c1f3d1ca59cdfe043fb05 SHA256: f766a803e23695f8198d4ec9040f88924c6bb592cd3f2e869da7ab9865016b41 SHA512: dd395d251b658a655778e6a477238f5a156d1b41fe8ea14484f916f4286d3ecf2a0436bc548f54f6476d1371a26b0d0a0ecd18c3e7636ece43880f55ca63cdd9 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. Decomposes dynamics into shared (common) and subject-specific (unique) components via adaptive LASSO with FISTA optimization. Supports cross-validation and extended BIC model selection and subgroup detection, and time-varying parameters. Package: r-cran-multivariance Architecture: arm64 Version: 2.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 496 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_arm64.deb Size: 353098 MD5sum: ea59b760a3bb6ea8c18c6287afd157a3 SHA1: e301f25adb8ec40618ca21e8e9c477cffff62072 SHA256: a38b9dcb45927c4cf8758fbf7d8f8e3b56e87fcb54bd114a3fa82c235fdda8ac SHA512: 4a88527e45f315eef8d71ebc546c526678c2a0483b3c7965c2a8eceb3c0ca53566b53e15a901e8d04512cb784ccf410322ed18e979e28449b652655d76dd6267 Homepage: https://cran.r-project.org/package=multivariance Description: CRAN Package 'multivariance' (Measuring Multivariate Dependence Using Distance Multivariance) Distance multivariance is a measure of dependence which can be used to detect and quantify dependence of arbitrarily many random vectors. The necessary functions are implemented in this packages and examples are given. It includes: distance multivariance, distance multicorrelation, dependence structure detection, tests of independence and copula versions of distance multivariance based on the Monte Carlo empirical transform. Detailed references are given in the package description, as starting point for the theoretic background we refer to: B. Böttcher, Dependence and Dependence Structures: Estimation and Visualization Using the Unifying Concept of Distance Multivariance. Open Statistics, Vol. 1, No. 1 (2020), . 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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: arm64 Version: 1.3.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 264 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 158268 MD5sum: 5cc3cf31ab6059d5bf3497a0820b9235 SHA1: 6e59e83dfc8e3af88b8985a26266c48ffa8d695f SHA256: 1c08038b9726a03a9a5227a90f34c266976baf711ef10cb280c0da78c09a9a10 SHA512: fa7f30466f258156dec9d2b923b0a4c30973597985cbeb3490a3dce05d0db4e73ec7af9ae8e480f1feeb331714b463682c56562469ef02dbcca655a85f1e0daa 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. 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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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See 'mvabund-package.Rd' for details of overall package organization. The package is implemented with the Gnu Scientific Library () and 'Rcpp' () 'R' / 'C++' classes. Package: r-cran-mvar.pt Architecture: arm64 Version: 2.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 485 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 395286 MD5sum: be600b27f87edaa272caef088838dd35 SHA1: 57fae9f2592acf76ab6f07a62c02e6341663498f SHA256: 5a461bd3d3c677cf81987400ecd1d92c0819f37ccabfe8967fbb52c795a721d9 SHA512: 3c11ee2329eb3394351ea81ad3d0998c6e37ee2e99ca2f6e5ca4f5b957856d78094dd0b74f2310d2da5e5593bda7f97ab62b0f48ae75dba267f7245e747b33d9 Homepage: https://cran.r-project.org/package=MVar.pt Description: CRAN Package 'MVar.pt' (Analise multivariada (brazilian portuguese)) Analise multivariada, tendo funcoes que executam analise de correspondencia simples (CA) e multipla (MCA), analise de componentes principais (PCA), analise de correlacao canonica (CCA), analise fatorial (FA), escalonamento multidimensional (MDS), analise discriminante linear (LDA) e quadratica (QDA), analise de cluster hierarquico e nao hierarquico, regressao linear simples e multipla, analise de multiplos fatores (MFA) para dados quantitativos, qualitativos, de frequencia (MFACT) e dados mistos, biplot, scatter plot, projection pursuit (PP), grant tour e outras funcoes uteis para a analise multivariada. Package: r-cran-mvar Architecture: arm64 Version: 2.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 484 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-mvar_2.2.9-1.ca2604.1_arm64.deb Size: 394642 MD5sum: 9f47b45c5cc773e49566b9242e00228f SHA1: b8c1e896b5ec72fa3165b7869deb1d4f3f89b070 SHA256: 09567da96203e6f76fd8c8437613d42656d576007dc4ff86f78fbfe13e9e96a0 SHA512: 5e9802a841a23939b68361f6511970420b0689d9af78ce1a424b1d350faf3ca34daa4d484d31042259140a67c87c80f42ea1a80aaf7fe4fbd3b66cd97f6698f6 Homepage: https://cran.r-project.org/package=MVar Description: CRAN Package 'MVar' (Multivariate Analysis) Multivariate analysis, having functions that perform simple correspondence analysis (CA) and multiple correspondence analysis (MCA), principal components analysis (PCA), canonical correlation analysis (CCA), factorial analysis (FA), multidimensional scaling (MDS), linear (LDA) and quadratic discriminant analysis (QDA), hierarchical and non-hierarchical cluster analysis, simple and multiple linear regression, multiple factor analysis (MFA) for quantitative, qualitative, frequency (MFACT) and mixed data, biplot, scatter plot, projection pursuit (PP), grant tour method and other useful functions for the multivariate analysis. Package: r-cran-mvgam Architecture: arm64 Version: 1.1.594-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10470 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 9084312 MD5sum: 53d692e617fa54cc96750978eb282870 SHA1: cee95d9eb9e926524ebe9cb26f136b8916c159af SHA256: d33b84eed6273cae763117f9fd4b83f6e225eac5b325d71a933ded0f78a142ed SHA512: 6711687efa6478dae3be3d16380e8a6a2fba9e887e6493ef820c42072d2d5a9b37c2357aae4f288f300a07a183e68091855074c622a286169ee0f067c9a000b1 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: arm64 Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1357 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1205344 MD5sum: bb102543a6dc027f2f7f4525b88ce247 SHA1: 41fbe80b15793d6b378a2d18b8cb353def235810 SHA256: 7e517aeb6cb119962be4677bb7ebfabecbebd32a0b7202c1edbdcb8e7bc2a90e SHA512: 0d565a2c98cdfee4b02816cf1a8e2f18eb687bc75795b337a4a4aa2421714864f405c767457c47ad7a550754f55d3648b2f5c96a54e76ca58eb82ba4ea00bf0b Homepage: https://cran.r-project.org/package=mvLSW Description: CRAN Package 'mvLSW' (Multivariate, Locally Stationary Wavelet Process Estimation) Tools for analysing multivariate time series with wavelets. This includes: simulation of a multivariate locally stationary wavelet (mvLSW) process from a multivariate evolutionary wavelet spectrum (mvEWS); estimation of the mvEWS, local coherence and local partial coherence. See Park, Eckley and Ombao (2014) for details. Package: r-cran-mvmapit Architecture: arm64 Version: 2.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2108 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 1054290 MD5sum: bd27d5fa46e275eef18361f3ce8adb0f SHA1: 4c73b7d3c15c5fe7ad3f982aaa53f301532090fc SHA256: 3d6933099d7d221c141d45a9df63b578c46c737a74e939ccdba4dcd59a6042de SHA512: 17755de0255f0d7eacb30ba72c2401f8df86e30a89f39bf1626a62f4e680e4abbf15ca9ce2198685dbf7fab2b656c67ffc90904e7266bf46ddbf6d529a6a8139 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) . 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Package: r-cran-mvpot Architecture: arm64 Version: 0.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 208 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_arm64.deb Size: 119798 MD5sum: 841f40fea7ca6a7adf6a2bdf2b8cd6da SHA1: 63726c2ecdba5602a53455f3be3705ea530434d6 SHA256: 368cf726e913840e04bb2166c2bedfe7ddf77f18e8e57219ed73e3d3741a4f23 SHA512: ad27e158cf0855a2b99fb11e5cdcb99d6f60e335cc92ec0063a4a77372a20688c9438ed29d0f714373a070450c44d32fa6a4c6d9dc783a84d5786f10b2940143 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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(2018) ). This package aims to provide a transcriptome analysis environment to quantify the average evolutionary age of genes contributing to a transcriptome of interest. 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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. 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Package: r-cran-nabor Architecture: arm64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 526 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 150840 MD5sum: b44a67121524a4dc92604f9f0ed65b27 SHA1: 843cd7576cefb01028770e3a4ef3e1b88cdd62f3 SHA256: 7d07c10e80150193fc35817c1b21a3ffd09bf388179234f176140fcb712cb61f SHA512: 8659037300d7f824a9aad2ac7870e34d947a1c1643321f0c2c41bd936fe9889e9c9a7356917327504161d34f4ced924ef84b1d3124ffa90f697ee677a857f570 Homepage: https://cran.r-project.org/package=nabor Description: CRAN Package 'nabor' (Wraps 'libnabo', a Fast K Nearest Neighbour Library for LowDimensions) An R wrapper for 'libnabo', an exact or approximate k nearest neighbour library which is optimised for low dimensional spaces (e.g. 3D). 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Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (). 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Package: r-cran-natural Architecture: arm64 Version: 0.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 301 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_arm64.deb Size: 169074 MD5sum: 8989c76ab5dfb9e54d0feab5c355c873 SHA1: ddd39d0749abee8eeb6725f2e39bb17d25aa8711 SHA256: 51077bf782a3e04fe4dd144c5cd01182888cf0776a266ff3d4e4e3a8ad3c9703 SHA512: 2a3f2ea167bc6a95c9313f58ce77361ab79ece46b4919b87c522ad7e5ec4621e2dceca65627fa44ad7bd04200947807429304f91fd8d455a648f140149d01769 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: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9014 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_arm64.deb Size: 3569220 MD5sum: c05df41861e0eaa10bea78ffd0ddc952 SHA1: 258309a70bebfb6e787b009cf4eb49de514f97df SHA256: d0be0c1fba27293147e2e1085a47162ca602cfe8f30aa23c0eda6e30fdcec7b4 SHA512: 60784b3f807cdb2d78d5f0da21e344250a5917acc045a423f9fcf96683a260bad3841dee3554a6f0274ad9e9e2ae971356d10bbd3c8b273883b3aa3b822dd60a Homepage: https://cran.r-project.org/package=navigation Description: CRAN Package 'navigation' (Analyze the Impact of Sensor Error Modelling on NavigationPerformance) Implements the framework presented in Cucci, D. A., Voirol, L., Khaghani, M. and Guerrier, S. (2023) which allows to analyze the impact of sensor error modeling on the performance of integrated navigation (sensor fusion) based on inertial measurement unit (IMU), Global Positioning System (GPS), and barometer data. The framework relies on Monte Carlo simulations in which a Vanilla Extended Kalman filter is coupled with realistic and user-configurable noise generation mechanisms to recover a reference trajectory from noisy measurements. The evaluation of several statistical metrics of the solution, aggregated over hundreds of simulated realizations, provides reasonable estimates of the expected performances of the system in real-world conditions. Package: r-cran-nbody Architecture: arm64 Version: 1.41-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 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_arm64.deb Size: 115364 MD5sum: bc7dcda2f68b09d1337fd9dfb1484643 SHA1: f009a548733e52dd8c31955036fb4f4ef43c2be3 SHA256: 6e04b0fcaa0e08404f2ba6595fae2f47d0d00497a3a8a3f30f8288b251b9f6fc SHA512: ba47a46fe4e76dc353111165bdbd64c1d2a6c80da921016ce66c8b68796ea42cd9e82a836e6ba41e43df0fac39361dc62ce41b47ea500ff57953011f774d1db5 Homepage: https://cran.r-project.org/package=nbody Description: CRAN Package 'nbody' (Gravitational N-Body Simulation) Run simple direct gravitational N-body simulations. The package can access different external N-body simulators (e.g. GADGET-4 by Springel et al. (2021) ), but also has a simple built-in simulator. This default simulator uses a variable block time step and lets the user choose between a range of integrators, including 4th and 6th order integrators for high-accuracy simulations. Basic top-hat smoothing is available as an option. The code also allows the definition of background particles that are fixed or in uniform motion, not subject to acceleration by other particles. Package: r-cran-nbpmatching Architecture: arm64 Version: 1.5.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 452 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 234228 MD5sum: 25b7fbb624a468644c34326ddeed94b8 SHA1: a1115b47235f5d1630d69ddf0511396471f39a9b SHA256: aa961207da2816de398e0b1447bb35975d307697498b1b8d5837a3d7f6b60dc5 SHA512: 206b4dcbc2d44c1f230a1b8729842c2de79d8726305e6018198d34d366c27236ae4a771c54abe52f510934f879df3cd73466e82efbeef4a1a1208c503a570b75 Homepage: https://cran.r-project.org/package=nbpMatching Description: CRAN Package 'nbpMatching' (Functions for Optimal Non-Bipartite Matching) Perform non-bipartite matching and matched randomization. A "bipartite" matching utilizes two separate groups, e.g. smokers being matched to nonsmokers or cases being matched to controls. A "non-bipartite" matching creates mates from one big group, e.g. 100 hospitals being randomized for a two-arm cluster randomized trial or 5000 children who have been exposed to various levels of secondhand smoke and are being paired to form a greater exposure vs. lesser exposure comparison. At the core of a non-bipartite matching is a N x N distance matrix for N potential mates. The distance between two units expresses a measure of similarity or quality as mates (the lower the better). The 'gendistance()' and 'distancematrix()' functions assist in creating this. The 'nonbimatch()' function creates the matching that minimizes the total sum of distances between mates; hence, it is referred to as an "optimal" matching. The 'assign.grp()' function aids in performing a matched randomization. Note bipartite matching can be performed using the prevent option in 'gendistance()'. Package: r-cran-nbpseq Architecture: arm64 Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 425 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 327218 MD5sum: 30d58311ae0b7a0a1ca5154a5adde667 SHA1: c9163ada940c241cc6e50dda02834e24caf0d9fb SHA256: 36babf6b9c29014db00dc34a076ee27ee13666f731f02d0600745801a39b147a SHA512: 86f5741c5cf669dde25f13edd9d6e1fbe1dce4bb029079a17157632c636da2068caabae0c708c048b4b708c0c8901d96485f41df46b36ddd35229fea25d7d30a Homepage: https://cran.r-project.org/package=NBPSeq Description: CRAN Package 'NBPSeq' (Negative Binomial Models for RNA-Sequencing Data) Negative Binomial (NB) models for two-group comparisons and regression inferences from RNA-Sequencing Data. Package: r-cran-ncdf4 Architecture: arm64 Version: 1.24-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 277626 MD5sum: 9c3fd04568090e785177d33962e6e241 SHA1: b3a910ce4e0834f303728de1fc073b001fa7c7cc SHA256: 3c9d1b058c3d88456799328fd86e7578315a98eaefecd87994c1113af1b40ba5 SHA512: 173ae04241cc3d046e3e35a0cbb3e14ef51eecbe10313a00324e26b0b172dc2f3d389d1fe3f8eee419c6710f1cbb1eee8bf4ed8e0a3a1ad77f4ffb38aaf1e5cb Homepage: https://cran.r-project.org/package=ncdf4 Description: CRAN Package 'ncdf4' (Interface to Unidata netCDF (Version 4 or Earlier) Format DataFiles) Provides a high-level R interface to data files written using Unidata's netCDF library (version 4 or earlier), which are binary data files that are portable across platforms and include metadata information in addition to the data sets. Using this package, netCDF files (either version 4 or "classic" version 3) can be opened and data sets read in easily. It is also easy to create new netCDF dimensions, variables, and files, in either version 3 or 4 format, and manipulate existing netCDF files. This package replaces the former ncdf package, which only worked with netcdf version 3 files. For various reasons the names of the functions have had to be changed from the names in the ncdf package. The old ncdf package is still available at the URL given below, if you need to have backward compatibility. It should be possible to have both the ncdf and ncdf4 packages installed simultaneously without a problem. However, the ncdf package does not provide an interface for netcdf version 4 files. Package: r-cran-ncpen Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 584 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 298978 MD5sum: dca85e591518bc0a39bd6ae59bc7a2d0 SHA1: b1718ba6fbea289ca0bb2c6b46d180ebc2f3d7a7 SHA256: 6d5f006d5c1c359b2136a4528c83d18aba3729834972a33025c3e3383ed880f0 SHA512: 157f676efb7362ede614226c06d270c1a07b3bc2fdd47dc0c28490524443f867a5dc1dd07043bfe070e395a8e8b6190f690a53a49c108d1827009d7f348722a2 Homepage: https://cran.r-project.org/package=ncpen Description: CRAN Package 'ncpen' (Unified Algorithm for Non-convex Penalized Estimation forGeneralized Linear Models) An efficient unified nonconvex penalized estimation algorithm for Gaussian (linear), binomial Logit (logistic), Poisson, multinomial Logit, and Cox proportional hazard regression models. The unified algorithm is implemented based on the convex concave procedure and the algorithm can be applied to most of the existing nonconvex penalties. The algorithm also supports convex penalty: least absolute shrinkage and selection operator (LASSO). Supported nonconvex penalties include smoothly clipped absolute deviation (SCAD), minimax concave penalty (MCP), truncated LASSO penalty (TLP), clipped LASSO (CLASSO), sparse ridge (SRIDGE), modified bridge (MBRIDGE) and modified log (MLOG). For high-dimensional data (data set with many variables), the algorithm selects relevant variables producing a parsimonious regression model. Kim, D., Lee, S. and Kwon, S. (2018) , Lee, S., Kwon, S. and Kim, Y. (2016) , Kwon, S., Lee, S. and Kim, Y. (2015) . (This research is funded by Julian Virtue Professorship from Center for Applied Research at Pepperdine Graziadio Business School and the National Research Foundation of Korea.) Package: r-cran-ncutyx Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2536 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2183952 MD5sum: cc32257a292ea2e3b04c55f9d1162e51 SHA1: 6f85a3183a5e96b8085a2ec79f3f0e08d5450aa6 SHA256: 9c7db4e2ef535ddbea8d1f2ba6416270a86ae2c49e27d2a4b8533fdd48f78935 SHA512: 36c337f943fed3d1c500b7ac1cf0e711529fe0a651ab5df5381a0472544b86cc99e29a2b7b312c37f94395f526c3e940003d39eaf81bd2d88fa71e53da4f2c54 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. 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Package: r-cran-ncvreg Architecture: arm64 Version: 3.16.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 544 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-ashr, r-cran-knitr, r-cran-rmarkdown, r-cran-survival, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-ncvreg_3.16.0-1.ca2604.1_arm64.deb Size: 369098 MD5sum: 643f52e65600b6aaa097b35883d7a0c3 SHA1: c57075255d82f64cbe75d063d28e3a9aeee96c87 SHA256: fad74f8480143bff3b81d7984def047fdebbb03946997ded00813fa2e7cbca78 SHA512: f45dd17c193fe6036fcf35504759aae2436f38bbc73ca25b34a20db8fabde5f1c473b21cb1fec5509640c3a01bd2ac658471788dc9023654fb03a7d3af51ec1f 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: arm64 Version: 0.2.18-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 652 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_arm64.deb Size: 443946 MD5sum: 5f5535e6786980c127fe724840b517ba SHA1: 10d93aa5044b5fcfe03b4b40601b2c43c263b5d0 SHA256: 59a12995b069f9bb9aa11a4014b1e88f1a41ac181dbd0112d9eb635caf9705dd SHA512: 4530bacc5e5eb05a3b3bc4be3cf24c753c7b5882059e59c5dc11415c2c1e869212f1805d180701ebce6b77c3e429fa4d8c7fa67d34137bf54c227ffcfd3bea76 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: arm64 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_arm64.deb Size: 115460 MD5sum: e788355018c82150c7a2c7e227e53f8f SHA1: 155bf38a7035fd0afc9c55971cec6f0cc296def0 SHA256: 4892bb62586dfc0df466eaad8c0b3dab3a7d9d2fd0a40e09d9aa3aa70095611e SHA512: ab6741dcbdfb3549d4878580f9265402b77fcd09053cfddb556656f06a3e543291f3be5b2b7efef88bde089ca9ed19c6d48e225c574a2e21fd588fafe8d18afc Homepage: https://cran.r-project.org/package=NetVAR Description: CRAN Package 'NetVAR' (Network Structures in VAR Models) Vector AutoRegressive (VAR) type models with tailored regularisation structures are provided to uncover network type structures in the data, such as influential time series (influencers). Currently the package implements the LISAR model from Zhang and Trimborn (2023) . The package automatically derives the required regularisation sequences and refines it during the estimation to provide the optimal model. The package allows for model optimisation under various loss functions such as Mean Squared Forecasting Error (MSFE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). It provides a dedicated class, allowing for summary prints of the optimal model and a plotting function to conveniently analyse the optimal model via heatmaps. Package: r-cran-network Architecture: arm64 Version: 1.20.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1013 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-magrittr, r-cran-statnet.common Suggests: r-cran-sna, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-network_1.20.0-1.ca2604.1_arm64.deb Size: 818940 MD5sum: 17671002732c7fbcc1555a201660f2db SHA1: 097ba56541e105b5c96558696e3fb892cd9d8bf2 SHA256: 19c919f476904a6170897f730b4e27ad2f37504968592a7465d96906f133774b SHA512: 948cf81722c5ca764ba503f15bf12fff126b47e53dd5d6237ae17090374c5b42e1f74e1ee9c9b61b822e3904a908d556539e396b224d47360d76db0bbc8a5290 Homepage: https://cran.r-project.org/package=network Description: CRAN Package 'network' (Classes for Relational Data) Tools to create and modify network objects. The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes. Package: r-cran-networkabc Architecture: arm64 Version: 0.9-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 568 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcolorbrewer, r-cran-network, r-cran-sna Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-networkabc_0.9-1-1.ca2604.1_arm64.deb Size: 309306 MD5sum: dc9b15e5fe816f8ac02ac5693620b0c9 SHA1: 20774fcc606b53a106a57c6710399d418e1f1778 SHA256: 1e0ba1996158233862eea9e76a87176ea2f3eadc116453fbdcbdc89cf61e1b86 SHA512: 88fa3b9190e12fcc525a2433eba8919d07e41527dab31d29e70ec53bd77854e893ff379ce2d78841ffedadf0fc05d5765031b4e4107c3341d573e83136bac639 Homepage: https://cran.r-project.org/package=networkABC Description: CRAN Package 'networkABC' (Network Reverse Engineering with Approximate BayesianComputation) We developed an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of the inferred links between network's actors. It is a new multi-level approximate Bayesian computation (ABC) approach. At the first level, the method captures the global properties of the network, such as a scale-free structure and clustering coefficients, whereas the second level is targeted to capture local properties, including the probability of each couple of genes being linked. Up to now, Approximate Bayesian Computation (ABC) algorithms have been scarcely used in that setting and, due to the computational overhead, their application was limited to a small number of genes. On the contrary, our algorithm was made to cope with that issue and has low computational cost. It can be used, for instance, for elucidating gene regulatory network, which is an important step towards understanding the normal cell physiology and complex pathological phenotype. Reverse-engineering consists in using gene expressions over time or over different experimental conditions to discover the structure of the gene network in a targeted cellular process. The fact that gene expression data are usually noisy, highly correlated, and have high dimensionality explains the need for specific statistical methods to reverse engineer the underlying network. Package: r-cran-networkchange Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1582 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1278902 MD5sum: 0d0cda95cc278bb991f5c729859ddf04 SHA1: c03fe355b1a84acc3a318b68fe9cf8b55cebbd35 SHA256: 6e576bda9a883cde8d87b216a515ddd3f7d166286057e1b1ed445739c847567e SHA512: e1633c593ec19983a91b29d0bcb8713e4df542baf9c448350374e0bff78ceb9404ea1da8c691b31dde188a6d96067ba623ea4c3d18263d69aac88421634b1bb0 Homepage: https://cran.r-project.org/package=NetworkChange Description: CRAN Package 'NetworkChange' (Bayesian Package for Network Changepoint Analysis) Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided. Version 1.1.0 includes high-performance C++ implementations via 'Rcpp'/'RcppArmadillo' for 5-15x faster MCMC sampling, along with modern 'ggplot2'-based visualizations with colorblind-friendly palettes. Package: r-cran-networkdistance Architecture: arm64 Version: 0.3.6-1.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), 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_arm64.deb Size: 415104 MD5sum: fd0ec811c80c4877964cec42b7d06aa4 SHA1: 1113fdd44fa2c027e0a86796fb10a759439f09bf SHA256: 13fb497846683345e2b01d7a3fde7c97f5799b86ebac3d9800f5d40131595c25 SHA512: 9532089154fe48462d663c71423636330804a26373acc47060134a34c118f1078f3b46f2a58cb5ef9fcda3f9da4d6c92668356fe3688b6e9e2ffc6bdb910ccac Homepage: https://cran.r-project.org/package=NetworkDistance Description: CRAN Package 'NetworkDistance' (Distance Measures for Networks) Network is a prevalent form of data structure in many fields. As an object of analysis, many distance or metric measures have been proposed to define the concept of similarity between two networks. We provide a number of distance measures for networks. See Jurman et al (2011) for an overview on spectral class of inter-graph distance measures. Package: r-cran-networkdynamic Architecture: arm64 Version: 0.12.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1341 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-network, r-cran-statnet.common, r-cran-networklite Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-networkdynamic_0.12.0-1.ca2604.1_arm64.deb Size: 1067486 MD5sum: 279b7253254bd20e96fd244046d27d0d SHA1: 1a5c85f1a51a1942792bb3e203c2a1cfcfd32d41 SHA256: 16bf52be768468e7c5909715fa059b3cfd6b3fb13cbd328b67c161212e4c6e6b SHA512: 52eee9218e301175efb1a31fb3df5f8fc3f740908f372e00455cae4d041602f1c22b78642d4dc9367273e364be92d3d4ab9b8d36b76af957e72f1a70182e88e2 Homepage: https://cran.r-project.org/package=networkDynamic Description: CRAN Package 'networkDynamic' (Dynamic Extensions for Network Objects) Simple interface routines to facilitate the handling of network objects with complex intertemporal data. This is a part of the "statnet" suite of packages for network analysis. Package: r-cran-networkinference Architecture: arm64 Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 662 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_arm64.deb Size: 371200 MD5sum: 4aed83ca2c2f8ecedcfaf0ab0c33096e SHA1: 1c65c60f012e82e85c95af7d4c5c46b690d493a7 SHA256: 60750ce1f68cb7763f3d8a3cc51a257de495f494ccc2fdd2f02e4b9de098f88f SHA512: 49b56e80a532bb8d35455315bbbdd12a97c38c4d4bb1b55c5ed02d40cc2d669ba0c80468f2aa70b5e2476e0f4b9292fcca876334d2a418051debd6b6e2db8f88 Homepage: https://cran.r-project.org/package=NetworkInference Description: CRAN Package 'NetworkInference' (Inferring Latent Diffusion Networks) This is an R implementation of the netinf algorithm (Gomez Rodriguez, Leskovec, and Krause, 2010). Given a set of events that spread between a set of nodes the algorithm infers the most likely stable diffusion network that is underlying the diffusion process. Package: r-cran-networkr Architecture: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 197 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 71736 MD5sum: 62f671503d2bbb737d06bd89f58f1fb1 SHA1: 5a3c0e0228150c3f641409943c0c39b2c77628af SHA256: 162a82cf94c98dcc3164ba36ee760961f4c48c558f225021d7e084aa9bc95948 SHA512: d1cef79698e9dfb73200f1f90e2c397e7252e590468854760446ab5cb8a51feffd301436ecb86763d3f63fa0aed3a43ae7586d12473394fb6e9b9fc95bac5254 Homepage: https://cran.r-project.org/package=networkR Description: CRAN Package 'networkR' (Network Analysis and Visualization) Collection of functions for fast manipulation, handling, and analysis of large-scale networks based on family and social data. Functions are utility functions used to manipulate data in three "formats": sparse adjacency matrices, pedigree trio family data, and pedigree family data. When possible, the functions should be able to handle millions of data points quickly for use in combination with data from large public national registers and databases. Kenneth Lange (2003, ISBN:978-8181281135). Package: r-cran-networkscaleup Architecture: arm64 Version: 0.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14086 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_arm64.deb Size: 2778196 MD5sum: 795f479777da96bb91e0a58d37812760 SHA1: 6840cb6054cf859112deb38626de14653d8d8ac1 SHA256: 6297a5df72d2b628db669dae3ff103a87937f90a6b238fab3c599d18727fa86c SHA512: 6988612eb23cd0a0cebbe44773d81ea0ae77bb096178df11d61d3a5b8dc2ff172bb95cf238233c8de68c62674cc8ed1d9c0157745d6ff4adc6d167aac33dac4e Homepage: https://cran.r-project.org/package=networkscaleup Description: CRAN Package 'networkscaleup' (Network Scale-Up Models for Aggregated Relational Data) Provides a variety of Network Scale-up Models for researchers to analyze Aggregated Relational Data, through the use of Stan and 'glmmTMB'. Also provides tools for model checking In this version, the package implements models from Laga, I., Bao, L., and Niu, X (2023) , Zheng, T., Salganik, M. J., and Gelman, A. (2006) , Killworth, P. D., Johnsen, E. C., McCarty, C., Shelley, G. A., and Bernard, H. R. (1998) , and Killworth, P. D., McCarty, C., Bernard, H. R., Shelley, G. A., and Johnsen, E. C. (1998) . Package: r-cran-neuroim2 Architecture: arm64 Version: 0.13.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5824 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.2), 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_arm64.deb Size: 2496460 MD5sum: 3375405365b07c857d50488f8b9df9ce SHA1: 7160662766fb6e4eeb1f8a487b2f06af36b8ce75 SHA256: 62469339df48920a9222e98113ef4482eedaadfaec311ca7a5f827a1c8b1d0bb SHA512: c35a151504b18df92a1337381e6b08a188ed1b7e398813acae933b206c0ecbdbb13b917f7cba5b2716f680714c4de0e781ebf16112a325b0e3f35483f441f918 Homepage: https://cran.r-project.org/package=neuroim2 Description: CRAN Package 'neuroim2' (Data Structures for Brain Imaging Data) A collection of data structures and methods for handling volumetric brain imaging data, with a focus on functional magnetic resonance imaging (fMRI). Provides efficient representations for three-dimensional and four-dimensional neuroimaging data through sparse and dense array implementations, memory-mapped file access for large datasets, and spatial transformation capabilities. Implements methods for image resampling, spatial filtering, region of interest analysis, and connected component labeling. General introduction to fMRI analysis can be found in Poldrack et al. (2024, "Handbook of functional MRI data analysis", ). Package: r-cran-neuroim Architecture: arm64 Version: 0.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1723 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1012242 MD5sum: 5655cf3b5c134ce23690f25b0ad48154 SHA1: 75200c16953de7dd8a8a3962f7968f716913638f SHA256: ac50d09ea7f25f4ffe5f6ee46a663a73c108f1e3383849ebf3aa8da6d5e8530f SHA512: d74a635b48819fb07ace6529b168151d295c48a509d81abcbf4b23f87e67a6f9e940fbabcdf03a5a5b08aaf3877cae77379d350a60441db08806cfad2614e7a4 Homepage: https://cran.r-project.org/package=neuroim Description: CRAN Package 'neuroim' (Data Structures and Handling for Neuroimaging Data) A collection of data structures that represent volumetric brain imaging data. The focus is on basic data handling for 3D and 4D neuroimaging data. In addition, there are function to read and write NIFTI files and limited support for reading AFNI files. Package: r-cran-neurosim Architecture: arm64 Version: 0.2-14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 129274 MD5sum: 74b85d0b7c1ae0d3dca431107b1fcaf7 SHA1: 44763ccc9052bb83ca52f7d412b4c1b27b2a698a SHA256: 3e86d617af314abd4e004d2977e023af9911d7395f702afc551f45be3cdaa943 SHA512: b0c9ff0ccb61122f9e24adf447fc666b7be3e214430e3eedbbd059143ce6f8520e98cc2b40be2e084522e6e314c3f9c4ac98de06099bb144aa1f02499bc26e55 Homepage: https://cran.r-project.org/package=neuRosim Description: CRAN Package 'neuRosim' (Simulate fMRI Data) Generates functional Magnetic Resonance Imaging (fMRI) time series or 4D data. Some high-level functions are created for fast data generation with only a few arguments and a diversity of functions to define activation and noise. For more advanced users it is possible to use the low-level functions and manipulate the arguments. See Welvaert et al. (2011) . Package: r-cran-nevada Architecture: arm64 Version: 0.2.0-1.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), 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_arm64.deb Size: 314770 MD5sum: e803031e7a9205ff2e929453a0effc36 SHA1: 5c81903690edaa56dce69365909c0b69c34ac7e9 SHA256: d05ea3ab35155de758ee365041268543da7a7f2c7942c8ab5976008178e066f7 SHA512: 6736a263c4724b5aa65c2a0e5652338b4028bf1037da472e2a9b147a356eac65f2eee73045ee2af4e49beb36c4b445d5c7dd1dcb4080fad938eb438cf0210519 Homepage: https://cran.r-project.org/package=nevada Description: CRAN Package 'nevada' (Network-Valued Data Analysis) A flexible statistical framework for network-valued data analysis. It leverages the complexity of the space of distributions on graphs by using the permutation framework for inference as implemented in the 'flipr' package. Currently, only the two-sample testing problem is covered and generalization to k samples and regression will be added in the future as well. It is a 4-step procedure where the user chooses a suitable representation of the networks, a suitable metric to embed the representation into a metric space, one or more test statistics to target specific aspects of the distributions to be compared and a formula to compute the permutation p-value. Two types of inference are provided: a global test answering whether there is a difference between the distributions that generated the two samples and a local test for localizing differences on the network structure. The latter is assumed to be shared by all networks of both samples. References: Lovato, I., Pini, A., Stamm, A., Vantini, S. (2020) "Model-free two-sample test for network-valued data" ; Lovato, I., Pini, A., Stamm, A., Taquet, M., Vantini, S. (2021) "Multiscale null hypothesis testing for network-valued data: Analysis of brain networks of patients with autism" . Package: r-cran-nfer Architecture: arm64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 107486 MD5sum: 5ec16e3eceb1f41588f5a39e087856f0 SHA1: 40ccc1f23f6d2e6abf3373d56ae62a9a7fc7eabe SHA256: 8b8afcbdab3d4a9a3c820929bfc7b7c1ff044478bd1edd44c6fad7ac3330bceb SHA512: ba4257da518700e54102aed2a2b87fd3eb41b39ba70506a9ce1e4fbeb8ba756afd2af6bf705b5d7b0750e2d55a3cb61f0edd3a8c33946b1ebcaa4063c0ae6390 Homepage: https://cran.r-project.org/package=nfer Description: CRAN Package 'nfer' (Event Stream Abstraction using Interval Logic) This is the R API for the 'nfer' formalism (). 'nfer' was developed to specify event stream abstractions for spacecraft telemetry such as the Mars Science Laboratory. Users write rules using a syntax that borrows heavily from Allen's Temporal Logic that, when applied to an event stream, construct a hierarchy of temporal intervals with data. The R API supports loading rules from a file or mining them from historical data. Traces of events or pools of intervals are provided as data frames. Package: r-cran-nftbart Architecture: arm64 Version: 2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 614 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 347878 MD5sum: 735fb71c007a79c9ac2a102ad92912f1 SHA1: 885635c246da0fd56b1179122be7712901d6f1a5 SHA256: 945342c6535422468004d0bce0f44fad792c13c9bf78f0049d2f51b8d385cb80 SHA512: 626d7edc1bb692a3bdbeec5a343256f5b521cfd1d4d1d2862b291b9179905d3d50c9be90e7253cf7def40a5613ee25eeab7407af9ed02099bc94b62a3ac12f23 Homepage: https://cran.r-project.org/package=nftbart Description: CRAN Package 'nftbart' (Nonparametric Failure Time Bayesian Additive Regression Trees) Nonparametric Failure Time (NFT) Bayesian Additive Regression Trees (BART): Time-to-event Machine Learning with Heteroskedastic Bayesian Additive Regression Trees (HBART) and Low Information Omnibus (LIO) Dirichlet Process Mixtures (DPM). An NFT BART model is of the form Y = mu + f(x) + sd(x) E where functions f and sd have BART and HBART priors, respectively, while E is a nonparametric error distribution due to a DPM LIO prior hierarchy. See the following for a description of the model at . Package: r-cran-ngme2 Architecture: arm64 Version: 0.9.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2828 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_arm64.deb Size: 1717998 MD5sum: 54e77a7900f35a75f869b777bdab5340 SHA1: 2c6b9b18eb53b383c6592122204c25c2e9f21d70 SHA256: 70e99fc5e1981d8c63f8441450e687661440a2e1b7cf5c5f8bd0cc651a53a1fd SHA512: f3775e097232683f7bca233e59ba522bd8a69892d7a09ff50e7cff72645f4105294d197c40915e52917c57becd8dd0ed430a20511e702efba28935e602b9666a Homepage: https://cran.r-project.org/package=ngme2 Description: CRAN Package 'ngme2' (Linear Latent Non-Gaussian Models with Flexible Distributions) Fits and analyzes linear latent non-Gaussian models for temporal, spatial, and space-time data. The package provides model components for autoregressive and Ornstein-Uhlenbeck processes, random walks, Matern fields based on stochastic partial differential equations, separable and non-separable space-time models, graph-based Matern models, bivariate type-G fields, and user-defined sparse operators. Latent fields and observation models can use Gaussian and non-Gaussian noise distributions, including normal inverse Gaussian, generalized asymmetric Laplace, and skew-t distributions. Functions are included for simulation, likelihood-based estimation, prediction, cross-validation, convergence diagnostics, stochastic gradient optimization, batch-means confidence intervals, and posterior-like sampling. The modeling framework is described in Bolin, Jin, Simas and Wallin (2026) "A Unified and Computationally Efficient Non-Gaussian Statistical Modeling Framework" . Package: r-cran-ngram Architecture: arm64 Version: 3.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-ngram_3.2.3-1.ca2604.1_arm64.deb Size: 347630 MD5sum: 23548c276f8e259a875b54958ed4c9ad SHA1: 814a46ec2aa3bb21c1588c535ce41afdb368f41a SHA256: ff95770f2240eef2328180f8374fb995adbe253ab148adf3415899e97b660ede SHA512: 9c350ec03f91ff2ec08227b9a2e51d8d88ecb65061ef3e18c00665a5922b66ee3f8c9dc34801b50e7680a65a0ce0aaf08b101d2b954a6cc0778aa382c1a327a2 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: arm64 Version: 1.2-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 611 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 390542 MD5sum: 87608086fc5a3b8ae5b1d4bda3e6bec4 SHA1: 23c86c762ebbddb86a6dc7add389c35b916387f1 SHA256: ec2c62d9cb00436378c2207ec9ab9075151cbffa5e525d67ea45f041eb8e590a SHA512: 6f31a72b47a97ac780e46f007aa80caffc2f129a4cdc9fcfa5857ed37cd037f1593ba7c6c657f807a5e745a73872b1761875e964ffb00508e53557144220ba34 Homepage: https://cran.r-project.org/package=ngspatial Description: CRAN Package 'ngspatial' (Fitting the Centered Autologistic and Sparse Spatial GeneralizedLinear Mixed Models for Areal Data) Provides tools for analyzing spatial data, especially non- Gaussian areal data. The current version supports the sparse restricted spatial regression model of Hughes and Haran (2013) , the centered autologistic model of Caragea and Kaiser (2009) , and the Bayesian spatial filtering model of Hughes (2017) . Package: r-cran-nhlscraper Architecture: arm64 Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2321 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-httr2, r-cran-jsonlite, r-cran-xml2, r-cran-arrow Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-nhlscraper_0.6.0-1.ca2604.1_arm64.deb Size: 1643788 MD5sum: b120ba73169866a268dfc9652a34f1c5 SHA1: eef9c0576329a4e89c84a3134cfe0ca6ca45f24b SHA256: 45fe771cf1dee7badd72baa1274438ff2f39a8e25392a4058c1876d0c6654eba SHA512: 921a2aced00cd3163730f3b6e2ad02229c8f06e403b2c7f169e67cf576e30f5b1070b4cdd47ae8cdfb3f98ae22a4335d741acf8995280f13accc1f7fd9d0568d Homepage: https://cran.r-project.org/package=nhlscraper Description: CRAN Package 'nhlscraper' (Scraper for National Hockey League Data) Scrapes and cleans data from the 'NHL' and 'ESPN' APIs into data.frames and lists. Wraps 125+ endpoints documented in from high-level multi-season summaries and award winners to low-level decisecond replays and bookmakers' odds, making them more accessible. Features cleaning and visualization tools, primarily for play-by-plays. Package: r-cran-nhm Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 833 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-desolve, r-cran-maxlik, r-cran-mvtnorm Suggests: r-cran-msm, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-nhm_0.1.2-1.ca2604.1_arm64.deb Size: 657818 MD5sum: 836abf2c931db625f52cbfc6f08b9cfe SHA1: 172b20b5237b277cbb44475091cfd55dca1a910f SHA256: db30d229cc6a6f4ad3ce0400261f02ea12b6a8993b41215fdefff5c4cb8a8992 SHA512: 46509ebf7da523cec0a0b424bed621332e6f7cc5ef30148d6d939d2477b8c9f8d3d84f482324f4c53402b95371b2d1eff6eae31976a42b95e88bf4851c52fec3 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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Package: r-cran-nhppp Architecture: arm64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 977 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lifecycle, r-cran-rstream, r-cran-rcpp Suggests: r-cran-data.table, r-cran-ggplot2, r-cran-knitr, r-cran-rlecuyer, r-cran-rmarkdown, r-cran-testthat, r-cran-tictoc, r-cran-truncnorm, r-cran-withr Filename: pool/dists/resolute/main/r-cran-nhppp_1.0.5-1.ca2604.1_arm64.deb Size: 501654 MD5sum: 258f468dc0efbf5a43f793d5f3cfc73f SHA1: 6b8d1419003ad31d4743bc84eb42ded1e97bd979 SHA256: 4e4cdf050a967ac872d369a39ecd44f90b4966eb76eba7aa1c89520abf4a7d06 SHA512: ff7b62cfdf38e42673b1208425131e11e0457e05d7d2c472448e7b356c3dedb6c82c116fd228c656735b7bf2ab6e1e36f0b7d44f816814f3b0b224654910c42e Homepage: https://cran.r-project.org/package=nhppp Description: CRAN Package 'nhppp' (Simulating Nonhomogeneous Poisson Point Processes) Simulates events from one dimensional nonhomogeneous Poisson point processes (NHPPPs) as per Trikalinos and Sereda (2024, and 2024, ). Functions are based on three algorithms that provably sample from a target NHPPP: the time-transformation of a homogeneous Poisson process (of intensity one) via the inverse of the integrated intensity function (Cinlar E, "Theory of stochastic processes" (1975, ISBN:0486497996)); the generation of a Poisson number of order statistics from a fixed density function; and the thinning of a majorizing NHPPP via an acceptance-rejection scheme (Lewis PAW, Shedler, GS (1979) ). Package: r-cran-niaidmi Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 845 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 290300 MD5sum: 83530196c81ebd4a86fc5ecfba5da235 SHA1: 831b4ec25b0ceb7ca95bc8864eac7070b6486e3c SHA256: e1c6f5219005f373709e2535108de478089e5171f8c8bcd1044e2b217ab02245 SHA512: 93aeafbdf51e341a90f8d4ebc4b154070fd0971151fe29d45c304026c94302aac0fa8f2bc39d978a487c703ebf81e207d715850ca7b18db32f2cd31e051acda0 Homepage: https://cran.r-project.org/package=niaidMI Description: CRAN Package 'niaidMI' (Markov Model Multiple Imputation for NIAID OS) The implementation of Markov Model Multiple Imputation with the application to COVID-19 scale, NIAID OS. Package: r-cran-niarules Architecture: arm64 Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2790 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 684372 MD5sum: dbc69b5c9aba7ecb4cd4236cfd252a27 SHA1: cdd106b7a1a25fe11d78874e70db6633c9913b05 SHA256: 403ff2df27e66398d968b6837da10f291dd35a3e1ba1fcb8973c8f5a1cdf7e24 SHA512: 918cccb03fb40a7f2e78a8d658bbfbf8ed6fd2b8dcba3e3ec669d47c5942ad86c9323b410d99e3c65f340a65a4f3933dc17279e6a65011155840a7cf280d6427 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: arm64 Version: 0.4-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 110 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-nice_0.4-2-1.ca2604.1_arm64.deb Size: 12608 MD5sum: 8f386ccd4b61ebd744544a8a9fbf20ff SHA1: 38c284d53062975c1e305f79c29d889b3f52b317 SHA256: 259f1146842b0c92128cbf3b919c914faf06bd3761b198d2f2f60141742f06cc SHA512: ec95a29ed637652a68bf8da9937a628cdf1d7f0c58596691d44239a11e9ed664174cee2a01cba26d78b32f31b5affdd3cad28bb2847f095d668e98f90600c200 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. 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The National Inventories of Landscapes in Sweden (NILS) is a long-term national monitoring program that collects, analyses and presents data on Swedish nature, covering both common and rare habitats . Package: r-cran-nimble Architecture: arm64 Version: 1.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 22885 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-coda, r-cran-r6, r-cran-pracma, r-cran-numderiv Suggests: r-cran-testthat, r-cran-mcmcse, r-cran-nloptr, r-cran-nimblequad Filename: pool/dists/resolute/main/r-cran-nimble_1.4.2-1.ca2604.1_arm64.deb Size: 8744342 MD5sum: b77d20fcdb7ae7e647754ae93662ca96 SHA1: e21ec2bf9b439d78ca2a8748473f42019c960367 SHA256: 062ed65a121ba8d21f34cc45091e64665f8db1bfdfb594e4deb3613e4fb6293f SHA512: 41c2f787c7346a01a0a9b820f5bd143c1d044a80c9ba378568b94c5b47099bce976ee4b929340864cac8e9ae3046eaec7d4a5fca20355ed8b013dc3db7907b07 Homepage: https://cran.r-project.org/package=nimble Description: CRAN Package 'nimble' (MCMC, Particle Filtering, and Programmable Hierarchical Modeling) A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, Laplace Approximation, deterministic nested approximations, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at . Package: r-cran-nleqslv Architecture: arm64 Version: 3.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-nleqslv_3.3.7-1.ca2604.1_arm64.deb Size: 108150 MD5sum: 598ddd41ba8824780d3f5b2bc5501bd9 SHA1: 56963ca6d0f0c8af25bc86c7e858c837a254ae49 SHA256: af6a2b252ee52175a0cec10c443be5a2833455d1f9ff40eff07f430a8b8bb9ee SHA512: e4ea53aaeda1ac20f8902d52eabdf37ba0c4c2e6c7d869eef9a9f66dfd351595bac33f6b86f190675871244365823eebf9da9fb4976a8f033537b68468b20772 Homepage: https://cran.r-project.org/package=nleqslv Description: CRAN Package 'nleqslv' (Solve Systems of Nonlinear Equations) Solve a system of nonlinear equations using a Broyden or a Newton method with a choice of global strategies such as line search and trust region. 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Package: r-cran-nlints Architecture: arm64 Version: 1.4.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 779 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_arm64.deb Size: 243512 MD5sum: 457a7358f23bff8901479ac46de2ad9b SHA1: 56381b2435d450186f2b0c2413ecbdd011e21a42 SHA256: 212ddfd8706f0947cb2658fd41e2791d6acafa4627c487afcc50a709fdeaa175 SHA512: d04412555b48f04c3e316c83a9bb663ddfa95933d1df26f99d4940963acd35dd75ba5f0ef38429ac6b716f79eeebb704033f9d48889e8a3dbd41f24bd6ae7721 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: arm64 Version: 2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 396 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 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_arm64.deb Size: 272292 MD5sum: 8e4e69053b9fadfe77cd5211c9649a37 SHA1: b8b8a3bc310f02763cfe45805618667933ada714 SHA256: 37c299e24bb8761e63263cfd79578b70fd76c835a19f8ff5d93533a775cb3e5e SHA512: 5a879f87eb85b75de897848fa719be29e86191b0d955b40135c3ca82667e72af0ce981c18a86aebc4c9c86f927522d808c2fd74d36fbf44c173e0193852b2005 Homepage: https://cran.r-project.org/package=nlmeVPC Description: CRAN Package 'nlmeVPC' (Visual Model Checking for Nonlinear Mixed Effect Model) Various visual and numerical diagnosis methods for the nonlinear mixed effect model, including visual predictive checks, numerical predictive checks, and coverage plots (Karlsson and Holford, 2008, ). Package: r-cran-nlmixr2est Architecture: arm64 Version: 5.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3807 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_arm64.deb Size: 2613856 MD5sum: 2d0ea96632733e4e8ddefa6abfdbebe9 SHA1: 1fa6860d2ecb2d0db4a7596fd4b640b2f12e7f32 SHA256: e34272e935a81406d77d8d90e99bed4570d2dbc8602bcf81bf2ac82208bc2bdc SHA512: e3d121ea4881ed48b20d23c87c1ce94c63e85f2dfa1caebac50315babdf0019193645acb4273ec11979881f26ddc1ee85d8761c773fda8669d3b4bc71313df47 Homepage: https://cran.r-project.org/package=nlmixr2est Description: CRAN Package 'nlmixr2est' (Nonlinear Mixed Effects Models in Population PK/PD, EstimationRoutines) Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 ). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 ). 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Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 ). This package is for support functions like preconditioned fits , boostrap and stepwise covariate selection. 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The GL mixed-effects model includes four special cases with normal random effects and normal errors (NN), normal random effects and Laplace errors (NL), Laplace random effects and normal errors (LN), and Laplace random effects and Laplace errors (LL). The methods are described in Geraci and Farcomeni (2020, Statistical Methods in Medical Research) . Package: r-cran-nloptr Architecture: arm64 Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1108 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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_arm64.deb Size: 500926 MD5sum: b0dcbe1612582184519d00b4a11227c8 SHA1: 759136127436d2faf6395ff3d2006197105a757d SHA256: a972192d778139f00c9c3346c8625a1e99de11cd6901523e757b9e244e0f6dd6 SHA512: ef8d1c55d193e7b38653ea8d25f377f962c7161d17ed49851fb74cccf90c5611ecbe32387dc1ac2376c653566112e6909f5a987c2cfb8c62bea0f8a1fd78f0a5 Homepage: https://cran.r-project.org/package=nloptr Description: CRAN Package 'nloptr' (R Interface to NLopt) Solve optimization problems using an R interface to NLopt. NLopt is a free/open-source library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. See for more information on the available algorithms. Building from included sources requires 'CMake'. On Linux and 'macOS', if a suitable system build of NLopt (2.7.0 or later) is found, it is used; otherwise, it is built from included sources via 'CMake'. On Windows, NLopt is obtained through 'rwinlib' for 'R <= 4.1.x' or grabbed from the appropriate toolchain for 'R >= 4.2.0'. 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Package: r-cran-nngeo Architecture: arm64 Version: 0.4.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 620 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-nabor, r-cran-units, r-cran-data.table Suggests: r-cran-dbi, r-cran-rpostgresql, r-cran-stars, r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-nngeo_0.4.8-1.ca2604.1_arm64.deb Size: 504916 MD5sum: 92aecde6a3e5c1de00acf2bf120caddd SHA1: 6e0e61577951604f1bb8e31b7d1ab7e781235228 SHA256: ecb7ccfdcd216e1fb039ff601d21643bf5f343ba1e612f2546f661f6e38d5cb4 SHA512: 5ba2463b3d4cc8958d7ef5364e5970169093f099bd7ec814621f831310a14859155a587b9f2167152607f5487ecb1bbc2e1e6f1905e61e9c68f002add19ac466 Homepage: https://cran.r-project.org/package=nngeo Description: CRAN Package 'nngeo' (k-Nearest Neighbor Join for Spatial Data) K-nearest neighbor search for projected and non-projected 'sf' spatial layers. 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Package: r-cran-nnlib2rcpp Architecture: arm64 Version: 0.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1857 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-class Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-nnlib2rcpp_0.2.9-1.ca2604.1_arm64.deb Size: 760160 MD5sum: 6a2e8479ab43e2fadefcac0dd7d737fb SHA1: bf3432530be574d624020ed9b0eece4833103710 SHA256: 7f71dbef911f4db15643fc27af486bdb71066e0e288a90494c45e3827fdba004 SHA512: 01cf9a4e54a288e2b1787ed814275f599c1597c83539f0bde18b0ddea846cccae159dfb9044aa413d945e0cbb47d2c712e5a68a1853288494c78dc526485bbfa Homepage: https://cran.r-project.org/package=nnlib2Rcpp Description: CRAN Package 'nnlib2Rcpp' (A Tool for Creating Custom Neural Networks in C++ and using Themin R) Contains a module to define neural networks from custom components and versions of Autoencoder, BP, LVQ, MAM NN. Package: r-cran-nnls Architecture: arm64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bvls, r-cran-quadprog Filename: pool/dists/resolute/main/r-cran-nnls_1.6-1.ca2604.1_arm64.deb Size: 38540 MD5sum: 87d7cb431f9d203d9e43fa806e31f61b SHA1: abc6027faaf0705bb1e212c5387eb7d0d314776f SHA256: 49e567c32c8162e2aa6b559bbbb352654482927d5442c20f8a333f9b32c0ef09 SHA512: 03bede4dce94e54f6d9a147ffe17695a00811990d17fd0a8aa05539f48e7ed1d60cc52dbb2a37c90a808c254a172e49bb47ad45985d23ecdbe9b078001864fdd Homepage: https://cran.r-project.org/package=nnls Description: CRAN Package 'nnls' (The Lawson-Hanson Algorithm for Non-Negative Least Squares(NNLS)) An R interface to the Lawson-Hanson implementation of an algorithm for non-negative least squares (NNLS). Also allows the combination of non-negative and non-positive constraints. Package: r-cran-nnmf Architecture: arm64 Version: 1.4-1.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_arm64.deb Size: 146000 MD5sum: 1e5cbb31d77e4b001e14edb1ff7a00cb SHA1: 80d6255c000ff3feb675fa4d84a547d2d95a6560 SHA256: efa8b58f53e2f0fb659fca9a29a598a6c5e04c000e6c8110362310d97fc91375 SHA512: 28333eac11483ecac81ed0bfef62108b57493c0234e95f06004171507bce1ffb04dca441f4877312f201e32c01800b96a9feb75fe519c4d03ee0dfce0e313d43 Homepage: https://cran.r-project.org/package=nnmf Description: CRAN Package 'nnmf' (Nonnegative Matrix Factorization) Nonnegative matrix factorization (NMF) is a technique to factorize a matrix with nonnegative values into the product of two matrices. Covariates are also allowed. Parallel computing is an option to enhance the speed and high-dimensional and large scale (and/or sparse) data are allowed. Relevant papers include: Wang Y. X. and Zhang Y. J. (2012). Nonnegative matrix factorization: A comprehensive review. IEEE Transactions on Knowledge and Data Engineering, 25(6): 1336-1353 and Kim H. and Park H. (2008). Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM Journal on Matrix Analysis and Applications, 30(2): 713-730 . Package: r-cran-nns Architecture: arm64 Version: 12.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2926 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.2), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-data.table, r-cran-doparallel, r-cran-foreach, r-cran-quantmod, r-cran-rcpp, r-cran-rcppparallel, r-cran-rfast, r-cran-rgl, r-cran-xts, r-cran-zoo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-nns_12.0-1.ca2604.1_arm64.deb Size: 1642764 MD5sum: 0e2671f052a2bf1996f97f5deea484d9 SHA1: 61ff30e1558257dc09b1a2712e28df63f97aa25d SHA256: 4823bfa4bbf47c6dcb7009ebdddd4f1f4460842155a1b46f6d17184f6d1a999f SHA512: 7ad7919c12966737383178f79c379eb218760ff31114f7f98e6c628476b5dd1a50ba7702143648fe24588228478139b54d7516926251d4a245f103274c215a5a Homepage: https://cran.r-project.org/package=NNS Description: CRAN Package 'NNS' (Nonlinear Nonparametric Statistics) NNS (Nonlinear Nonparametric Statistics) leverages partial moments – the fundamental elements of variance that asymptotically approximate the area under f(x) – to provide a robust foundation for nonlinear analysis while maintaining linear equivalences. Designed for real-world data that violates symmetry, linearity, or distributional assumptions, NNS delivers a comprehensive suite of advanced statistical techniques, including: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization, Stochastic superiority / dominance and Advanced Monte Carlo sampling. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995, Second edition: ). Package: r-cran-nnsolve Architecture: arm64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 78298 MD5sum: d8c73e1e85c37428c92334ae9929bdf2 SHA1: 6f2786e7cadc5ec5fdbf952af60ec33e1b7a9bed SHA256: 5793ad83ea534f1056748ca00f5396b87f732915fd4ac6a505d3908b755e7473 SHA512: 0ad923e9f94fe0ec9de6a293b27eecacb56d182b99cabd46e6376222cb776a4f8ddcf5e22db2f763ac662e3eeeed28bfe082e14c9a78b2c57c51e193ea6921f5 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). Package: r-cran-nntmvn Architecture: arm64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gpgp, r-cran-truncatednormal, r-cran-rann, r-cran-dplyr, r-cran-ggplot2, r-cran-tidyr, r-cran-r.utils, r-cran-lhs, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-nntmvn_1.3.0-1.ca2604.1_arm64.deb Size: 81756 MD5sum: 1dd8162fed023967b3a46b5a8454f6d4 SHA1: 26698e99855934214f6539c66d59424b76339bed SHA256: dc9718691d6cbb6aed6d34bb43cb560c34427a77a1497f108720a722615dd0cb SHA512: 22f18519448991f42bbaf1748b8e733474f0a790cab6cd5ab050824fbe791f9c9dc144a6ec37bf821d2230d5238ca35bb89a92c41d18cc730c2bbf211eccaf23 Homepage: https://cran.r-project.org/package=nntmvn Description: CRAN Package 'nntmvn' (Draw Samples of Truncated Multivariate Normal Distributions) Draw samples from truncated multivariate normal distribution using the sequential nearest neighbor (SNN) method introduced in "Scalable Sampling of Truncated Multivariate Normals Using Sequential Nearest-Neighbor Approximation" . 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Package: r-cran-nomclust Architecture: arm64 Version: 2.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 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_arm64.deb Size: 198806 MD5sum: e126a4e3f98b4281bad3d3e849191f56 SHA1: 8ed6ec99dcb49b0244889989bb5fcf6f44f47e68 SHA256: 0102f3722b751c98917e034d43bcd46a55e2ae1a315b4d3502ca5fc5226fb1e3 SHA512: 3a63769c31d8f427bb5ae9a465dd032b0c821e4644794b023f2057d98e67de6befe409de4d43e3f4ad5675cd15f3c6e5985a3c61f154f0d514c68f8637560347 Homepage: https://cran.r-project.org/package=nomclust Description: CRAN Package 'nomclust' (Hierarchical Cluster Analysis of Nominal Data) Similarity measures for hierarchical clustering of objects characterized by nominal (categorical) variables. Evaluation criteria for nominal data clustering. Package: r-cran-noncompliance Architecture: arm64 Version: 0.2.2-1.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_arm64.deb Size: 102596 MD5sum: e5e524753aba704aa703c60490cb5653 SHA1: db4bdfdb0f0febf567ebafd33e2c6defe0a0b1de SHA256: c5133fb8e014d20a34112ba9f6523acbd642378581f37578a37d9ac1e2aa0caa SHA512: 4fabdbce89b9c627d22ce14ddc89bd3ac0a9dc7bdf48075d95b1dd248bd2bc1e6fbe6fa19eaa0a23ea4483fcdd17fc451a3f7d732ccb6b88477230eb339cc0e2 Homepage: https://cran.r-project.org/package=noncompliance Description: CRAN Package 'noncompliance' (Causal Inference in the Presence of Treatment NoncomplianceUnder the Binary Instrumental Variable Model) A finite-population significance test of the 'sharp' causal null hypothesis that treatment exposure X has no effect on final outcome Y, within the principal stratum of Compliers. A generalized likelihood ratio test statistic is used, and the resulting p-value is exact. Currently, it is assumed that there are only Compliers and Never Takers in the population. Package: r-cran-nonlineardid Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 469 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 220126 MD5sum: f0e2949f9ddebbf9c6e443e3f47b090e SHA1: 7e0bd534b8cd58bfe48b067d9c8c10303bfee6b8 SHA256: 4153510b978d9a82060666a2e7cbeb8c22d60cf3c44f004f96c3999dc8987cac SHA512: 6d4c281d996175c24af92e867e6c506308fa096bce8b9d7b47c45c5d6596a52896dd24844a794c70a52eb7aee218e728e84bcc7143df48d1fa82f0ca1558540c Homepage: https://cran.r-project.org/package=NonlinearDiD Description: CRAN Package 'NonlinearDiD' (Staggered Difference-in-Differences with Nonlinear Outcomes) Implements difference-in-differences estimators for staggered treatment adoption with binary, count, and other nonlinear outcomes. Extends Callaway and Sant'Anna (2021) to handle the fundamental identification challenges that arise with nonlinear outcome models (logit, probit, Poisson) in heterogeneous treatment timing designs. Provides group-time average treatment effects on the treated (ATT), aggregation schemes, and pre-treatment parallel trends tests appropriate for nonlinear settings. Methods include doubly-robust semiparametric estimators, nonparametric bounds, and an odds-ratio DiD approach for binary outcomes. Methods extend Callaway and Sant'Anna (2021) , Roth and Sant'Anna (2023) , and Wooldridge (2023) . Package: r-cran-nonlineartseries Architecture: arm64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 956 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 602848 MD5sum: d8bb07c2ede7256d956452a60548514f SHA1: 4ca247c21795969744d6d2546ee80454358fbb9b SHA256: 3bf65203b17f9291536a4d7c1ffbfe3a54344d03401816e3e346adf0ffe75f43 SHA512: 39162405e22b261b6a772de823a4c9ebd4cd7f6edeaa47671728fa28a40e56ae47e67f405ae720308a73ac88a2c3556705aa524680ffd0693c008aacb3b38156 Homepage: https://cran.r-project.org/package=nonlinearTseries Description: CRAN Package 'nonlinearTseries' (Nonlinear Time Series Analysis) Functions for nonlinear time series analysis. This package permits the computation of the most-used nonlinear statistics/algorithms including generalized correlation dimension, information dimension, largest Lyapunov exponent, sample entropy and Recurrence Quantification Analysis (RQA), among others. Basic routines for surrogate data testing are also included. Part of this work was based on the book "Nonlinear time series analysis" by Holger Kantz and Thomas Schreiber (ISBN: 9780521529020). Package: r-cran-nonmem2rx Architecture: arm64 Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6660 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_arm64.deb Size: 967484 MD5sum: 81c445a4145743510f25167d0509c9ec SHA1: dc6fa57a4b6a625afa38d0ba08bae9e0d280ce74 SHA256: f7e4fec50da6be99a750a85f0295aeb22d898b3a035e2a63dd63690d11bdfa15 SHA512: 894f956c6589bd63948e7eaa0d3123f9e35d6a42b640e8711f23f1ce40746b7277c5a8d68c2f82d93d019360ea9830861b17dc078c389ad95b814e414f69ef69 Homepage: https://cran.r-project.org/package=nonmem2rx Description: CRAN Package 'nonmem2rx' (Converts 'NONMEM' Models to 'rxode2') 'NONMEM' has been a tool for running nonlinear mixed effects models since the 80s and is still used today (Bauer 2019 ). This tool allows you to convert 'NONMEM' models to 'rxode2' (Wang, Hallow and James (2016) ) and with simple models 'nlmixr2' syntax (Fidler et al (2019) ). The 'nlmixr2' syntax requires the residual specification to be included and it is not always translated. If available, the 'rxode2' model will read in the 'NONMEM' data and compare the simulation for the population model ('PRED') individual model ('IPRED') and residual model ('IWRES') to immediately show how well the translation is performing. This saves the model development time for people who are creating an 'rxode2' model manually. Additionally, this package reads in all the information to allow simulation with uncertainty (that is the number of observations, the number of subjects, and the covariance matrix) with a 'rxode2' model. This is complementary to the 'babelmixr2' package that translates 'nlmixr2' models to 'NONMEM' and can convert the objects converted from 'nonmem2rx' to a full 'nlmixr2' fit. Package: r-cran-nonneg.cg Architecture: arm64 Version: 0.1.6-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 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_arm64.deb Size: 41292 MD5sum: c0d1d18b2bd683e07a266355954caa52 SHA1: 15c7166370749c9f893fb12882705c79f0f8c8b6 SHA256: 213d8d413305c38c6aaadd9ff35dd0ed055da9dfd13ffaa3ee5a404ba8161dd4 SHA512: 4f2c54c8e3c41b47b76c530dbc87a686341e276a76c8825866988a8c6e8d18d1d21262533c684177d47fad6f14a25415d9d3ffc2a500a916614d5769145c7824 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: arm64 Version: 1.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 596 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_arm64.deb Size: 438946 MD5sum: ed663931ef6121e7bad0b9d1cad2e3b9 SHA1: ef84ea71dd21d06923e9169dd0a0d2594101baca SHA256: 597cdb21ffeebfb0c895a103ca92d52ace837f4973c235185c0027e7d0a87b38 SHA512: f604e3c005b13fdaa587cb90042663b80240b88677478b5a6dd30f71563ecf8679805c22ba9d53c209a3b6d04761af82335188209343b3678a543b3f05acd626 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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Package: r-cran-normpsy Architecture: arm64 Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lcmm Filename: pool/dists/resolute/main/r-cran-normpsy_1.0.8-1.ca2604.1_arm64.deb Size: 48430 MD5sum: cb329f4f828add3d95db26206e965c32 SHA1: 01085a2721bafbac05824c19e221ff9cfa33e1de SHA256: d4c224f1bb01c039cc79cab044e4da189522109f1ebf6c7a37fc3278beea0996 SHA512: 8f0fded328fb88b5f36ecf7bfebdb8a6e24f2b79c510574aeaa02b2f746f3720e07e781c9bbb3ecf591be63634244dea8891dc42e495c2f4a8e442ff32c45ee7 Homepage: https://cran.r-project.org/package=NormPsy Description: CRAN Package 'NormPsy' (Normalisation of Psychometric Tests) Functions for normalizing psychometric test scores. The normalization aims at correcting the metrological properties of the psychometric tests such as the ceiling and floor effects and the curvilinearity (unequal interval scaling). Functions to compute and plot predictions in the natural scale of the psychometric test from the estimates of a linear mixed model estimated on the normalized scores are also provided. See Philipps et al (2014) for details. 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Package: r-cran-nosleepr Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 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_arm64.deb Size: 31896 MD5sum: a7fb654526a000ae0e607689566e5612 SHA1: a4d3214064ec18e54675320661da1214b3f7df0f SHA256: e8ab84f3d11552d2a7801027bb9fef90f98308de3a0849c15110c2d3b6ebfd77 SHA512: ea86819df5550e8bd5608f62b375d838f6fac498070a3cc9213b7fa7292c6f5a5be18ff928db43c41761b4921b77e0ab379d98cc19f4ee414807e6fae1bba4d9 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. Package: r-cran-not Architecture: arm64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 160 Depends: libc6 (>= 2.17), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-not_1.6-1.ca2604.1_arm64.deb Size: 71660 MD5sum: 9dcd1a4f147e215618bffdfa43de0784 SHA1: 8688f491f7dccb43ea5b2a80c796ecb47891b01c SHA256: 7f0f88f4b2d76fd75a56f73a3f306ea077b34995eb3592b76ee5652dfa8652d2 SHA512: 91b7f929a4974105cd60b5bd539ee9edd983ba054ed0b195eb0a2866f0af352d1451940c4541441c12e6525a8868f0dcd322e92ca3e2141e92ac23b4fb02b19b Homepage: https://cran.r-project.org/package=not Description: CRAN Package 'not' (Narrowest-Over-Threshold Change-Point Detection) Provides efficient implementation of the Narrowest-Over-Threshold methodology for detecting an unknown number of change-points occurring at unknown locations in one-dimensional data following 'deterministic signal + noise' model. Currently implemented scenarios are: piecewise-constant signal, piecewise-constant signal with a heavy-tailed noise, piecewise-linear signal, piecewise-quadratic signal, piecewise-constant signal and with piecewise-constant variance of the noise. For details, see Baranowski, Chen and Fryzlewicz (2019) . 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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: arm64 Version: 0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 750 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_arm64.deb Size: 474398 MD5sum: 0b7da629950fe739165d4b4af9738a20 SHA1: 07e723f475b1ff518602b8529c09eb133ae60707 SHA256: 62dbab8c4ba25aae36e8198c7f6ea3b4af74d95ec0a95978fa6a020e96e21cf7 SHA512: 97caf03a306b06b59863e941a5a4b7a813a74beb88c8461be4dcc2c3468db41be29ffe2173533720bb035b13d6eeb5441d084c62cd2e3d28f27a7762dbf621f9 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) . Package: r-cran-npbr Architecture: arm64 Version: 1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1791 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-benchmarking, r-cran-np, r-cran-quadprog, r-cran-rglpk Filename: pool/dists/resolute/main/r-cran-npbr_1.8-1.ca2604.1_arm64.deb Size: 1498322 MD5sum: 8ef6b0d851130829b8c5dfb82d8ec125 SHA1: f2aa5af760372600ce005b94badc73978c04951e SHA256: afa24081df6e6f4d2f510815b4a68f99d48be2ebbaa8b02425c92b5e02440d49 SHA512: bb32ea4d5d6f7a055f97243174073460e51e09f8e6240fdfee4d8cf9429d304ab4b44bb0ef958a3fdb220fb543ed686b75b837ff03ec5156baacf597d62af306 Homepage: https://cran.r-project.org/package=npbr Description: CRAN Package 'npbr' (Nonparametric Boundary Regression) A variety of functions for the best known and most innovative approaches to nonparametric boundary estimation. The selected methods are concerned with empirical, smoothed, unrestricted as well as constrained fits under both separate and multiple shape constraints. They cover robust approaches to outliers as well as data envelopment techniques based on piecewise polynomials, splines, local linear fitting, extreme values and kernel smoothing. The package also seamlessly allows for Monte Carlo comparisons among these different estimation methods. Its use is illustrated via a number of empirical applications and simulated examples. Package: r-cran-npcirc Architecture: arm64 Version: 3.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1319 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-circular, r-cran-rcpp, r-cran-misc3d, r-cran-movmf, r-cran-plotrix, r-cran-rgl, r-cran-shape, r-cran-bolstad2, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-npcirc_3.2.1-1.ca2604.1_arm64.deb Size: 863374 MD5sum: 2bdce9cbfc6403393a7ec4b85362ff3a SHA1: 88f4e227be1ecc9d650211411bcbf91a5c2b702f SHA256: 6e7fcfeccdd1e01a89afc52973498be386190681c734f967cc6dd1b8e83c05e3 SHA512: 504eb70458acf4427a04c7e6bbcf9cbe62e524c8efa10d2263804674badf56f943739fed15f9460d09c27833793e72b2441a7d1b159b12016dc57f48603b81a5 Homepage: https://cran.r-project.org/package=NPCirc Description: CRAN Package 'NPCirc' (Nonparametric Circular Methods) Nonparametric smoothing methods for density and regression estimation and inference with circular data. The package provides kernel density estimation along with inferential tools such as circular SiZer for feature significance, mode estimation, and modal clustering. It includes multiple methods for selecting the smoothing parameter, allowing users to optimize the trade-off between bias and variance. Various plotting functions help visualize estimated densities, modes, clusters, and significance features. For regression, the package implements nonparametric estimation of the mean regression function as well as other conditional characteristics, including modal regression and generalized regression. Bandwidth selection is also supported in the regression context, and testing procedures are available to assess structural features or effects in circular regression models. Package: r-cran-npcp Architecture: arm64 Version: 0.2-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 239544 MD5sum: 41722f689e3faf60d12462bf54f32118 SHA1: 1a5bf6f86ec7fff70dc0fa758c1a15357756a55e SHA256: b4a56017eff299b4a0f46e6bf079ef2f63d2d3472f6b97f09bffc41ba67e49b2 SHA512: 952c4acbd339be48a144708f39212a3fd78e7935716884f90380478c4077a9a0df47ff9e439c6b2b199f54cbcafd3a4932394b71754e5a794951f7efe163a8ed Homepage: https://cran.r-project.org/package=npcp Description: CRAN Package 'npcp' (Some Nonparametric CUSUM Tests for Change-Point Detection inPossibly Multivariate Observations) Provides nonparametric CUSUM tests for detecting changes in possibly serially dependent univariate or low-dimensional multivariate observations. Retrospective tests sensitive to changes in the expectation, the variance, the covariance, the autocovariance, the distribution function, Spearman's rho, Kendall's tau, Gini's mean difference, and the copula are provided, as well as a test for detecting changes in the distribution of independent block maxima (with environmental studies in mind). The package also contains a test sensitive to changes in the autocopula and a combined test of stationarity sensitive to changes in the distribution function and the autocopula. The latest additions are an open-end sequential test based on the retrospective CUSUM statistic that can be used for monitoring changes in the mean of possibly serially dependent univariate observations, as well as closed-end and open-end sequential tests based on empirical distribution functions that can be used for monitoring changes in the contemporary distribution of possibly serially dependent univariate or low-dimensional multivariate observations. Package: r-cran-npcure Architecture: arm64 Version: 0.1-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 235 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-permute, r-cran-zoo Suggests: r-cran-kmsurv Filename: pool/dists/resolute/main/r-cran-npcure_0.1-5-1.ca2604.1_arm64.deb Size: 152784 MD5sum: d6cb7643ceb63cafb1409d9819979ec3 SHA1: 3354859404d343b7cfd7d65cab0e64b1c2b45481 SHA256: f38e0f280ae9deb67b591a51f31569eb0b537417a6f5049dd6890ef5182c891e SHA512: 32432ca7380ef5fb35e0b972db2ee83058c212a176cc262fcc096ea4cc32768d68a435ffa31bd2fbc544fe074343831af326dc17d8327c3d712de6fd6b7e2e44 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) . Package: r-cran-npflow Architecture: arm64 Version: 0.13.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1090 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-truncnorm, r-cran-ellipse, r-cran-fastcluster, r-cran-ggplot2, r-cran-pheatmap, r-cran-reshape2, r-cran-ggally, r-cran-rcpparmadillo Suggests: r-cran-foreach, r-cran-doparallel, r-cran-itertools, r-cran-microbenchmark, r-cran-mass Filename: pool/dists/resolute/main/r-cran-npflow_0.13.6-1.ca2604.1_arm64.deb Size: 727488 MD5sum: 38dc79dd7824267e5cf0f4fc54645b16 SHA1: 85a35ddfbd95a44f309bb3402f19683e0983608f SHA256: 4bc502c1635304b009fcb38406e6ea43c843b832a6498eb3c38091cfe96f92ce SHA512: 6f5ceda0ac09a7a8a92fdd04fef4e832e0997fb567611c4d768c59916ecd9e8e277dd601186f9102370add3195f80f5f9023cd43e67824dc2b100254a7bda904 Homepage: https://cran.r-project.org/package=NPflow Description: CRAN Package 'NPflow' (Bayesian Nonparametrics for Automatic Gating of Flow-CytometryData) Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions modeling oriented towards flow-cytometry data preprocessing applications. Method is detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) . Package: r-cran-npiv Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 188376 MD5sum: 50d19e4f7165add86faaa6b06cefbedf SHA1: 03d54c5c906fa31257a6fc9505d4d3ae80bb953d SHA256: 348e2fa156121d960268a410db7499ec9318e303864c8059b1c7740ab2f4ea02 SHA512: 5e6f86b60bcbe763876aff0d5130f059e9430c4a29fb420cb9d5194dc342bc890b3816f3426ffcb99655a609d2663b80d8197412b28683a81261e85206a7c3d9 Homepage: https://cran.r-project.org/package=npiv Description: CRAN Package 'npiv' (Nonparametric Instrumental Variables Estimation and Inference) Implements methods introduced in Chen, Christensen, and Kankanala (2024) for estimating and constructing uniform confidence bands for nonparametric structural functions using instrumental variables, including data-driven choice of tuning parameters. All methods in this package apply to nonparametric regression as a special case. Package: r-cran-npregfast Architecture: arm64 Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-doparallel, r-cran-foreach, r-cran-mgcv, r-cran-sfsmisc, r-cran-shinyjs, r-cran-wesanderson, r-cran-ggplot2 Suggests: r-cran-gridextra Filename: pool/dists/resolute/main/r-cran-npregfast_1.6.0-1.ca2604.1_arm64.deb Size: 278182 MD5sum: 53d0542040f01072f964e8fc7b0769ef SHA1: bc5ccc3666de5ab2eabfdf5bbcf7bb5c6081f0bc SHA256: ecc3c13e0ad5110e5ffca7068298dfa30229fe8d24600f8dbad574c69e68028d SHA512: a6c662dd598d3984971e0b881bc65d14dcca68db27af55ecd74721e8728dfcdbddf6f504f351eeac7d17f5f7e5453cb882899966203acbe5eedb12c5881afb9d 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: arm64 Version: 0.70-2-1.ca2604.4 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5706 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_arm64.deb Size: 4923356 MD5sum: c5e2ccfbbd79b7a2fc4f7ce1630ee5a9 SHA1: 15c244820891b47ece277ca8aa0e5f91de569f98 SHA256: ece8d0863b38246a80bceb7510b87b37c2f1de572e5cf828f4bceaefd576bc85 SHA512: e415fd56be12ccc1486260af7a3710168a3bd0e596896dab7ba50d884f4207dfdfe10803ff513aa90eb630298232f64a4935d403beaa0b620309b0abc81e5f42 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: arm64 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_arm64.deb Size: 239074 MD5sum: b0c10495216c9c88669490b7056d697c SHA1: beb2ee15888726726f36edc767406cfed9006c33 SHA256: 561c758eb369ac578096df1e2512e2dedeed58552eaca25da1dbed1abb15c745 SHA512: 89178ae8bb12e7e98bc3bbfbcdef45a7535d4f3ec2060e8f8256cdacfbdd9ea4b30f9b88d3eadf90398e53795028aeec5f43bef6e83b60ec0afef89403931b51 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: arm64 Version: 1.0-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 629 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_arm64.deb Size: 364864 MD5sum: 21b97ce32f17e0b12f91bec9d217bcd7 SHA1: 5c34107775cd136a1ea99f8657accc2a6ab3d664 SHA256: 40bf0797e22222f8d425c90f83ff4512f95fe54d36ef2ea4baf824b426cf21ae SHA512: 61c30b60495e6674a054793630fb4aee4250cffa338c11748f26033f745e4a5c5b55d95a3a8cf3c1bad52d1266b94d802479174c1834e57e551fae48b07e9cfd 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: arm64 Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1529 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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_arm64.deb Size: 1194462 MD5sum: 6a4dd8725b7a99baf72f70677e657068 SHA1: 55b1321a3e01d67c54331b4116b829aba985aec8 SHA256: 92b93c5fa9da39d7a00c08425a02c15e4a3e1ab40c0896ea297323738c8dda26 SHA512: 3aa9c7a67f81d08d669c9d6238a4fc9c2ffac63dd98e07d244683cf6714afa75cc210e8fa0a1c824e473fa800d339fa34a766f3865a730fda146a9bc5cda9e1f 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: arm64 Version: 1.3.6-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 499 Depends: libc6 (>= 2.38), 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_arm64.deb Size: 407896 MD5sum: 39a1ec3fa05e5633f1d77c7b5e29292a SHA1: 27a1a005a2fc7321e04b3243088b7fc134ec1b6a SHA256: 8d63e85e78cecede54aa37ed104182d586dcf435d83c680b63efa180cbfab34e SHA512: 6078b5c20b7bcc233aa654c10689f5869e2e6288dfd77d33a2ec57f72821a5fbd719d3a878f6ae4bb6a29fb0d5979272ba9f75adefb49a2af78d7e98168b0901 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: arm64 Version: 1.22-1.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, 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_arm64.deb Size: 97996 MD5sum: efab7db0a81a16a41b37b3379e13dd3e SHA1: 27c5b3f1f1f29e1796c1b1ae1ff8c4aa1104632b SHA256: 3c4762b15a0a4a7297c5ddc17308ae91c7432bf5bac2312885f6e580c528b8f1 SHA512: 454ab13bf23e91082d3c2c80d6db600ae89c351ddcd37505eafd3656f5dfc1c526ecf55992266daa5444ce90b6c94b48853317523027e644888b7bac05d34496 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. 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User-defined functions may also be supplied to guide custom pattern searches. Supports both crisp (Boolean) and fuzzy data. Generates candidate conditions expressed as elementary conjunctions, evaluates them on a dataset, and inspects the induced sub-data for statistical, logical, or structural properties such as associations, correlations, or contrasts. Includes methods for visualization of logical structures and supports interactive exploration through integrated Shiny applications. Package: r-cran-nullcat Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 707 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 418170 MD5sum: bd80da8dad86066d3e71fcbdb36f7e77 SHA1: a29e46bf972728784f79f8d6c07ae9dc711aaa4e SHA256: 561e3257d1fc835b27f03a32a3a692c5505f509743433095f80e8cc5672b5f81 SHA512: 6770cf9eaf1129e1bec33552e62146b934c95a445a8ac17a601852901c0f6d39eba9e8431cda1a7791261e306802cbeb0d4ad05862a8c801c00aa0ba831742b6 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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See for a high-level description of select functionality. 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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: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 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_arm64.deb Size: 76270 MD5sum: 75d9ac67d924794f4b2dc03e42005c96 SHA1: 2ac7e0f471de6d791ad061dd750084f82e04d194 SHA256: 82e7123182d05833e8e7417a94f21aa3c0b25421dab081ffbb411435ff52336e SHA512: 8e9888cb17d1c0d34e5dc047f5d6c885069be81e59e08597b4dbfd5262a5c7d2510581ab21ecd3c94a226b4955bee45ac6beef60fa4b9213b4d5d30d08b678ac 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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For the later, it allows statistical analysis by simultaneously estimating linkage and linkage phases (genetic map construction) according to Wu and colleagues (2002) . All analysis are based on multi-point approaches using hidden Markov models. Package: r-cran-onion Architecture: arm64 Version: 1.5-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2901 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-emulator, r-cran-matrix, r-cran-freealg, r-cran-mathjaxr Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-onion_1.5-3-1.ca2604.1_arm64.deb Size: 2175180 MD5sum: f7125808e2dcabd1030bebbebf77c5b4 SHA1: f0d65d30a87aafbd956249e00ad28150b391a0d7 SHA256: dc2ac4c9630da9d86ee5d4130aefbf58de51aac84c4b32bc9c596a306f80b8d9 SHA512: d6ae70814816617e19decfaa104026746363b986ad407d1e3fa13d54659b56a06e62720787a79f90e4a9963ab86f87753f8455310948b64e178abad394120042 Homepage: https://cran.r-project.org/package=onion Description: CRAN Package 'onion' (Octonions and Quaternions) Quaternions and Octonions are four- and eight- dimensional extensions of the complex numbers. 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Package: r-cran-onlinecov Architecture: arm64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 124 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-onlinecov_1.3-1.ca2604.1_arm64.deb Size: 29310 MD5sum: 97e4b35980bae98ebfe13a23618d8105 SHA1: 60b7c2d6ca68b32ada5fb1cbff732662f675a2cf SHA256: fc19fc48f8764cfdad6b221a79e0fe73d0e2de4862fc2e0d976a98c27e86bc00 SHA512: 536ec3f216f4027750eb4f16db23f6661b92e01602e871eee9a7279aadf955355edb7d2efd4f28385724064339de56d0ba80b6194ba8d87e9608c3439d9c1d50 Homepage: https://cran.r-project.org/package=onlineCOV Description: CRAN Package 'onlineCOV' (Online Change Point Detection in High-Dimensional CovarianceStructure) Implement a new stopping rule to detect anomaly in the covariance structure of high-dimensional online data. The detection procedure can be applied to Gaussian or non-Gaussian data with a large number of components. 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Package: r-cran-onlineforecast Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4082 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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-pbs, r-cran-digest, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-r.rsp, r-cran-testthat, r-cran-data.table, r-cran-plotly Filename: pool/dists/resolute/main/r-cran-onlineforecast_1.0.2-1.ca2604.1_arm64.deb Size: 2843486 MD5sum: 33609292dbd5fc10202ea9aa042da34f SHA1: 612c9286585a224bfc4a3553b824ee967d9a990a SHA256: 9ac98f161b3d76f7fb3cb4663492e6815d0d0108f47c39b940b2affd43202d91 SHA512: aeb45660813411bdc7b88555b0ee8252fe5fbbab394e7e4ea18f9ab256434b498573ecf367dc050c773a31dc50c6f1b9765530e2ad51a03d63cfca33d249208b Homepage: https://cran.r-project.org/package=onlineforecast Description: CRAN Package 'onlineforecast' (Forecast Modelling for Online Applications) A framework for fitting adaptive forecasting models. 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Package: r-cran-onlinepca Architecture: arm64 Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 275 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rspectra, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-onlinepca_1.3.2-1.ca2604.1_arm64.deb Size: 159182 MD5sum: 789c42793a84c6710dc48759be5b20e9 SHA1: bfd7d727f4db9fcc7b7f3a3e6b596888e8287965 SHA256: a09c991676678b90f4f3fc0f8187804593f249b17af1485a402c80afac0ed93b SHA512: 58c972f4aac4637bd2df3fdab279d122a5f0f5b93385fbd1585af1b862c42e207540f5e4a97ccf8497cd11cb41da7c2a609dd978594d9d2c79529a2442d7b4f1 Homepage: https://cran.r-project.org/package=onlinePCA Description: CRAN Package 'onlinePCA' (Online Principal Component Analysis) Online PCA for multivariate and functional data using perturbation methods, low-rank incremental methods, and stochastic optimization methods. Package: r-cran-ontologysimilarity Architecture: arm64 Version: 2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 985 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-ontologyindex Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-paintmap Filename: pool/dists/resolute/main/r-cran-ontologysimilarity_2.9-1.ca2604.1_arm64.deb Size: 732936 MD5sum: f68842bab7c327438d25a45e20510f21 SHA1: 52faa47695e8a4e051b89ef159f70710cfa32fc9 SHA256: ed6c31015e6f7b0c903c6034f06011bc608dfe40ce8cf3a6acff63c5fb2c71f2 SHA512: e20fe18c7ea35b813ce505fabe4961d169108bb8c5f992cdeb0422bd0e5b6de35abd56defd1bcda6cedb7d35c56de0ae48e2e7d49a5acb620ab6b140d2776c20 Homepage: https://cran.r-project.org/package=ontologySimilarity Description: CRAN Package 'ontologySimilarity' (Calculating Ontological Similarities) Calculate similarity between ontological terms and sets of ontological terms based on term information content and assess statistical significance of similarity in the context of a collection of terms sets - Greene et al. 2017 . 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Package: r-cran-openimager Architecture: arm64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4041 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-rcpp, r-cran-shiny, r-cran-jpeg, r-cran-png, r-cran-tiff, r-cran-r6, r-cran-lifecycle, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-openimager_1.3.0-1.ca2604.1_arm64.deb Size: 2640912 MD5sum: 50fe203b751688f9e8fe7a4e3d31390e SHA1: 5a9ea64aff393c7d42b7fc517ce25a81ecc75622 SHA256: 513a515199268e9dbae490bc96f9b2759c79e9410245baef7776678d94453f1d SHA512: 8bc159903da15d061b7875c8cabe3276b29348f59c6e87cf5bbd7f6d7ca34e2c650425a5a1d99278c24d0dc7303b17870bfff837b7a80b7e56b1b41d6f60caea Homepage: https://cran.r-project.org/package=OpenImageR Description: CRAN Package 'OpenImageR' (An Image Processing Toolkit) Incorporates functions for image preprocessing, filtering and image recognition. The package takes advantage of 'RcppArmadillo' to speed up computationally intensive functions. The histogram of oriented gradients descriptor is a modification of the 'findHOGFeatures' function of the 'SimpleCV' computer vision platform, the average_hash(), dhash() and phash() functions are based on the 'ImageHash' python library. The Gabor Feature Extraction functions are based on 'Matlab' code of the paper, "CloudID: Trustworthy cloud-based and cross-enterprise biometric identification" by M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015, . The 'SLIC' and 'SLICO' superpixel algorithms were explained in detail in (i) "SLIC Superpixels Compared to State-of-the-art Superpixel Methods", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274-2282, May 2012, and (ii) "SLIC Superpixels", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, EPFL Technical Report no. 149300, June 2010. 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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: arm64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-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_arm64.deb Size: 99982 MD5sum: 6079a1b212a1dff94dc6e1c86454e60d SHA1: 49761c5d0a100a60d561d394368d23f2eaa48082 SHA256: 707df5c9e1b64bce2c0ee1b6e5bcca24b245bb6995c39c1ea888d478c3dd4668 SHA512: e7ff597af9289a454cfb84fd47f8f67a5235bb0fe9b784e0542a494bd4b678f623a9ec969600e3059fc0d99cf35912c52c3a46fdb98008f077cefa2e1b830e7f 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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Package: r-cran-openxlsx2 Architecture: arm64 Version: 1.26-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4456 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_arm64.deb Size: 2634802 MD5sum: 0aefda644d9a76d6d546231438847150 SHA1: 4798df0c604fcdc83acc48f3591ac41bf220fba1 SHA256: bc9702365cc15e235c47cd15fad8f1cda3491d8e1e8e6c24cbc4dfca140448db SHA512: a5a8102b501475e23a37cc67c9af3251d8fe1d954ef92451bed695c8958e190bb228a2804eab970ade0313e308a5f32e941478545660d3877bb557eefca2bf93 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: arm64 Version: 4.2.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2829 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_arm64.deb Size: 2017116 MD5sum: bb68c964eaf5ec51aa142f71932760d9 SHA1: 846ca895e6d3af86c124177c0b85f3d615bf1048 SHA256: 0075a49f3d18ff81966f131b74238a441e42db580e0da2b158e6410ee7d7b95e SHA512: 67167d2d4214ab3929cee9e0f34d83755d80cbe5cb3ffdfa7deb77671f6c0445e92b92c3d7d1d104f5becd3f7a08266c37fef1e171ed8dbcf82cd42b8428d9d7 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: arm64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1922 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_arm64.deb Size: 1125776 MD5sum: ae6d355c4860c1a35da9c3e1bb29085f SHA1: 6adea400ce5a4731b4efa3e702244da73aab4518 SHA256: 9f9f9eb881e722293bf7e327004cb7b3b119ffb9fcef7b855dbfd5c241e4753a SHA512: b7837880ad847f0d4f50e8712e5da54386327178d6112777e5d9d4dd8258de555bb754a467d97839c0c2b25f88e46b7c7a7ad445557c97040f826bea9179049b 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: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 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_arm64.deb Size: 177998 MD5sum: 6e4f3576b77ad0ccf9d7acdca96819a0 SHA1: cb045354aae74487262e1d7f79034f64b2838d8d SHA256: 65258676672d8eda93b1410de46a7f6fc4398a93ac770a66d860ce9eb3f3bc7e SHA512: 4b2442db6d87abaeff888a475b5a2351d6eb8d84cf31b4527038384ff919c6b01fdc1df18fe6c366fe8230f8e430b6aed71026e863fdbf219dca4b727c2d3803 Homepage: https://cran.r-project.org/package=Opt5PL Description: CRAN Package 'Opt5PL' (Optimal Designs for the 5-Parameter Logistic Model) Obtain and evaluate various optimal designs for the 3, 4, and 5-parameter logistic models. The optimal designs are obtained based on the numerical algorithm in Hyun, Wong, Yang (2018) . Package: r-cran-optbin Architecture: arm64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 130 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-optbin_1.4-1.ca2604.1_arm64.deb Size: 37142 MD5sum: 2c5302abfb61afa878116c094b68bdc3 SHA1: 99d32481caa34f0b28a50c49db48bf3b9682ac32 SHA256: 7bab35014999d8c84ff24faef28ef31f1f952d1b0fd57b9ded0a54cb589aad30 SHA512: 296836ca2602332b52c4bbdeb35783ecc5bfe3a176d054a3da29ae7b58ba4d52f396ffa6c41c147a0121f95c74ba7f2b5f0e75eda20adb74ada879357e5d4ffc Homepage: https://cran.r-project.org/package=optbin Description: CRAN Package 'optbin' (Optimal Binning of Data) Defines thresholds for breaking data into a number of discrete levels, minimizing the (mean) squared error within all bins. Package: r-cran-optcirclust Architecture: arm64 Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1039 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 549302 MD5sum: 7a11112fdd8f91f72cfccd6a27d6b7a6 SHA1: 9eb49729821aea3cfb344de8d83bc36aec6921ea SHA256: 1998d1ddbacca4a23fd5861f084d72f72e9b83ee6844b710e63dc546d54184bf SHA512: bac387bc4dec0c5646afdefebb07e097c121c1dec5606f261855cc217ecd3083071b4f9650c247ff64024d57851be5e20bdb8c67781549708156e3e92e5daeaa Homepage: https://cran.r-project.org/package=OptCirClust Description: CRAN Package 'OptCirClust' (Circular, Periodic, or Framed Data Clustering: Fast, Optimal,and Reproducible) Fast, optimal, and reproducible clustering algorithms for circular, periodic, or framed data. The algorithms introduced here are based on a core algorithm for optimal framed clustering the authors have developed (Debnath & Song 2021) . The runtime of these algorithms is O(K N log^2 N), where K is the number of clusters and N is the number of circular data points. On a desktop computer using a single processor core, millions of data points can be grouped into a few clusters within seconds. One can apply the algorithms to characterize events along circular DNA molecules, circular RNA molecules, and circular genomes of bacteria, chloroplast, and mitochondria. One can also cluster climate data along any given longitude or latitude. Periodic data clustering can be formulated as circular clustering. The algorithms offer a general high-performance solution to circular, periodic, or framed data clustering. Package: r-cran-optgs Architecture: arm64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 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_arm64.deb Size: 58462 MD5sum: c8292dfe5d6c9ada18fbe16fac3c35be SHA1: fbb8507136e5280f016dbd40b808f17b21cf45df SHA256: 3d1d8e1b2d0b831d5d5271aac95175d3735f42ff6820cfa0d4a7220bffd11f00 SHA512: b6c2456487785dfcba29e021ca13c73c44bc1f3bc7148a16db5ea675dd7695c9023a037b9e381588e2a6e595b49f5f7cc5d0063449d5e3e78f3dbb50882af03d Homepage: https://cran.r-project.org/package=OptGS Description: CRAN Package 'OptGS' (Near-Optimal Group-Sequential Designs for Continuous Outcomes) Optimal group-sequential designs minimise some function of the expected and maximum sample size whilst controlling the type I error rate and power at a specified level. 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Package: r-cran-opthedging Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-opthedging_1.0-1.ca2604.1_arm64.deb Size: 18946 MD5sum: 410896ed36edca958250f4a43b89fe02 SHA1: 183dbe78f5b6feeba0b5d7b28fdf703f600160fc SHA256: d6baf8011861a3cf06e45f45f184e1b78a092a3d0af7fb482761cb7271f52da8 SHA512: fc4cd438a96c973006d8e24c0ce922fe04b3c46722177ab749f6a5bcb1bd685834bb4f50e66787c586de5d12d90853e36fb3d148b6136a7f7a30c7daf89dc830 Homepage: https://cran.r-project.org/package=OptHedging Description: CRAN Package 'OptHedging' (Estimation of value and hedging strategy of call and putoptions) Estimation of value and hedging strategy of call and put options, based on optimal hedging and Monte Carlo method, from Chapter 3 of 'Statistical Methods for Financial Engineering', by Bruno Remillard, CRC Press, (2013). Package: r-cran-optimalbinningwoe Architecture: arm64 Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3235 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_arm64.deb Size: 1569796 MD5sum: e33b735b158a1ed58bb7beb6e0ccc447 SHA1: 44f76f06aef15e2c6812ce631225ad78d36deb6c SHA256: 54b997b5d837995d41cf233ffffed6197a7faae009a5ee29b70bd653165b6aeb SHA512: 99e08b00ba6001eb96e74517d7ebe6795d08f230ca2dee119d2472ef1a32fd6694d1d1f0a24413597f5a67f3dc9b941f060111a9716af4870fc0363aceec2e8d Homepage: https://cran.r-project.org/package=OptimalBinningWoE Description: CRAN Package 'OptimalBinningWoE' (Optimal Binning and Weight of Evidence Framework for Modeling) High-performance implementation of 36 optimal binning algorithms (16 categorical, 20 numerical) for Weight of Evidence ('WoE') transformation, credit scoring, and risk modeling. Includes advanced methods such as Mixed Integer Linear Programming ('MILP'), Genetic Algorithms, Simulated Annealing, and Monotonic Regression. Features automatic method selection based on Information Value ('IV') maximization, strict monotonicity enforcement, and efficient handling of large datasets via 'Rcpp'. Fully integrated with the 'tidymodels' ecosystem for building robust machine learning pipelines. Based on methods described in Siddiqi (2006) and Navas-Palencia (2020) . Package: r-cran-optimization Architecture: arm64 Version: 1.0-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1017 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_arm64.deb Size: 874436 MD5sum: 6abda6d3c329da0b545ceb5cba0d44fd SHA1: 1190fbc691b6c01f1f2f10224c51869f4f08695e SHA256: 9f4d784ec0bbc7eb7411abd884d5caed3062e59f4720ff9e4a7ad4832dbed110 SHA512: 3227cfb88b7842a4893782f602dd0fba1c0511421ec2fe49f0f485a6fb7c6026ece34565bf92965e5130b1058be528b005596d1e8b5da0af383f5bb6c56aec12 Homepage: https://cran.r-project.org/package=optimization Description: CRAN Package 'optimization' (Flexible Optimization of Complex Loss Functions with State andParameter Space Constraints) Flexible optimizer with numerous input specifications for detailed parameterisation. Designed for complex loss functions with state and parameter space constraints. Visualization tools for validation and analysis of the convergence are included. Package: r-cran-optisel Architecture: arm64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3077 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_arm64.deb Size: 939810 MD5sum: 147f4b8fcfaac8267bcf3ee9c8cb6d33 SHA1: 6b89c83b507a10acb3d2a83ea9c20d06c0c2aca8 SHA256: eadaf27168515f719712ab84dee8b4ba889048c074cb1a9367558ae627a569fc SHA512: 918c473d9439c14e7f75030ec16a1728f23c45ccd56f4db4727385e825ca0151aff66e5dce593a7f1f2eb9429395ffaadd77d1a785e4e223b669efb697ee20f2 Homepage: https://cran.r-project.org/package=optiSel Description: CRAN Package 'optiSel' (Optimum Contribution Selection and Population Genetics) A framework for the optimization of breeding programs via optimum contribution selection and mate allocation. An easy to use set of function for computation of optimum contributions of selection candidates, and of the population genetic parameters to be optimized. These parameters can be estimated using pedigree or genotype information, and include kinships, kinships at native haplotype segments, and breed composition of crossbred individuals. They are suitable for managing genetic diversity, removing introgressed genetic material, and accelerating genetic gain. Additionally, functions are provided for computing genetic contributions from ancestors, inbreeding coefficients, the native effective size, the native genome equivalent, pedigree completeness, and for preparing and plotting pedigrees. The methods are described in:\n Wellmann, R., and Pfeiffer, I. (2009) .\n Wellmann, R., and Bennewitz, J. (2011) .\n Wellmann, R., Hartwig, S., Bennewitz, J. (2012) .\n de Cara, M. A. R., Villanueva, B., Toro, M. A., Fernandez, J. (2013) .\n Wellmann, R., Bennewitz, J., Meuwissen, T.H.E. (2014) .\n Wellmann, R. (2019) . Package: r-cran-optmatch Architecture: arm64 Version: 0.10.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3129 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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-tibble, r-cran-rlemon Suggests: r-cran-ritools, r-cran-boot, r-cran-biglm, r-cran-survey, r-cran-testthat, r-cran-roxygen2, r-cran-brglm, r-cran-arm, r-cran-knitr, r-cran-rmarkdown, r-cran-markdown, r-cran-pander, r-cran-xtable, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-optmatch_0.10.8-1.ca2604.1_arm64.deb Size: 1035086 MD5sum: 1fc48f967c4e3e5df82a1a034f9d6b46 SHA1: 13ba24254031a82288a4defda647336ea0593fe2 SHA256: fffeb82105e01b7921227c592a72c03e07c30235ac7d447ce1a62d85294fdcb0 SHA512: 87af5847a76e117b8bafa1d76492668541bcb0b74e7ade97699d014ef42e68e3f8cda7b35326c4b8f0b90a6402a3d0b43664706d5f0da73e42f679e01bb3d56a Homepage: https://cran.r-project.org/package=optmatch Description: CRAN Package 'optmatch' (Functions for Optimal Matching) Distance based bipartite matching using minimum cost flow, oriented to matching of treatment and control groups in observational studies ('Hansen' and 'Klopfer' 2006 ). Routines are provided to generate distances from generalised linear models (propensity score matching), formulas giving variables on which to limit matched distances, stratified or exact matching directives, or calipers, alone or in combination. Package: r-cran-opusminer Architecture: arm64 Version: 0.1-1-1.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-arules, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-opusminer_0.1-1-1.ca2604.1_arm64.deb Size: 85758 MD5sum: a170b1272a19beab5fa177d27dfde2a2 SHA1: 1d855d82385de11855197b1b425271aa95df82e6 SHA256: faff1e42480cc48ff24f03e100193adbbba2dd9f3b96a068002c5b8a800561a6 SHA512: 8bd689f924a8b40ba7e413e73b9ca7466b59fffa07764840fbc3f9ae8c9bc5c1cfdd3aee0452c6151856ed08a07e89223dd4f649a6896b558f6dec8df988fa16 Homepage: https://cran.r-project.org/package=opusminer Description: CRAN Package 'opusminer' (OPUS Miner Algorithm for Filtered Top-k Association Discovery) Provides a simple R interface to the OPUS Miner algorithm (implemented in C++) for finding the top-k productive, non-redundant itemsets from transaction data. The OPUS Miner algorithm uses the OPUS search algorithm to efficiently discover the key associations in transaction data, in the form of self-sufficient itemsets, using either leverage or lift. See for more information in relation to the OPUS Miner algorithm. 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Simulate and visualize complex orbital mechanics, celestial trajectories, and gravitational interactions using tidy data principles. Features multiple numerical integration methods, including the energy-conserving velocity Verlet algorithm (Verlet (1967) ), to ensure highly stable orbital propagation. Gravitational N-body methods follow Aarseth (2003, ISBN:0-521-43272-3). Package: r-cran-orbweaver Architecture: arm64 Version: 0.18.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2680 Depends: libc6 (>= 2.39), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-glue, r-cran-rlang Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-orbweaver_0.18.2-1.ca2604.1_arm64.deb Size: 847692 MD5sum: 4924706a4abdf0e306bceb8ff5dd9345 SHA1: a1fd6348de691645ddb09bd5f69b17caccef0740 SHA256: b7aa83f0c91a9025c9f4b43a5ade694d557d385d7ea308fdca38dd677bd59e93 SHA512: 1fd400d0089ccb1ccd782ac9578e190eed5c77e314f5aabbf680b7a4b8cc58d0908be33f4c2af2ab945bd2f34f00b5c9fd61bc6f289b3182c33f5a3c70b876ad Homepage: https://cran.r-project.org/package=orbweaver Description: CRAN Package 'orbweaver' (Fast and Efficient Graph Data Structures) Seamlessly build and manipulate graph structures, leveraging its high-performance methods for filtering, joining, and mutating data. Ensures that mutations and changes to the graph are performed in place, streamlining your workflow for optimal productivity. Package: r-cran-orca Architecture: arm64 Version: 1.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-orca_1.1-3-1.ca2604.1_arm64.deb Size: 66900 MD5sum: 740af52a6776358baf6bc9398ef923b8 SHA1: 2cdf4b6cc027a3c6fff72d46dab7b36fe69e43de SHA256: 1a3e1405a2c67065960da49e9bc9c4581b47fc4a0b177bf05b60d20650eee4e4 SHA512: 8243e758f9cecbc11ba5118b9d5dd16b3dba40107481206c8055bce31d77c3108def11e61ca072f3949efae83d1560ffbcef1e40528e804aa4973efa8e6de78a Homepage: https://cran.r-project.org/package=orca Description: CRAN Package 'orca' (Computation of Graphlet Orbit Counts in Sparse Graphs) Implements orbit counting using a fast combinatorial approach. 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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: arm64 Version: 2025.12-29-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1498 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ucminf, r-cran-mass, r-cran-matrix, r-cran-numderiv, r-cran-nlme Suggests: r-cran-lme4, r-cran-nnet, r-cran-xtable, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ordinal_2025.12-29-1.ca2604.1_arm64.deb Size: 1259384 MD5sum: a15c467c702994e48d227dbd3b16502f SHA1: 2f0b1968b24e1e8f557476fe9d7ddc4523b552d1 SHA256: 54adb3b585d8d35312b43a5203c2b19e884ba5a42ebe765d7de301b116a4f014 SHA512: c7c25d505829775299c12b46762c22b6d8c82f231c7bf8dacff6f62ffa755d155d8699a26cb1beb0f429bb904062e8b704e56b117d989a407b6e1956dd9105c7 Homepage: https://cran.r-project.org/package=ordinal Description: CRAN Package 'ordinal' (Regression Models for Ordinal Data) Implementation of cumulative link (mixed) models also known as ordered regression models, proportional odds models, proportional hazards models for grouped survival times and ordered logit/probit/... models. Estimation is via maximum likelihood and mixed models are fitted with the Laplace approximation and adaptive Gauss-Hermite quadrature. Multiple random effect terms are allowed and they may be nested, crossed or partially nested/crossed. Restrictions of symmetry and equidistance can be imposed on the thresholds (cut-points/intercepts). Standard model methods are available (summary, anova, drop-methods, step, confint, predict etc.) in addition to profile methods and slice methods for visualizing the likelihood function and checking convergence. Package: r-cran-ordinalclust Architecture: arm64 Version: 1.3.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 949 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 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_arm64.deb Size: 385242 MD5sum: ed59cad1fb41d3a405d8392873f9204b SHA1: a3ac143f1eaef118c4b06e7d0b6c8e44520a53cb SHA256: b825653432d211d4ada47c723f7a75845bea39a01fd87ff9a86642eb8d3b5a2f SHA512: 585b4d3093c5453cac87a9a1af021ec8caadfe9c920d5e4749b59693d7a8d63f0e204a93e67bbd3e3b181723e850a88bce5031001a77a29ec2e447c7ae719636 Homepage: https://cran.r-project.org/package=ordinalClust Description: CRAN Package 'ordinalClust' (Ordinal Data Clustering, Co-Clustering and Classification) Ordinal data classification, clustering and co-clustering using model-based approach with the BOS (Binary Ordinal Search) distribution for ordinal data (Christophe Biernacki and Julien Jacques (2016) ). Package: r-cran-ordinalforest Architecture: arm64 Version: 2.4-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 520 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_arm64.deb Size: 241038 MD5sum: 051a164797aec2eaf06eceed078c0cf1 SHA1: 7903311682f4345a2b7bf43a91f253532e372048 SHA256: 9e46265f51d7994d9d6f7962653d597118d62526643d43fd76474c66cb653799 SHA512: df516830aad123cfc9e63132a2161c090096aab1e8a096989aaa23526c41d4ff48f13b100a352b167d2b6f44463eca3b9f2c6f3fc92664fdadd4d234fc646b95 Homepage: https://cran.r-project.org/package=ordinalForest Description: CRAN Package 'ordinalForest' (Ordinal Forests: Prediction and Variable Ranking with OrdinalTarget Variables) The ordinal forest (OF) method allows ordinal regression with high-dimensional and low-dimensional data. After having constructed an OF prediction rule using a training dataset, it can be used to predict the values of the ordinal target variable for new observations. Moreover, by means of the (permutation-based) variable importance measure of OF, it is also possible to rank the covariates with respect to their importance in the prediction of the values of the ordinal target variable. OF is presented in Hornung (2020). NOTE: Starting with package version 2.4, it is also possible to obtain class probability predictions in addition to the class point predictions. Moreover, the variable importance values can also be based on the class probability predictions. Preliminary results indicate that this might lead to a better discrimination between influential and non-influential covariates. The main functions of the package are: ordfor() (construction of OF) and predict.ordfor() (prediction of the target variable values of new observations). References: Hornung R. (2020) Ordinal Forests. Journal of Classification 37, 4–17. . Package: r-cran-ordinalgmifs Architecture: arm64 Version: 1.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 658 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival Filename: pool/dists/resolute/main/r-cran-ordinalgmifs_1.0.9-1.ca2604.1_arm64.deb Size: 521778 MD5sum: 8491b7a2ba5817aff59f13044de32508 SHA1: c6fe9cdea92bec54fdd9715aa2298cfcb799abe1 SHA256: b6e834f33875f6c7342008ea8b54c505455bba96f39c945b941bb1bafa953459 SHA512: a283f1c5648e141c112bda905fe9d0ab6d489a808a79beb06a319e22f013a13ee611f80e1335d6bf80e1f54f2f88ee0301e86d9f5c4a9626be413c17e169913a Homepage: https://cran.r-project.org/package=ordinalgmifs Description: CRAN Package 'ordinalgmifs' (Ordinal Regression for High-Dimensional Data) Provides a function for fitting cumulative link, adjacent category, forward and backward continuation ratio, and stereotype ordinal response models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method. Package: r-cran-ordinalpattern Architecture: arm64 Version: 0.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-ordinalpattern_0.2.9-1.ca2604.1_arm64.deb Size: 58542 MD5sum: 928a559d9253e4d160a85392d4335bdb SHA1: 829961bf7c77c78542a7d6dcd9c203db9b9148b7 SHA256: e35a1dfd10192f3687e3fe4d3a263a973f4fd63b794b87bde8fcae3145c15a8c SHA512: 0c79cd0bf360b70695b6e1b982fdeb7e8042cf5beef5691c6ed86c5aeaeb7c4c4c3a0f661fdb361930e27341bcdb0635fbaa9d2006d720cf335c0ccb480dab36 Homepage: https://cran.r-project.org/package=ordinalpattern Description: CRAN Package 'ordinalpattern' (Tests Based on Ordinal Patterns) Ordinal patterns describe the dynamics of a time series by looking at the ranks of subsequent observations. By comparing ordinal patterns of two times series, Schnurr (2014) defines a robust and non-parametric dependence measure: the ordinal pattern coefficient. Functions to calculate this and a method to detect a change in the pattern coefficient proposed in Schnurr and Dehling (2017) are provided. Furthermore, the package contains a function for calculating the ordinal pattern frequencies. Generalized ordinal patterns as proposed by Schnurr and Fischer (2022) are also considered. 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The Ordered Forest flexibly estimates the conditional probabilities of models with ordered categorical outcomes (so-called ordered choice models). Additionally to common machine learning algorithms the 'orf' package provides functions for estimating marginal effects as well as statistical inference thereof and thus provides similar output as in standard econometric models for ordered choice. The core forest algorithm relies on the fast C++ forest implementation from the 'ranger' package (Wright & Ziegler, 2017) . 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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: arm64 Version: 1.0-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 285 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_arm64.deb Size: 157112 MD5sum: c8791ec3167c3fc54414e71c1cb57195 SHA1: e623304ee62b80a68304202397dcbf211098a4eb SHA256: 172a8c02057fc3ed2cd58705bd844d7679939a31d88a410cc070a71bcbf7145c SHA512: 0a95c62c2b0de3ab176b6d8dcb4a8bfeeb7ca21e45d8eb0d1178d7cc2e99997a0adb1cb6442dd8e8462efdfc1aaa114a30656f72dea6303852393a2ccc208530 Homepage: https://cran.r-project.org/package=orsk Description: CRAN Package 'orsk' (Converting Odds Ratio to Relative Risk in Cohort Studies withPartial Data Information) Reconstructs plausible 2 by 2 contingency tables from published cohort-study summaries when the original cell counts are unavailable. Given group sample sizes and an odds ratio with partial confidence interval information, the package searches for compatible event counts, then derives corresponding relative risks and confidence intervals. It implements the methods described in Wang (2013) and includes summary and plotting methods for reviewing admissible scenarios. Package: r-cran-orthodr Architecture: arm64 Version: 0.6.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1181 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_arm64.deb Size: 476372 MD5sum: 2ac192d0deb67b82eaaca3c497e46d24 SHA1: 13b0adce1f36d15bcbf044759dc1cd238c7027e5 SHA256: a3fa7b803e218f94b441338a5ff8b49aa65f8f982155c254cf20eed03d85d270 SHA512: 943a7dc1dd586a8c8ced9b380683756bca4c2beac426c5b99826dccd513e6796d1666411d655dc1b0acf0007c7ffe1712f2a5b479d14759b89eb57b58c727e61 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) . 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Package: r-cran-osc Architecture: arm64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 836 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 722866 MD5sum: a5c7cfc4d16540bfc229216c5d393d2b SHA1: 99c1bf0b6740e8b83d6cc29d644bfee42377800c SHA256: 6a5f8ad2ca4a24d354ef25be23367d9ef21e98a4977827d2758ad65b865afa19 SHA512: 12638dc36b06db61d0b5fef872ae92d022c6a04591315b19f8954b18538886dac687a0af4514e3a8d91b9ae78759c295425e1ebf33b9541f27c1f59c49928a77 Homepage: https://cran.r-project.org/package=osc Description: CRAN Package 'osc' (Orthodromic Spatial Clustering) Allows distance based spatial clustering of georeferenced data by implementing the City Clustering Algorithm - CCA. Multiple versions allow clustering for a matrix, raster and single coordinates on a plain (Euclidean distance) or on a sphere (great-circle or orthodromic distance). Package: r-cran-oscar Architecture: arm64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 828 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_arm64.deb Size: 466282 MD5sum: e85ba88131bdd9ac705f8fdeff085305 SHA1: 2e5b9be31f7e51116fdcadb51c78c4bafdd387c0 SHA256: ee0848aafef626d0bbfd501371314b1ae92705b3cf2304bb8ab015cb27cc1989 SHA512: 26c76c0c55ec3f956dce6382b141858cbe5688ce907b6ff1cab919a4daebfbd0ecd8740069e8d354bda4265b234dd8de03141c461d46b98740663358bbd7b3cc Homepage: https://cran.r-project.org/package=oscar Description: CRAN Package 'oscar' (Optimal Subset Cardinality Regression (OSCAR) Models Using theL0-Pseudonorm) Optimal Subset Cardinality Regression (OSCAR) models offer regularized linear regression using the L0-pseudonorm, conventionally known as the number of non-zero coefficients. The package estimates an optimal subset of features using the L0-penalization via cross-validation, bootstrapping and visual diagnostics. Effective Fortran implementations are offered along the package for finding optima for the DC-decomposition, which is used for transforming the discrete L0-regularized optimization problem into a continuous non-convex optimization task. These optimization modules include DBDC ('Double Bundle method for nonsmooth DC optimization' as described in Joki et al. (2018) ) and LMBM ('Limited Memory Bundle Method for large-scale nonsmooth optimization' as in Haarala et al. (2004) ). The OSCAR models are comprehensively exemplified in Halkola et al. (2023) ). Multiple regression model families are supported: Cox, logistic, and Gaussian. Package: r-cran-osfd Architecture: arm64 Version: 3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 Depends: libblas3 | libblas.so.3, libc6 (>= 2.32), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 149414 MD5sum: f3143632f7f1afb740b3db5413324d73 SHA1: 9cf7e224bbea767dd64651b8904b3cba26cdc9ac SHA256: 898c0632ef2e81c7976266efabb2c1b1785de9c21ac3a167aae848911eac462c SHA512: 8b5ad5bc46eca0287ae726711319d2eedea13da9543b4670656e1e8ae8582b9af3956921798db332654d76570a89d968acf0d2c7a6ea880d3beb0f97711f7116 Homepage: https://cran.r-project.org/package=OSFD Description: CRAN Package 'OSFD' (Output Space-Filling Design) Methods to generate a design in the input space that sequentially fills the output space of a black-box function. The output space-filling designs are helpful in inverse design or feature-based modeling problems. See Wang, Shangkun, Adam P. Generale, Surya R. Kalidindi, and V. Roshan Joseph. (2024), Sequential designs for filling output spaces, Technometrics, 66, 65–76. for details. This work is supported by U.S. National Foundation grant CMMI-1921646. 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A partition refers to an overall clustering result. Jia Li, Beomseok Seo, and Lin Lin (2019) . Lixiang Zhang, Lin Lin, and Jia Li (2020) . 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Package: r-cran-ouch Architecture: arm64 Version: 2.20-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 471 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_arm64.deb Size: 292010 MD5sum: 664120f8f805c9c0d4fd3c346d86130a SHA1: 1b33defb03bfd31ae9106ad6aedbe5e560f88ec0 SHA256: c60405ccebc8eb215cd7e9cf73163fc17f3d080127f14c8236081723de00b35f SHA512: 00384fa1322ab541e44a0e761d0e4c42708f4fc994b1ce74f79c38ee1a162b5d00668df8bd6708e023124ce5dc21769089f24de64b533849bad64d15d75050c7 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. 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Jombart T, Cori A, Didelot X, Cauchemez S, Fraser C and Ferguson N. 2014. . Campbell, F, Cori A, Ferguson N, Jombart T. 2019. . 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It is useful for the calculation of the power of equivalence tests. Package: r-cran-pac Architecture: arm64 Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 303 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-parmigene, r-cran-infotheo, r-cran-dplyr, r-cran-rtsne, r-cran-ggplot2, r-cran-ggrepel Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pac_1.1.6-1.ca2604.1_arm64.deb Size: 151526 MD5sum: 6aa3a031cde429204aadb3da3ddb2c43 SHA1: 5d1eade3d3807ec5ebdde5f02760da73d77c42de SHA256: 79f501cca9fd5b4dd30079d9f1187d5c4111b8f45e71aa8574dc6cf5a103141f SHA512: ce18412fcf2bbd8e4890c60359cc54fd1d6d3969d7c53f09c9ab683f943d6399847ee71deb4e7f209f19e45ab47888071de436451a2310923d0df370ad523a68 Homepage: https://cran.r-project.org/package=PAC Description: CRAN Package 'PAC' (Partition-Assisted Clustering and Multiple Alignments ofNetworks) Implements partition-assisted clustering and multiple alignments of networks. 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: arm64 Version: 0.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 945 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 380340 MD5sum: 916d46576d4636174ec1c0a4341a8ed4 SHA1: af79b2e3edc755485f6011cfab985cb0f39dc0e0 SHA256: a8e62c1ded7ea1897b9a5222fee399aac87a01195e0938295980ab84e9b60f1d SHA512: 827b55668110b03da645713fddbb6ffba9d7904b1309e5104efc8184b362d2324a671e55a0bec83c8f6d6c6c9f3f4812830852b295b3d3a080ee2fb4a1fb88bb Homepage: https://cran.r-project.org/package=packcircles Description: CRAN Package 'packcircles' (Circle Packing) Algorithms to find arrangements of non-overlapping circles. Package: r-cran-pacotest Architecture: arm64 Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 687 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-vinecopula, r-cran-numderiv, r-cran-ggplot2, r-cran-gridextra, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-pacotest_0.4.3-1.ca2604.1_arm64.deb Size: 337870 MD5sum: bdf3b62446f7c0acc0a48485dda01b62 SHA1: 934531e97c8370cc05f1da1b20c9a41039a1af77 SHA256: b57a41d29a8a9a5ae4ff2239587945a010b8891b1844a1849a009fbf49354eb5 SHA512: dce5265a9bf4ede70e10a7dbbbc7cf228be4334445aaf38a99e89979c65c2d708fc7ea4bc416e3bca064dea73148aca3bedb664c8d1398f58c2aade68335191d Homepage: https://cran.r-project.org/package=pacotest Description: CRAN Package 'pacotest' (Testing for Partial Copulas and the Simplifying Assumption inVine Copulas) Routines for two different test types, the Constant Conditional Correlation (CCC) test and the Vectorial Independence (VI) test are provided (Kurz and Spanhel (2022) ). The tests can be applied to check whether a conditional copula coincides with its partial copula. Functions to test whether a regular vine copula satisfies the so-called simplifying assumption or to test a single copula within a regular vine copula to be a (j-1)-th order partial copula are available. The CCC test comes with a decision tree approach to allow testing in high-dimensional settings. Package: r-cran-padr Architecture: arm64 Version: 0.6.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3347 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2840260 MD5sum: 7841c2931042e1ed104490a3d56df321 SHA1: dac5e9cc15de5dc80328f9cea2ab754aef577299 SHA256: dbfba52cf2bf0766e0d00ef70d59c9627b22108c6a7f1b02e03dbacf3092ffbc SHA512: 0b8a176477aa6dd4b1bf690647e9113a6af83481d6166d065103ec0b96d97ee535e7b4f2ac14fbcf8ae81805edbb9eacd8953ca46f00be09bf34bf698181388f Homepage: https://cran.r-project.org/package=padr Description: CRAN Package 'padr' (Quickly Get Datetime Data Ready for Analysis) Transforms datetime data into a format ready for analysis. It offers two core functionalities; aggregating data to a higher level interval (thicken) and imputing records where observations were absent (pad). Package: r-cran-pafit Architecture: arm64 Version: 1.2.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1464 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 1214780 MD5sum: cc587f86169be3fe919ecc1cb6d5435d SHA1: 6bf018815c141c9195925abafa8efbc61efd2907 SHA256: 8daa98c38868015ea47cf2e4c96caa1e2431bb6367d3f330ff9e02a48a95bdfa SHA512: b2087b48d7449980f4db387cd102b3799668d257105f237b8c722c4c5589a9e5062c2642c257505c31f707cbce9390a935fc78c0362bc679966b3123b05fa156 Homepage: https://cran.r-project.org/package=PAFit Description: CRAN Package 'PAFit' (Generative Mechanism Estimation in Temporal Complex Networks) Statistical methods for estimating preferential attachment and node fitness generative mechanisms in temporal complex networks are provided. Thong Pham et al. (2015) . Thong Pham et al. (2016) . Thong Pham et al. (2020) . Thong Pham et al. (2021) . Package: r-cran-page Architecture: arm64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 158 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-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_arm64.deb Size: 64880 MD5sum: 4d6b2267c4545c8071d806542842dd62 SHA1: 3af4962abdccfa9d0a08e48bc157bd8010d387ce SHA256: ab3180d98476e7fd152b22b08e50e4493c92b5c5c8c3726883542a465ca9ad14 SHA512: 8857207eb165e9b0adb2a062cea229e99053f68b906a6308c69c6dd41a7fd37d42d0218c41bba8fc8815afb84ca9ae15b57fb780ff091c4fdf16d2b369c4212c Homepage: https://cran.r-project.org/package=PAGE Description: CRAN Package 'PAGE' (Predictor-Assisted Graphical Models under Error-in-Variables) We consider the network structure detection for variables Y with auxiliary variables X accommodated, which are possibly subject to measurement error. The following three functions are designed to address various structures by different methods : one is NP_Graph() that is used for handling the nonlinear relationship between the responses and the covariates, another is Joint_Gaussian() that is used for correction in linear regression models via the Gaussian maximum likelihood, and the other Cond_Gaussian() is for linear regression models via conditional likelihood function. Package: r-cran-pagfl Architecture: arm64 Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1128 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 788410 MD5sum: 4be0bb590567f2ac5009ef53917e5dbe SHA1: 3f48236cf4db4b5dd36e62db99b6081c77d51030 SHA256: 468d2b0de2077fc9dfd7c1c7b45a5829f0b65175955e8a610b422c2c3af06452 SHA512: e5f8a693f6aca6a60edaa07e1974b0b46a08c6d45d9234f280e4bd4e3c609942f6bba617ce5d46aae9ff3caae63eb8bb5a2567c70966b08793abb2d37b5152b7 Homepage: https://cran.r-project.org/package=PAGFL Description: CRAN Package 'PAGFL' (Joint Estimation of Latent Groups and Group-SpecificCoefficients in (Time-Varying) Panel Data Models) Latent group structures are a common challenge in panel data analysis. Disregarding group-level heterogeneity can introduce bias. Conversely, estimating individual coefficients for each cross-sectional unit is inefficient and may lead to high uncertainty. This package addresses the issue of unobservable group structures by implementing the pairwise adaptive group fused Lasso (PAGFL) by Mehrabani (2023) . PAGFL identifies latent group structures and group-specific coefficients in a single step. On top of that, we extend the PAGFL to time-varying coefficient functions (FUSE-TIME), following Haimerl et al. (2025) . Package: r-cran-pagoda2 Architecture: arm64 Version: 1.0.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2166 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_arm64.deb Size: 1258004 MD5sum: c984a446881bbbaff151724f6d348f03 SHA1: 70ed41d80261eb5dd63045c57ac16926ae437f3a SHA256: 7951da51cc08181b013f49fc079945f2ecae7b58db9512af9d49eec57f6b3b68 SHA512: 65cdcab5fc7f0f38f369e2c4e1d3f11a13413cf2469bac965c1af6c9d6f5ccce557f6988839a79190c9cd71fed486b55d73cd68f5c0792ae984f47c99d67f6c0 Homepage: https://cran.r-project.org/package=pagoda2 Description: CRAN Package 'pagoda2' (Single Cell Analysis and Differential Expression) Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos, . This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at . The size of the 'p2data' package is approximately 6 MB. Package: r-cran-pairscale Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 149536 MD5sum: 138c2dedeef6d8c41071adfc77152fbf SHA1: af36c4296087bf0dc04bd16ffe567db89b7f7fee SHA256: a52ae3848f05a5e8b1e7c362eb936b8f88a1b512ba689f4898af053a7d278b7b SHA512: 8b8acb0f3342fb3c9d189fc47d299effc0964b28f9c5df314457d926b77baca782cb3652a2e3a43b6910c36dd8793262741f1989be92a6f588c5230efaaa0835 Homepage: https://cran.r-project.org/package=pairscale Description: CRAN Package 'pairscale' (Pairwise Rescaling of Numeric Matrices) Normalization of numerical matrices by minimizing the mean/median/mode difference between all column pairs. Package: r-cran-pak Architecture: arm64 Version: 0.9.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10549 Depends: libc6 (>= 2.38), libcurl4t64 (>= 7.74.0), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-callr, r-cran-cli, r-cran-covr, r-cran-curl, r-cran-desc, r-cran-filelock, r-cran-gitcreds, r-cran-glue, r-cran-jsonlite, r-cran-keyring, r-cran-pingr, r-cran-pkgbuild, r-cran-pkgcache, r-cran-pkgdepends, r-cran-pkgload, r-cran-pkgsearch, r-cran-processx, r-cran-ps, r-cran-rstudioapi, r-cran-testthat, r-cran-webfakes, r-cran-withr, r-cran-yaml Filename: pool/dists/resolute/main/r-cran-pak_0.9.5-1.ca2604.1_arm64.deb Size: 5782344 MD5sum: 5d6614064818fea937946dc4e8c0789d SHA1: 6d976d07bab86d2a04f1d2aaabbc52cb80219032 SHA256: b13b4db8098d6ef25cddf123528ba73ba8f51a9c3e92fbd29a7621ef8eb3ffbd SHA512: 558439a5a3e31aa424936bb3a489fad7858e0f188bb43e734602ab8cd18517e77056e37908d979d53985e6bc044dc19b0692160d5f991cbe06808242f8274160 Homepage: https://cran.r-project.org/package=pak Description: CRAN Package 'pak' (Another Approach to Package Installation) The goal of 'pak' is to make package installation faster and more reliable. In particular, it performs all HTTP operations in parallel, so metadata resolution and package downloads are fast. Metadata and package files are cached on the local disk as well. 'pak' has a dependency solver, so it finds version conflicts before performing the installation. This version of 'pak' supports CRAN, 'Bioconductor' and 'GitHub' packages as well. Package: r-cran-palm Architecture: arm64 Version: 1.1.6-1.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-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_arm64.deb Size: 216474 MD5sum: 76715e411323546ddcf290f41a576d2c SHA1: 62cb4ab1b22fb3e493225884737e2f6ca717c69d SHA256: 89baa97aeeadc694e74b2a0e7855be9e180cfba0079fb43ccd318c261fbbcd11 SHA512: bb23a6be4e100dc8757c925a20c822d4cec6daae2549c2626e35534fe09b8ccfd7aca4b929fd7dfc9a0c3377285cc501bd960c97e04d731eae8eec0573bad18b Homepage: https://cran.r-project.org/package=palm Description: CRAN Package 'palm' (Fitting Point Process Models via the Palm Likelihood) Functions to fit point process models using the Palm likelihood. First proposed by Tanaka, Ogata, and Stoyan (2008) , maximisation of the Palm likelihood can provide computationally efficient parameter estimation for point process models in situations where the full likelihood is intractable. This package is chiefly focused on Neyman-Scott point processes, but can also fit the void processes proposed by Jones-Todd et al. (2019) . The development of this package was motivated by the analysis of capture-recapture surveys on which individuals cannot be identified---the data from which can conceptually be seen as a clustered point process (Stevenson, Borchers, and Fewster, 2019 ). As such, some of the functions in this package are specifically for the estimation of cetacean density from two-camera aerial surveys. Package: r-cran-pammisc Architecture: arm64 Version: 1.13.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1066 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tuner, r-cran-seewave, r-cran-dplyr, r-cran-rcpproll, r-cran-pambinaries, r-cran-rsqlite, r-cran-lubridate, r-cran-rerddap, r-cran-ncdf4, r-cran-httr, r-cran-purrr, r-cran-xml2, r-cran-geosphere, r-cran-scales, r-cran-suncalc, r-cran-rjson, r-cran-fftw, r-cran-signal Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pammisc_1.13.0-1.ca2604.1_arm64.deb Size: 616782 MD5sum: c6d5efc1744febfbb5d75df08ee1fd69 SHA1: d2134395a6cea98f5219c4918aa2525e76683294 SHA256: f2f95fe7b8b905cc3b9175b49914bcab022c55c0b7f4f7d1a690764be0bdf002 SHA512: 6ad630921fd8191c27c20b33d13fc103f114f3dd1b6a5b203c8cecce93b072bbce435aadf9cc1b86d47909bec53720de04fa67d696d2038dc21e72ca031578b5 Homepage: https://cran.r-project.org/package=PAMmisc Description: CRAN Package 'PAMmisc' (Miscellaneous Functions for Passive Acoustic Analysis) A collection of miscellaneous functions for passive acoustics. Much of the content here is adapted to R from code written by other people. If you have any ideas of functions to add, please contact Taiki Sakai. Package: r-cran-pan Architecture: arm64 Version: 1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 698 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_arm64.deb Size: 541944 MD5sum: 8aac9944e309bb7abf443d561f339651 SHA1: 8fbd2d3ec1c4dbd10e6d3a0261188a5704c0212a SHA256: 3ec97ceaf53dbb4bc823de80b8f6a9a3ea6f9afa82638370889c6dc4097217de SHA512: 26ec934f4fb5be187c2750390f73c2a7d89e882656dd8000d95854c6d7c5c05539c9aff6d346254b61cd15474c204e6578d0b402cf656efcd9fa9cb986e906e8 Homepage: https://cran.r-project.org/package=pan Description: CRAN Package 'pan' (Multiple Imputation for Multivariate Panel or Clustered Data) It provides functions and examples for maximum likelihood estimation for generalized linear mixed models and Gibbs sampler for multivariate linear mixed models with incomplete data, as described in Schafer JL (1997) "Imputation of missing covariates under a multivariate linear mixed model". Technical report 97-04, Dept. of Statistics, The Pennsylvania State University. Package: r-cran-panacea Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2228 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_arm64.deb Size: 2045492 MD5sum: cd4f2dc38785e967433877c53114d6aa SHA1: f63ea83b7d2e72c4684b4b81e793b08fa84bf298 SHA256: 4bfc5209d1d80ce2699179dd4a77cc388469b329d57d4d2b8e6c958520ff2fb8 SHA512: cf83564491b4062657e2a1cb19e18de03155678486e2b3966322e7227722067e402e5d2dd00f671fc4444e2385c3da93db68121b59a02c6cd8e1c43b4b946432 Homepage: https://cran.r-project.org/package=PANACEA Description: CRAN Package 'PANACEA' (Personalized Network-Based Anti-Cancer Therapy Evaluation) Identification of the most appropriate pharmacotherapy for each patient based on genomic alterations is a major challenge in personalized oncology. 'PANACEA' is a collection of personalized anti-cancer drug prioritization approaches utilizing network methods. The methods utilize personalized "driverness" scores from 'driveR' to rank drugs, mapping these onto a protein-protein interaction network. The "distance-based" method scores each drug based on these scores and distances between drugs and genes to rank given drugs. The "RWR" method propagates these scores via a random-walk with restart framework to rank the drugs. The methods are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2023. PANACEA: network-based methods for pharmacotherapy prioritization in personalized oncology. Bioinformatics . Package: r-cran-pander Architecture: arm64 Version: 0.6.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1560 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 859010 MD5sum: b185550403b0918b7016a52f675b38c6 SHA1: ae1b64b390fa899ce7c87c110779097d2680d3cd SHA256: 95d8d82b92b9aa044edc44045906c4ea9f2ae3fa43dc792a33ebed7624d2c08f SHA512: cbb7b7fd292d9793984eddaed9247bb67498c4f7ef0fa949c11e6c9cf14207d0f0354f18043c2a5e750d1fdf11bf4055a5cda0108c24bee6c41d7fc3c66af8a6 Homepage: https://cran.r-project.org/package=pander Description: CRAN Package 'pander' (An R 'Pandoc' Writer) Contains some functions catching all messages, 'stdout' and other useful information while evaluating R code and other helpers to return user specified text elements (like: header, paragraph, table, image, lists etc.) in 'pandoc' markdown or several type of R objects similarly automatically transformed to markdown format. Also capable of exporting/converting (the resulting) complex 'pandoc' documents to e.g. HTML, 'PDF', 'docx' or 'odt'. This latter reporting feature is supported in brew syntax or with a custom reference class with a smarty caching 'backend'. Package: r-cran-panelcount Architecture: arm64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 418 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 250154 MD5sum: 72f877655d610bc84424cfd65a0b301f SHA1: a7d525e6f144b43b08c8655d33b6ca52635d6e29 SHA256: bd8b67675eebbbc35e8def39cd13068a555ce1e43de3620560bd4b943a809145 SHA512: e88463b8a56b04c738184cc716a86d961641a0f74c21d87a5dd315f8e9364450222dcb4afd447e0ccfc26e1424fc70ef63b8becd3e4bd8ed104ce45044e36f0b 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) . Package: r-cran-panelmatch Architecture: arm64 Version: 3.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 978 Depends: libc6 (>= 2.17), 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-cbps, r-cran-mass, r-cran-matrix, r-cran-doparallel, r-cran-foreach, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-panelmatch_3.1.3-1.ca2604.1_arm64.deb Size: 716694 MD5sum: 2599eec34043af5d42e4876e87e0768a SHA1: ec8e529bffff0e613ed0c8f1581b2a37d65642d5 SHA256: 8d17d1fec6c1542bdea0c09b8d747f2e0903c9e616923dbdc937c8789d00a8a0 SHA512: 3810a66da24ea2794ec32c7d53677150c8f6f3f937ab7b59b78dce5120735d9bba2f164be3e956bb437b71fc195e9836846877a520b478d98d4a4783b715f406 Homepage: https://cran.r-project.org/package=PanelMatch Description: CRAN Package 'PanelMatch' (Matching Methods for Causal Inference with Time-SeriesCross-Sectional Data) Implements a set of methodological tools that enable researchers to apply matching methods to time-series cross-sectional data. Imai, Kim, and Wang (2023) proposes a nonparametric generalization of the difference-in-differences estimator, which does not rely on the linearity assumption as often done in practice. Researchers first select a method of matching each treated observation for a given unit in a particular time period with control observations from other units in the same time period that have a similar treatment and covariate history. These methods include standard matching methods based on propensity score and Mahalanobis distance, as well as weighting methods. Once matching and refinement is done, treatment effects can be estimated with standard errors. The package also offers diagnostics for researchers to assess the quality of their results. Package: r-cran-panelpomp Architecture: arm64 Version: 1.7.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2033 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pomp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-bookdown Filename: pool/dists/resolute/main/r-cran-panelpomp_1.7.0.0-1.ca2604.1_arm64.deb Size: 1055812 MD5sum: bcb5c49227a6958eba0d0aad1efa23cf SHA1: f38d6e047e11a5c4cc10e5326061019b0d7f7fda SHA256: 317c49a7bda73a3ff2087d796521000c125aa7365ef6a38c7836de2ecb215db7 SHA512: cbeb79bb4ec191dadfbe1781ed27accdd5a9296e8f68c46c840bfd82da340aadd935077dd14280236f2fd1f4fcc68c4a57eee60997bc7d88bc36dcdc623fd38b Homepage: https://cran.r-project.org/package=panelPomp Description: CRAN Package 'panelPomp' (Inference for Panel Partially Observed Markov Processes) Data analysis based on panel partially-observed Markov process (PanelPOMP) models. To implement such models, simulate them and fit them to panel data, 'panelPomp' extends some of the facilities provided for time series data by the 'pomp' package. Implemented methods include filtering (panel particle filtering) and maximum likelihood estimation (Panel Iterated Filtering) as proposed in Breto, Ionides and King (2020) "Panel Data Analysis via Mechanistic Models" . Package: r-cran-panelselect Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 156352 MD5sum: cdc197a6ebfbaf8b6a4f422f9ec7db6f SHA1: 907f387435e3d3ca6b39e9d44157228a8cd96141 SHA256: 6c2f8189b73cb195f55201c831931cc33fa23539176a6c0f3380fba3f1f77c1d SHA512: 8e7f4d4166af5dbd83be339fbe1e322842063094f5572fd80b4597e5de586601dc0c1412b81f09d6f3ff7f5c61f4033c04d1f2c4711579b68f1602f921e4ef1e Homepage: https://cran.r-project.org/package=PanelSelect Description: CRAN Package 'PanelSelect' (Panel Sample Selection Models) Extends the Heckman selection framework to panel data with individual random effects. The first stage models participation via a panel Probit specification, while the second stage can take a panel linear, Probit, Poisson, or Poisson log-normal form. Model details are provided in Bailey and Peng (2025) and Peng and Van den Bulte (2024) . Package: r-cran-panprsnext Architecture: arm64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1946 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 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_arm64.deb Size: 1791144 MD5sum: 6903d56bc0215dfb968e24b9c2992f84 SHA1: 97a27dd25267073d83ad324d7560f4c433a86c1e SHA256: a8248859cf0d954ca026436f0b6a33298f25368b13ff3f0ccc3120347f7bc740 SHA512: 07ead8b37468de3bb84c2bc08f8a367e5447c54e3d945051741f0c067cabed3f82beac4fdc23ff98eb57e061f77ecc0ca726394c7c820dc21a918aa1db1c4947 Homepage: https://cran.r-project.org/package=PANPRSnext Description: CRAN Package 'PANPRSnext' (Building PRS Models Based on Summary Statistics of GWAs) Shrinkage estimator for polygenic risk prediction (PRS) models based on summary statistics of genome-wide association (GWA) studies. Based upon the methods and original 'PANPRS' package as found in: Chen, Chatterjee, Landi, and Shi (2020) . Package: r-cran-paralleldist Architecture: arm64 Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 811 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 448106 MD5sum: 7422b8a74d28100492aab3c3e11b97b6 SHA1: 012a35af2aa78515be830eb01da2c6d5dab7c3a4 SHA256: 4c6a5f0154c84de352a293ef6ba99d2a0ec096f08ba8bb581cb84f59a33ed1a7 SHA512: 541480e19c8fb86447ee7942136a4329d6670f97abda62b473ed267022ec075c1fef8251e059fbcb15d9fbf0400a986b9ef1185d1b2266aa0d6b8c6208931646 Homepage: https://cran.r-project.org/package=parallelDist Description: CRAN Package 'parallelDist' (Parallel Distance Matrix Computation using Multiple Threads) A fast parallelized alternative to R's native 'dist' function to calculate distance matrices for continuous, binary, and multi-dimensional input matrices, which supports a broad variety of 41 predefined distance functions from the 'stats', 'proxy' and 'dtw' R packages, as well as user- defined functions written in C++. For ease of use, the 'parDist' function extends the signature of the 'dist' function and uses the same parameter naming conventions as distance methods of existing R packages. The package is mainly implemented in C++ and leverages the 'RcppParallel' package to parallelize the distance computations with the help of the 'TinyThread' library. Furthermore, the 'Armadillo' linear algebra library is used for optimized matrix operations during distance calculations. The curiously recurring template pattern (CRTP) technique is applied to avoid virtual functions, which improves the Dynamic Time Warping calculations while the implementation stays flexible enough to support different DTW step patterns and normalization methods. Package: r-cran-parallelly Architecture: arm64 Version: 1.47.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1034 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-commonmark, r-cran-base64enc Filename: pool/dists/resolute/main/r-cran-parallelly_1.47.0-1.ca2604.1_arm64.deb Size: 605136 MD5sum: 1caecce30e31e82e021152d9157a5ae3 SHA1: a437f9bc00b973b570d011a9bb856c1d98e86980 SHA256: 9bce07cc86a0d1c95a67f2d8fe9fbeaae306d4d60c659241212edde5bf3ad815 SHA512: a341c3942e85e8835603d2a424ee837feecaff5e3e43fdccbab3cb79db8ff751d587c9698842d1906d36054722a33d37b4f1a3f2701cbdb3a54e52211506bdfa Homepage: https://cran.r-project.org/package=parallelly Description: CRAN Package 'parallelly' (Enhancing the 'parallel' Package) Utility functions that enhance the 'parallel' package and support the built-in parallel backends of the 'future' package. For example, availableCores() gives the number of CPU cores available to your R process as given by the operating system, 'cgroups' and Linux containers, R options, and environment variables, including those set by job schedulers on high-performance compute clusters. If none is set, it will fall back to parallel::detectCores(). Another example is makeClusterPSOCK(), which is backward compatible with parallel::makePSOCKcluster() while doing a better job in setting up remote cluster workers without the need for configuring the firewall to do port-forwarding to your local computer. Package: r-cran-parallelpam Architecture: arm64 Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1979 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_arm64.deb Size: 466470 MD5sum: 4c956049955ea8eebf206b8e41ae673b SHA1: 3861011a36b4b0bfe4277f3e00e607940222e533 SHA256: 90023bf82828ede8a0249a3ed0e3fab33c264fa37183b6877d07b06b28a27e6f SHA512: 7990008cce37e3623afc940de0afdccaec47481322f108e5a01533414a888df98b026e5bd4a79bf2e4fb0880f13c5a6a665af8c63fff0c5a92afa3dc1e7fc0cd 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: arm64 Version: 1.14.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 559 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_arm64.deb Size: 434374 MD5sum: 6be46083bf27eb1cae75855104d362fe SHA1: ccd82b7200e65cec48bce37ebacade3728b0945b SHA256: d684ff22b29353626c7939571e477c9a15fce65fa509d2317fd02571aa9cd9e7 SHA512: 52eef7910a7f74c5e24bb7a837931e8a9c2dad3792473bfeb36937eb4234ec719c63ac307ec7471565715d056c17b9573c0dc5d00670cffb03a2bad545fab08c 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 ). Package: r-cran-particles Architecture: arm64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1417 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-digest, r-cran-dplyr, r-cran-igraph, r-cran-mgcv, r-cran-rlang, r-cran-tidygraph, r-cran-cpp11 Suggests: r-cran-covr, r-cran-ggraph, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-particles_0.2.4-1.ca2604.1_arm64.deb Size: 1000678 MD5sum: ff5a74bc22bca7a37bbc352bb139cbce SHA1: e6f9a05e18eba4e10564abf55688c4f485b61257 SHA256: a1d1c40988f601eb86975ee54e814cdbf526caad58f8358fb8b1112cdc050b13 SHA512: 1d18f46661a8f0ee81a30aab200b3339bfec1303fa300061c50cbfb167a3651c324e4bc7ca8a3ae98a2c90bc48ef3ad6bbd19c1fd22e5b3b24681a33db7275ce Homepage: https://cran.r-project.org/package=particles Description: CRAN Package 'particles' (A Graph Based Particle Simulator Based on D3-Force) Simulating particle movement in 2D space has many application. The 'particles' package implements a particle simulator based on the ideas behind the 'd3-force' 'JavaScript' library. 'particles' implements all forces defined in 'd3-force' as well as others such as vector fields, traps, and attractors. Package: r-cran-partimeroc Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2635 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 930088 MD5sum: 3ff73820289b24981f716fbfc9662db8 SHA1: fcba6cd36b0b84a06278fa215869529c711b0e2c SHA256: 1e8be9dd1b959ff22a85f86ade51fd9082bc085a57df0f3c77d617272d54b598 SHA512: 10621dc35263e8b4aed1e736799249e425f9515c93a33fdaca083f004795d4a4512076d58c67f168342727d444311d65ad886e5519f3cc465ca2985646fd1cb0 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: arm64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2298 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_arm64.deb Size: 1561476 MD5sum: bab221ca0bb78498ae20154154347eb0 SHA1: 19af55272b1eec44bd072aaef1bd94ca03971ada SHA256: 7c83b1b8da6b1e8b45eb4fe8cf786f6e3eb7b1db560c7f86ecbcbc0d60ed4b47 SHA512: 41c079f434ee960a6c6de1597a4880cfacc8db8e8d46dd490ba5386aea5866301cc0e9cb8c139c5a62eb1b80864d4132ba58c548b7e5d0ac092b74648edf5b8d Homepage: https://cran.r-project.org/package=partition Description: CRAN Package 'partition' (Agglomerative Partitioning Framework for Dimension Reduction) A fast and flexible framework for agglomerative partitioning. 'partition' uses an approach called Direct-Measure-Reduce to create new variables that maintain the user-specified minimum level of information. Each reduced variable is also interpretable: the original variables map to one and only one variable in the reduced data set. 'partition' is flexible, as well: how variables are selected to reduce, how information loss is measured, and the way data is reduced can all be customized. 'partition' is based on the Partition framework discussed in Millstein et al. (2020) . Package: r-cran-partitions Architecture: arm64 Version: 1.10-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 679 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gmp, r-cran-polynom, r-cran-sets, r-cran-rdpack Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-partitions_1.10-9-1.ca2604.1_arm64.deb Size: 510574 MD5sum: a5899bd6dc4452c96472040d0eb41f2b SHA1: fbe02af1530c0f28e61a944d0236064466b5b49d SHA256: 916fb19e2e6833351e8f137f99b08d5e2b93914f360565330fef5fc808121355 SHA512: 24cf140a1693fb8c2ecc9a80f636b89ea02e06f711445ed383571991b1deb7d5a01872e173249ffe45adf5aa1fcbe53eabe5680ce53cba2ce0a5335e8a247f28 Homepage: https://cran.r-project.org/package=partitions Description: CRAN Package 'partitions' (Additive Partitions of Integers) Additive partitions of integers. Enumerates the partitions, unequal partitions, and restricted partitions of an integer; the three corresponding partition functions are also given. Set partitions and now compositions and riffle shuffles are included. Package: r-cran-party Architecture: arm64 Version: 1.3-20-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1233 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 899360 MD5sum: 35101308a8d904d76670f739f8a0992c SHA1: 6258b4e49673e8a6b942333db4bfe7d19bda3147 SHA256: b3c5efc9c44a9acde28c4c2980c13e50bc8ba163c3cda97e4aebce04cd4d10e1 SHA512: 1c2fd070c773df36a596678c66d2c9c65917c825a185ce5ae1dd48c9f409a239c7a8504de6d8b041e6d2f59286c3ad91e56a2e54a9caaf7719c8e0ad172b4851 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: arm64 Version: 1.2-27-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3234 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2339304 MD5sum: e057e0180772150c71e9a1ea1184c6ff SHA1: 045f6281e6b242a5a7f8e06f49456812e17fc4d7 SHA256: e8d19610cc82b5bc90b58a926e49533bf6be6ef1e6013402bccecaeffdd0d868 SHA512: ce03757e4ce8184f93adb2e9b52795262b8e0132fb63be4b41af8e11470c854a945f830226b7c1a10f36e9bdd8c9894772fb3a46daf01f6520250b02e57d2c94 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: arm64 Version: 0.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 921 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_arm64.deb Size: 270508 MD5sum: 680dadc15c66afc10e97092453686e12 SHA1: d180650b0ee50187c07f915597c06560ab92b626 SHA256: 9b02002fc29b59c0fe13e3a33f02108efd3beb7f028a27766151afd46d3cdc53 SHA512: faff1b721549aa3e5e31acb8679e6c7012f9097f7df53c68fc64fe207585b8f9d244b1e929e836bf5d675e0f54806af671214da01c5a4a0f057c4327c784a2cc 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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Despite its simplicity, Boolean network modeling has been a successful method to describe the behavioral pattern of various phenomena. Applying stochastic noise to Boolean networks is a useful approach for representing the effects of various perturbing stimuli on complex systems. A number of methods have been developed to control noise effects on Boolean networks using parameters integrated into the update rules. This package provides functions to examine three such methods: Boolean network with perturbations (BNp), described by Trairatphisan et al. (2013) , stochastic discrete dynamical systems (SDDS), proposed by Murrugarra et al. (2012) , and Boolean network with probabilistic edge weights (PEW), presented by Deritei et al. (2022) . This package includes source code derived from the 'BoolNet' package, which is licensed under the Artistic License 2.0. 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Package: r-cran-pbsddesolve Architecture: arm64 Version: 1.13.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 209630 MD5sum: 016704e80305ae26560caf452a6cfda5 SHA1: f9462b1dd4bff2d822c8c98087e9f0b098c9a070 SHA256: 7f127102f3e4c80130e8c176b47cec1bd2dee1f42fe4f5007c398f517d55c31a SHA512: cfe5233671d44e2992afc0cd38c0c39df7b323381630123a8fbd3faae8b756008f4ce45ff4df29dc2186cfb08cd274735a01df96f3a1a1fcda8f42d7e238272b 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: arm64 Version: 2.74.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5631 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_arm64.deb Size: 4728066 MD5sum: 2f0f1d6a4e05965947490d021379c27b SHA1: 82a03ab7dc1e729ce413e5dce5636a70cd92a32b SHA256: 75fd81df3f9ea435fb6ccbf33393b0dff2cb97453b200b6641e1878765d26c23 SHA512: 2bc1e957cf84d1baa66ba7792f58b4b2386583f7a4185480eb39399f0740b8773f58817d27d51a3f21569677c9e08e74d0249018630b2c547118ce44d78a8c44 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. It extends the R language to include two-dimensional plotting features similar to those commonly available in a Geographic Information System (GIS). Embedded C code speeds algorithms from computational geometry, such as finding polygons that contain specified point events or converting between longitude-latitude and Universal Transverse Mercator (UTM) coordinates. Additionally, we include 'C++' code developed by Angus Johnson for the 'Clipper' library, data for a global shoreline, and other data sets in the public domain. Under the user's R library directory '.libPaths()', specifically in './PBSmapping/doc', a complete user's guide is offered and should be consulted to use package functions effectively. Package: r-cran-pbsmodelling Architecture: arm64 Version: 2.70.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5064 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml Suggests: r-cran-pbsmapping, r-cran-desolve, r-cran-kernsmooth Filename: pool/dists/resolute/main/r-cran-pbsmodelling_2.70.2-1.ca2604.1_arm64.deb Size: 3629212 MD5sum: aa5a814360166f2aae95e01b0f6ff3f0 SHA1: 5661c467602891fbbf5ad6882f1d46f3ddc675a0 SHA256: 1216316a859c50e6ef51b99587dbc4412646e3d16b7c54d4f4859929bc4680d2 SHA512: 3c92dd1661762711da812120a1b0a6f569e7f8b995c1b37eeebe0bf70e00291a4f1752acd85ca98b0ea26adc36ec7de72aaa9b4a42a3d509825bbe89476935af Homepage: https://cran.r-project.org/package=PBSmodelling Description: CRAN Package 'PBSmodelling' (GUI Tools Made Easy: Interact with Models and Explore Data) Provides software to facilitate the design, testing, and operation of computer models. It focuses particularly on tools that make it easy to construct and edit a customized graphical user interface ('GUI'). Although our simplified 'GUI' language depends heavily on the R interface to the 'Tcl/Tk' package, a user does not need to know 'Tcl/Tk'. Examples illustrate models built with other R packages, including 'PBSmapping', 'PBSddesolve', and 'BRugs'. A complete user's guide 'PBSmodelling-UG.pdf' shows how to use this package effectively. Package: r-cran-pbv Architecture: arm64 Version: 0.5-47-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 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-pbv_0.5-47-1.ca2604.1_arm64.deb Size: 46494 MD5sum: f6df9b45a0d46caf086f43f23afa44bb SHA1: 2776e0f14d10510ee92a0024641628f5751971d7 SHA256: 95b686944ccccdcb5fe8672b76ee6517e9a65bce91745010f847d0cdba8441be SHA512: 38dcf7e95bf30d3cb8a2e4b6ec0a6bf4bd5bf55a482e46e34dcbd861ca1a463cae7ca69f654f4f25e6234fbb89d18e89614e9e689b0be7b5cc5f09a5ab1c526f Homepage: https://cran.r-project.org/package=pbv Description: CRAN Package 'pbv' (Probabilities for Bivariate Normal Distribution) Computes probabilities of the bivariate normal distribution in a vectorized R function (Drezner & Wesolowsky, 1990, ). Package: r-cran-pc Architecture: arm64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2617 Depends: 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-sdsfun, r-cran-sf, r-cran-terra, r-cran-rcpp, r-cran-rcppthread Suggests: r-cran-readr, r-cran-infoxtr, r-cran-spedm, r-cran-tedm Filename: pool/dists/resolute/main/r-cran-pc_0.2-1.ca2604.1_arm64.deb Size: 1378842 MD5sum: a7c0becf635b9f3d87411b28ac4518ba SHA1: 38f2f6d01a5e85b933870b0c9a008e60de90ff42 SHA256: f53e6645706fb4845cac45f5855de816b9b4ec7b09a74a5abb64a6e84aefe866 SHA512: eadb0b1b0c876e9bc4c5d6768cb461b0bcd7b458a7cfdb0dc71b0f9bac0b390b2ab4287f2efea2cdb9d7f3769819873b1a7cbc46ff3fe191686c416d3dd13112 Homepage: https://cran.r-project.org/package=pc Description: CRAN Package 'pc' (Pattern Causality Analysis) Infer causation from observational data through pattern causality analysis (PC), with original algorithm for time series data from Stavroglou et al. (2020) , as well as methodological extensions for spatial cross-sectional data introduced by Zhang & Wang (2025) , together with a systematic description proposed in Lyu et al. (2026) . Package: r-cran-pcadapt Architecture: arm64 Version: 4.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3707 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bigutilsr, r-cran-data.table, r-cran-ggplot2, r-cran-magrittr, r-cran-mmapcharr, r-cran-rcpp, r-cran-rspectra, r-cran-rmio Suggests: r-cran-plotly, r-cran-shiny, r-cran-spelling, r-cran-testthat, r-cran-vcfr Filename: pool/dists/resolute/main/r-cran-pcadapt_4.4.1-1.ca2604.1_arm64.deb Size: 1674152 MD5sum: 9759969a42f3675d37e6582e45818b0d SHA1: 452445c92d51fafa37ad2ed16081804c8567bc15 SHA256: 7dc23c218dc55099add6ec68879e376b6ff3dcf3eb971ad7ad8c2abf9fc3a7a3 SHA512: e93e883a3c832fb9d20cc3ad7cef0426052902c3e6b917ef20bad10e4cd62bcc6d3a6e580c3a32e46b061e96041607c55c770b189f5af8fcc4c3911b85f3f3da Homepage: https://cran.r-project.org/package=pcadapt Description: CRAN Package 'pcadapt' (Fast Principal Component Analysis for Outlier Detection) Methods to detect genetic markers involved in biological adaptation. 'pcadapt' provides statistical tools for outlier detection based on Principal Component Analysis. Implements the method described in (Luu, 2016) and later revised in (Privé, 2020) . Package: r-cran-pcal1 Architecture: arm64 Version: 1.5.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 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_arm64.deb Size: 144912 MD5sum: 3ec72c6d391f8e512cc725d9c05b25ef SHA1: 8a85aedc8b3279334024d68b1151a15cd0b27602 SHA256: 2a4083d5dc5be82ac32ab410da9ca37d0ceaff8060e99e5a7f3cff407b7a8ce0 SHA512: 95f9438e4e934f3ed6e9f9d12f14baff0ae9e56cf7677580e9333f100d9e1a65fea6643c5bf7e5c5fcc0d2075d016837100c89c2cef83aa1d062c494c8cec37b Homepage: https://cran.r-project.org/package=pcaL1 Description: CRAN Package 'pcaL1' (L1-Norm PCA Methods) Implementations of several methods for principal component analysis using the L1 norm. The package depends on COIN-OR Clp version >= 1.17.4. The methods implemented are PCA-L1 (Kwak 2008) , L1-PCA (Ke and Kanade 2003, 2005) , L1-PCA* (Brooks, Dula, and Boone 2013) , L1-PCAhp (Visentin, Prestwich and Armagan 2016) , wPCA (Park and Klabjan 2016) , awPCA (Park and Klabjan 2016) , PCA-Lp (Kwak 2014) , and SharpEl1-PCA (Brooks and Dula, submitted). Package: r-cran-pcalg Architecture: arm64 Version: 2.7-12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5262 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-abind, r-bioc-graph, r-bioc-rbgl, r-cran-igraph, r-cran-ggm, r-cran-corpcor, r-cran-robustbase, r-cran-vcd, r-cran-rcpp, r-cran-bdsmatrix, r-cran-sfsmisc, r-cran-fastica, r-cran-clue, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-mass, r-cran-matrix, r-bioc-rgraphviz, r-cran-mvtnorm, r-cran-huge, r-cran-ggplot2, r-cran-dagitty Filename: pool/dists/resolute/main/r-cran-pcalg_2.7-12-1.ca2604.1_arm64.deb Size: 4809418 MD5sum: edf948f82afe7b13d6be0d859baa8312 SHA1: bb48428f87a6ff786898180ee634a0524255b146 SHA256: 32e52f36e3b9af46327af7f60243b442f6f3707af8a34a1542ea997a1bd52992 SHA512: 2c894547a5e75d8682c8673098c560f2c6eee8f4b6861cb1be0359b819e0bc74990064316721ae4b7e21095590b5c496e61aa1dc97119683e5466e6662e55621 Homepage: https://cran.r-project.org/package=pcalg Description: CRAN Package 'pcalg' (Methods for Graphical Models and Causal Inference) Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided. Package: r-cran-pcaone Architecture: arm64 Version: 1.1.0-1.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-rcpp, r-cran-rcppeigen Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-pcaone_1.1.0-1.ca2604.1_arm64.deb Size: 114454 MD5sum: 4c70e13d4e6cf86c01c6daa15196e297 SHA1: a48da5e13a3dc9bfe4424ae562c0279f48a04f98 SHA256: 717994d890a3ad09e9b6cffd9a049dfaffd9046cd7797e344642517ae6aa7876 SHA512: e84f2326d74eb034d75f010207c7d7a0c7622ebe5c9e45b63f784f112b2640c744e31cd2766db9a44a21bf3d0eed9d8ba11ebb0b65795293ecc96b7821165866 Homepage: https://cran.r-project.org/package=pcaone Description: CRAN Package 'pcaone' (Fast and Accurate Randomized Singular Value DecompositionAlgorithms with 'PCAone') Fast and Accurate Randomized Singular Value Decomposition (RSVD) methods proposed in the 'PCAone' paper by Li (2023) . Package: r-cran-pcapp Architecture: arm64 Version: 2.0-5-1.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 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm Suggests: r-cran-robustbase Filename: pool/dists/resolute/main/r-cran-pcapp_2.0-5-1.ca2604.1_arm64.deb Size: 359436 MD5sum: bfe018f6d1f3dc9f178d2374cd25ed21 SHA1: fb5abd7cc99e9fc6bdfb05451cca9caa86c855df SHA256: 7b17a3f775f144bfdbb27870d85383a41a7e83d653981f00795372eb74ba7bc7 SHA512: a94232480e4a53d613938d4096f65ac68f56392995859f8f925e408de4aafff8dea9b15eb35783f7ddb571695e8eab2fc35d4f22e14a70e491f91d0efa4a945a Homepage: https://cran.r-project.org/package=pcaPP Description: CRAN Package 'pcaPP' (Robust PCA by Projection Pursuit) Provides functions for robust PCA by projection pursuit. The methods are described in Croux et al. (2006) , Croux et al. (2013) , Todorov and Filzmoser (2013) . Package: r-cran-pccc Architecture: arm64 Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2179 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-rcpp Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-readr Filename: pool/dists/resolute/main/r-cran-pccc_1.0.7-1.ca2604.1_arm64.deb Size: 1269514 MD5sum: bf341ad9f84fb06168ddd4fae7b8d8bb SHA1: 4aa4610ee624b5f0afc15ed23c7947cffe1ec874 SHA256: 6fb51c2f20e0a67dcb9b3bb31b4a359f26a680a8e1de6d5ee8f074791c5b8a14 SHA512: 5c3fcece0f7a1953b323e43a3834eaebd46c3793c288f2b52dfe6eea1f652e789adbc47a3bd904e9a7c6ebee14b79731adad380c935bd1a71468dec76468a854 Homepage: https://cran.r-project.org/package=pccc Description: CRAN Package 'pccc' (Pediatric Complex Chronic Conditions) An implementation of the pediatric complex chronic conditions (CCC) classification system using R and C++. 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Its purpose is to make fitting paired comparison data using Stan easy. This package is described in Pritikin (2020) . Package: r-cran-pclasso Architecture: arm64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-svd Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pclasso_1.2-1.ca2604.1_arm64.deb Size: 144854 MD5sum: 9e802dcbeae6a41545dae685b4107ec2 SHA1: ed4606aed1d02c68f92a792d6d14a14d2f7f0fb8 SHA256: 4495926b4bdb4da295bc778abb4ae15bf98d579e56894eaa3e8479fc2c9c0be8 SHA512: 37c9ed96e441fddcfc38a5e885fd8767768664f3b0cf70db1d4e941c7fab0babc34e40e0c82249d287f91defd11ced15335197f4d480a968d072a85bcd1e744b Homepage: https://cran.r-project.org/package=pcLasso Description: CRAN Package 'pcLasso' (Principal Components Lasso) A method for fitting the entire regularization path of the principal components lasso for linear and logistic regression models. The algorithm uses cyclic coordinate descent in a path-wise fashion. See URL below for more information on the algorithm. See Tay, K., Friedman, J. ,Tibshirani, R., (2014) 'Principal component-guided sparse regression' . Package: r-cran-pcmbasecpp Architecture: arm64 Version: 0.1.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3719 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-pcmbase, r-cran-data.table, r-cran-abind, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-pcmbasecpp_0.1.12-1.ca2604.1_arm64.deb Size: 1426298 MD5sum: 7438cd0d5f5aba427ea738e961e893ce SHA1: d942e6b7e61d1337a066ae550e8c7172dd06bd3a SHA256: 6e287cc0b19889597f4bbe292dfc74b65f76eeacaa3376e927d30c2d677ae024 SHA512: 2db8ea16eaa7027d79232d20e0337b0e6b399861b66649be181c92c51e613fb034b55b6cd3a9dad11c025920e22073c3c4b9ab519c200bfa4df3d4d84f2760e3 Homepage: https://cran.r-project.org/package=PCMBaseCpp Description: CRAN Package 'PCMBaseCpp' (Fast Likelihood Calculation for Phylogenetic Comparative Models) Provides a C++ backend for multivariate phylogenetic comparative models implemented in the R-package 'PCMBase'. Can be used in combination with 'PCMBase' to enable fast and parallel likelihood calculation. Implements the pruning likelihood calculation algorithm described in Mitov et al. (2020) . Uses the 'SPLITT' C++ library for parallel tree traversal described in Mitov and Stadler (2018) . Package: r-cran-pcmrs Architecture: arm64 Version: 0.1-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ltm, r-cran-statmod, r-cran-cubature, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-pcmrs_0.1-5-1.ca2604.1_arm64.deb Size: 130948 MD5sum: 1adf2bba3339cdc269f8c349db072e27 SHA1: 3a74fd4cb4858d00ee26fc3e7310f1756f06e43c SHA256: e975e5c4e435aa06890e53449f2805e04f34128eec5fb0986724603d9aaa82ce SHA512: eb1e3c13248e6e39865caf6d1fd9ede35773e3a1f4d44e95c86d3e2ad289eebaec3e5979aea0cd1b7f2725629afbf012437113ada76a2b2c7913e55e3a4a9630 Homepage: https://cran.r-project.org/package=PCMRS Description: CRAN Package 'PCMRS' (Model Response Styles in Partial Credit Models) Implementation of PCMRS (Partial Credit Model with Response Styles) as proposed in by Tutz, Schauberger and Berger (2018) . PCMRS is an extension of the regular partial credit model. PCMRS allows for an additional person parameter that characterizes the response style of the person. By taking the response style into account, the estimates of the item parameters are less biased than in partial credit models. Package: r-cran-pcobw Architecture: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 568 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_arm64.deb Size: 197886 MD5sum: 20aea3a8bc1bfb48a1cdce222795d7e5 SHA1: 7fff1d0ebc83e5fafa8ad9d9a8daef7a6eec7b22 SHA256: 67144608d5f4fc4533877ae0247299ecf43cab78a9a5ddd4811d6bcaef148d9e SHA512: ddadc238aab2d507bfc91c7c1f33969b30a09990f522c01ea27ccd0879f62bad3f806eb49c925f0751ad473d85b38162b695fce23689eec8a9a288856d816a4d Homepage: https://cran.r-project.org/package=PCObw Description: CRAN Package 'PCObw' (Bandwidth Selector with Penalized Comparison to OverfittingCriterion) Bandwidth selector according to the Penalised Comparison to Overfitting (P.C.O.) criterion as described in Varet, S., Lacour, C., Massart, P., Rivoirard, V., (2019) . It can be used with univariate and multivariate data. Package: r-cran-pcplus Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 336 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 138500 MD5sum: 97bd1267298dd82e1b0981a8ce35cd1b SHA1: 9e497f90622396bea1f8c76ec0b86ee61e3ee3e4 SHA256: 6919a9bcc2a35661e99ecbeb0843389c87382c804c233c7345c1f68a871d3eaa SHA512: ef24e2d154b7fdd44bffdd4e065af2b7c0b7521c3546b9d824a0ca9cce91475c030eefdd42a7bcefe03710a2b27b5211be7189201db13a68ce027c854f2897fa 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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Package: r-cran-pda Architecture: arm64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1193 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-httr, r-cran-rvest, r-cran-jsonlite, r-cran-data.table, r-cran-cobalt, r-cran-empiricalcalibration, r-cran-survival, r-cran-minqa, r-cran-glmnet, r-cran-mass, r-cran-numderiv, r-cran-metafor, r-cran-matrix, r-cran-ordinal, r-cran-plyr, r-cran-tidyr, r-cran-tibble, r-cran-dplyr, r-cran-geex, r-cran-data.tree, r-cran-rcpparmadillo, r-cran-rcppeigen Suggests: r-cran-lme4 Filename: pool/dists/resolute/main/r-cran-pda_1.3.0-1.ca2604.1_arm64.deb Size: 875010 MD5sum: 0c85572cd46e00457d4db1e40308a3fa SHA1: c2b8b880ae2004b05ee891b4152d6bcf2ac0fbde SHA256: c2831e39e3e7033d876cb8d16847e9218ee6fe206d794209020dcbfa516e1c85 SHA512: 3bad09408cbd10003675afa90e674992432b94093a0626cdc38e905b47df5a9acbccedab4b9fc7812759b64b68f1f9fb28af7d05465a04a40d2afe5344df4269 Homepage: https://cran.r-project.org/package=pda Description: CRAN Package 'pda' (Privacy-Preserving Distributed Algorithms) A collection of privacy-preserving distributed algorithms (PDAs) for conducting federated statistical learning across multiple data sites. The PDA framework includes models for various tasks such as regression, trial emulation, causal inference, design-specific analysis, and clustering. The PDA algorithms run on a lead site and only require summary statistics from collaborating sites, with one or few iterations. The package can be used together with the online data transfer system () for safe and convenient collaboration. For more information, please visit our software websites: , and . Package: r-cran-pdc Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-plotrix, r-cran-lattice Filename: pool/dists/resolute/main/r-cran-pdc_1.0.3-1.ca2604.1_arm64.deb Size: 190688 MD5sum: e0570f28297f435bed8fcf3c623c1cd6 SHA1: d1956b1b1592dcfd155a530f61d0935634a69383 SHA256: e527a78627905c8c0f531fd9ba1b2e4712ba8fbb41823c7644e5c03d59b711d2 SHA512: b2a2ea6e8059065d00e9961eed941dd4a7b5670b215b2cd13277312f8b93c69551656051a4953a3274e521cb406e1cdb14f214ca61ebb7d0000e8576a30449e9 Homepage: https://cran.r-project.org/package=pdc Description: CRAN Package 'pdc' (Permutation Distribution Clustering) Permutation Distribution Clustering is a clustering method for time series. Dissimilarity of time series is formalized as the divergence between their permutation distributions. 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Package: r-cran-pdenaivebayes Architecture: arm64 Version: 0.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1407 Depends: libc6 (>= 2.17), 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-pracma, r-cran-plotly, r-cran-ggplot2, r-cran-databionicswarm, r-cran-memshare Suggests: r-cran-fcps, r-cran-abcanalysis, r-cran-modeest, r-cran-deldir, r-cran-scatterdensity, r-cran-gridextra, r-cran-paralleldist, r-cran-datavisualizations, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-pdenaivebayes_0.2.9-1.ca2604.1_arm64.deb Size: 614212 MD5sum: 34e5a7f942f3f09cd706e52d8cf5ae0b SHA1: 18f9aeb31fd4af49e0a814ceee923b36450d958d SHA256: 3d9c19038b2bed32b48e54abb5c51ea67f2209f92bf06d48837c9365a0ee6b49 SHA512: c23163638edfb78d61854e364cf1f7f838321eaabbdbf40b52d7c6fed0d73f8a1d7023f358f1bb8f95a166c86936538de1fe25108f3b99b4abaa9f5999b73d3f Homepage: https://cran.r-project.org/package=PDEnaiveBayes Description: CRAN Package 'PDEnaiveBayes' (Plausible Naive Bayes Classifier Using PDE) A nonparametric, multicore-capable plausible naive Bayes classifier based on the Pareto density estimation (PDE) featuring a plausible approach to a pitfall in the Bayesian theorem covering low evidence cases. 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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Previously, this could be done by binding the two matrices together and calling 'dist', but this creates unnecessary computation by computing the distances between a row of X and another row of X, and likewise for Y. pdist strictly computes distances across the two matrices, not within the same matrix, making computations significantly faster for certain use cases. 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Package: r-cran-peakerror Architecture: arm64 Version: 2023.9.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-peakerror_2023.9.4-1.ca2604.1_arm64.deb Size: 26164 MD5sum: 4e942830abad1a108a0d2baac56527d2 SHA1: 0b36ee00ee7505ab3191e8a8a9edc3c8afa83666 SHA256: 076553addd3d13478eb81f81fba52e9058a4fd9c33bebaf9715342ad409efb64 SHA512: bc34abc48b165b2172f83dc0a4a5e17d27cc4251bddf00d1e7d3f08cc920759910c9f12bfd0b0812c2149d50e3bdf601bd1aff4ea70e78569139362d28b032eb 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. This package implements fast C code that computes the true and false positives with respect to a database of annotated region labels. 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Package: r-cran-peaksegjoint Architecture: arm64 Version: 2024.12.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1001 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-peakerror, r-cran-penaltylearning Suggests: r-cran-testthat, r-cran-ggplot2, r-cran-microbenchmark Filename: pool/dists/resolute/main/r-cran-peaksegjoint_2024.12.4-1.ca2604.1_arm64.deb Size: 849152 MD5sum: ccee9a9de7b389c83bdc33b02b7975f6 SHA1: 2f313341a0bf62542c0209e919e039bb95fa701b SHA256: 9d2979e487c6c99bf28ba80f55768369e1ddc8a09b5dd70d4cc1798e17705432 SHA512: 1312ff264de2a04aa4c7997c4119120c221586c96f9adc66f0a633284bc1bc70349dfabd16b5fc88dddc163e1376fa27ff7beb5d91d303ac990d0f6ad38cdcac Homepage: https://cran.r-project.org/package=PeakSegJoint Description: CRAN Package 'PeakSegJoint' (Joint Peak Detection in Several ChIP-Seq Samples) Jointly segment several ChIP-seq samples to find the peaks which are the same and different across samples. The fast approximate maximum Poisson likelihood algorithm is described in "PeakSegJoint: fast supervised peak detection via joint segmentation of multiple count data samples" by TD Hocking and G Bourque. 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The use of pedigree data is central to genetics research within the animal and plant breeding communities to predict breeding values. The relationship matrix between the individuals can be derived from pedigree structure ('Vazquez et al., 2010') . 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The methods found here include the calibration of linear regression models using covariate selection strategies, computation of summary validation statistics for predictions, generation of summary plots, evaluation of the local quality of a geostatistical model of uncertainty, and so on. Other functions simply extend the functionalities of or facilitate the usage of functions from other packages that are commonly used for the analysis of soil data. Formerly available versions of suggested packages no longer available from CRAN can be obtained from the CRAN archive . 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It fits the multivariate ridge regression model for genomic prediction Xavier and Habier (2022) and Xavier et al. (2025) , providing heritability estimates, genetic correlations, breeding values, and regression coefficient estimates for prediction. This package provides an alternative to the 'bWGR' package by Xavier et al. (2019) by using 'LAPACK' for its algebraic operations. Package: r-cran-pema Architecture: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9194 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-sn, r-cran-shiny, r-cran-ggplot2, r-cran-cli, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-mice, r-cran-testthat, r-cran-webexercises, r-cran-bain, r-cran-metaforest, r-cran-metafor Filename: pool/dists/resolute/main/r-cran-pema_0.1.5-1.ca2604.1_arm64.deb Size: 2016926 MD5sum: fb0c34cd802f6bab178342c8a5ee38b1 SHA1: 1f94bfe9534e215f83f94653569105afd55e79bf SHA256: d5ab1ab16c1e66db367a10f0402a088fea623d11dfbe447f5fb2a51e4303081b SHA512: 44663073fb763a5ef06eacdc53344ff86d22f1dbf5f48ac405af1649c8c18e22080e3416d99bd41981e59e09308cd2fd3a3fc8e5252faa450baf56c321fa25c1 Homepage: https://cran.r-project.org/package=pema Description: CRAN Package 'pema' (Penalized Meta-Analysis) Conduct penalized meta-analysis, see Van Lissa, Van Erp, & Clapper (2023) . In meta-analysis, there are often between-study differences. These can be coded as moderator variables, and controlled for using meta-regression. However, if the number of moderators is large relative to the number of studies, such an analysis may be overfit. Penalized meta-regression is useful in these cases, because it shrinks the regression slopes of irrelevant moderators towards zero. Package: r-cran-pemultinom Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 299 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_arm64.deb Size: 129442 MD5sum: 3ebaaf1394dce9f3e004bb29bde54742 SHA1: a62bb0c1f5eef3673b9240aaca294fe190beab53 SHA256: e79dede659aed2238668c9f9e871c60b515e569bcd1764313f4fb9156f1f2fa0 SHA512: 9d33f37a158a3b67e84ce8f2e2342359304c32c834bcf14c57ce5e89fc42d6ba76455cf9445839cd0f4fe765c05a90ba1909483879fa6dd6be3205a381c4fe00 Homepage: https://cran.r-project.org/package=pemultinom Description: CRAN Package 'pemultinom' (L1-Penalized Multinomial Regression with Statistical Inference) We aim for fitting a multinomial regression model with Lasso penalty and doing statistical inference (calculating confidence intervals of coefficients and p-values for individual variables). It implements 1) the coordinate descent algorithm to fit an l1-penalized multinomial regression model (parameterized with a reference level); 2) the debiasing approach to obtain the inference results, which is described in "Tian, Y., Rusinek, H., Masurkar, A. V., & Feng, Y. (2024). L1‐Penalized Multinomial Regression: Estimation, Inference, and Prediction, With an Application to Risk Factor Identification for Different Dementia Subtypes. Statistics in Medicine, 43(30), 5711-5747." Package: r-cran-penaft Architecture: arm64 Version: 0.3.2-1.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-matrix, r-cran-ggplot2, r-cran-irlba, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-penaft_0.3.2-1.ca2604.1_arm64.deb Size: 169718 MD5sum: d4177554f8d8d6d005590df156d229a5 SHA1: 8bffeba89860d608a9747721666bc3ae1e50a919 SHA256: ee6e15fb91fb1938fc351e85563c838b96315ac80b70fbdf84256806786624c3 SHA512: 8a028ad925bc231f55374925886286ed3b62c701b139cf348348d5d9aeb6850b347458e9bb6c6e83fedbc080dccb22d9e713d3fb4a664559c64f59b954922eac Homepage: https://cran.r-project.org/package=penAFT Description: CRAN Package 'penAFT' (Fit the Semiparametric Accelerated Failure Time Model withElastic Net and Sparse Group Lasso Penalties) The semiparametric accelerated failure time (AFT) model is an attractive alternative to the Cox proportional hazards model. This package provides a suite of functions for fitting one popular rank-based estimator of the semiparametric AFT model, the regularized Gehan estimator. Specifically, we provide functions for cross-validation, prediction, coefficient extraction, and visualizing both trace plots and cross-validation curves. For further details, please see Suder, P. M. and Molstad, A. J., (2022) Scalable algorithms for semiparametric accelerated failure time models in high dimensions, Statistics in Medicine . Package: r-cran-penalized Architecture: arm64 Version: 0.9-53-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1202 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.2), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 810884 MD5sum: 29a1a59978410fbcf585512594ac7e99 SHA1: c50ba7ec3c2b92ade6437518d7e1e21c22e94655 SHA256: 82f493a8e0ff7c3773ff53fe480326b2f2bb94fcda124929db4e8111a15619cb SHA512: b74a907ea007e5156ab87fa52405d389c68067ccc6a8546e8ed29e188cb865653b5e0cef8b8226a0dcf62dda22bed8346ca2164579413c08dbcc43c6168a01c8 Homepage: https://cran.r-project.org/package=penalized Description: CRAN Package 'penalized' (L1 (Lasso and Fused Lasso) and L2 (Ridge) Penalized Estimationin GLMs and in the Cox Model) Fitting possibly high dimensional penalized regression models. The penalty structure can be any combination of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a positivity constraint on the regression coefficients. The supported regression models are linear, logistic and Poisson regression and the Cox Proportional Hazards model. Cross-validation routines allow optimization of the tuning parameters. Package: r-cran-penaltylearning Architecture: arm64 Version: 2024.9.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3019 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2981390 MD5sum: a6608525616e8bf236642bcda63d8903 SHA1: 9c0584edddafd809532a4459fe6472c666b8faf3 SHA256: b723521baf10e16883bb7941c0d389fab5f26091389f5350ba827ed1c5b91afc SHA512: 8c23c6e95e8010656081e427c7ce3e502fec915c0656c4102478ed0469596ed7bd1c04f4f48a3cac42fb863383c8b56da3c3754f6735c5c6f2cda42b3ce5596f Homepage: https://cran.r-project.org/package=penaltyLearning Description: CRAN Package 'penaltyLearning' (Penalty Learning) Implementations of algorithms from Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression, by Hocking, Rigaill, Vert, Bach published in proceedings of ICML2013. Package: r-cran-pencoxfrail Architecture: arm64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 477 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_arm64.deb Size: 323402 MD5sum: 7f19fe210890b86874b3e2fcc74bb916 SHA1: a860dc374bf01eb7ef70319b42084493d76b6c67 SHA256: a2d6485158e008c9e9b6ad6731f6e21e3074b651c89c917d4c7210172942ebee SHA512: b18cf8bc43ab5e9e322ab62e2806ac38550be23327f8993dc1a211b3b38b0b00bb6a02dc1107132f34b99640bf0a645e037393082ffeffbcf0bc1f4dab19938f Homepage: https://cran.r-project.org/package=PenCoxFrail Description: CRAN Package 'PenCoxFrail' (Regularization in Cox Frailty Models) Different regularization approaches for Cox Frailty Models by penalization methods are provided. see Groll et al. (2017) for effects selection. See also Groll and Hohberg (2024) for classical LASSO approach. Package: r-cran-penmsm Architecture: arm64 Version: 0.99-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 155 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 79078 MD5sum: 6d5504e9315b02c658f50fcca01f762e SHA1: 446de524a45cad100b1c3e01d810905dff720a1c SHA256: 82d39ae591bce4fa6626633819c924460001e02539a4b9feb159dba8210dccae SHA512: 52437f2efc3633eefc0268ee1a3de4c62951bfa1e5f883b9669af8c7eb67fc85d480815dd40f2fb2f6f50582b238c5197d594569df0d8de97b8bdf9cfdf55dd2 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: arm64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2150 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1495512 MD5sum: 4033ac0484c0cda5025353789e67908f SHA1: a89f11520c0a4b9cd448e61d043646b8cce0732d SHA256: f64befe741c614d8e347c25c76835f8aa2a157e222a65f70031df722ca3f66b2 SHA512: c2270c5ad669291f9482752c04d665764c75a76d30d87d539acc4efa04a3cb97a3e93f33a56178a33e84405096b4aaaf42ddd5a9b8ffc683a4f0b8f2ec7792a5 Homepage: https://cran.r-project.org/package=penppml Description: CRAN Package 'penppml' (Penalized Poisson Pseudo Maximum Likelihood Regression) A set of tools that enables efficient estimation of penalized Poisson Pseudo Maximum Likelihood regressions, using lasso or ridge penalties, for models that feature one or more sets of high-dimensional fixed effects. The methodology is based on Breinlich, Corradi, Rocha, Ruta, Santos Silva, and Zylkin (2021) and takes advantage of the method of alternating projections of Gaure (2013) for dealing with HDFE, as well as the coordinate descent algorithm of Friedman, Hastie and Tibshirani (2010) for fitting lasso regressions. The package is also able to carry out cross-validation and to implement the plugin lasso of Belloni, Chernozhukov, Hansen and Kozbur (2016) . Package: r-cran-pense Architecture: arm64 Version: 2.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7325 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_arm64.deb Size: 5187600 MD5sum: fab391452925715017f2bb81a8ff4d0c SHA1: 6189cf7c7f351efe23ce1cafc272da3846f15e22 SHA256: 0e4b7389db8013aaba6901ea6529d02ce21d4d6e4fa1af4e5a8f2d85da12c4e4 SHA512: 50f26aca9a90dc6f383d341b971fb97f18564ecee2a9447fa1b7621181a0970cddb9399fcf96750f36367398cd2dc1eff0bac3020abdd72b35405db19ddf7992 Homepage: https://cran.r-project.org/package=pense Description: CRAN Package 'pense' (Penalized Elastic Net S/MM-Estimator of Regression) Robust penalized (adaptive) elastic net S and M estimators for linear regression. The adaptive methods are proposed in Kepplinger, D. (2023) and the non-adaptive methods in Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E. (2019) . The package implements robust hyper-parameter selection with robust information sharing cross-validation according to Kepplinger & Wei (2025) . Package: r-cran-pepa Architecture: arm64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3332 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 3238112 MD5sum: 05cedb728f5333002c30a5dee20a57e3 SHA1: 970e3bb0830cc2d1a1621c4f5df7c270394dad6e SHA256: c2e3896dc964728b1b86780b1d802ec7d3687f21e8125b01b3c804ebc64e2c8e SHA512: c3c2ac2bf82e8d31d2d37916c52586bcaee3b3deab3f57fddf013ec6ae782c6fd940fc353e387dcd6bd330344dc02f1c7e42a5d4f74eb28f5db6bed71f0553f5 Homepage: https://cran.r-project.org/package=pEPA Description: CRAN Package 'pEPA' (Tests of Equal Predictive Accuracy for Panels of Forecasts) Allows to perform the tests of equal predictive accuracy for panels of forecasts. Main references: Qu et al. (2024) and Akgun et al. (2024) . 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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: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 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_arm64.deb Size: 76026 MD5sum: b1ec21d383975ca6c0a5fac15a567996 SHA1: 530651c584b8d3ac335f630544d90bc28c1baa5d SHA256: 833f7c9591ee2d26f55f05998261667088c8c727ef81968e65f57881227c6a73 SHA512: c29086a3c089296e71aaec504957db709567b456b6609da1241ef860b3edeb291cb9e66f741301ff6238f28797197d597b116eeb3b0fb68e3ffa5530a773a8dd 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. 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Package: r-cran-ph2bye Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 152 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_arm64.deb Size: 74616 MD5sum: 33bb8cfbf6cc54bcc4d0384de1810e84 SHA1: 3ce98cbedfbdb396476ec33f0ca3d85fd469b3a3 SHA256: 98098a5790a38b3a1a32880a480f20eaa6845656f39403b213664cfb43cf9f8a SHA512: 34c8393faf4dfbfb01b7708e8203574329498d772067461558fea3cb4608299dc9c0141ec50216506826d7a720d52fecea34ebfea76507bbf5745b31904dc7ed 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. 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"Sensitivity analysis for p-hacking in meta-analyses." .]. Unlike publication bias alone, p-hacking that favors significant, positive results (termed "affirmative") can distort the distribution of affirmative results. To bias-correct results from affirmative studies would require strong assumptions on the exact nature of p-hacking. In contrast, joint p-hacking and publication bias do not distort the distribution of published nonaffirmative results when there is stringent p-hacking (e.g., investigators who hack always eventually obtain an affirmative result) or when there is stringent publication bias (e.g., nonaffirmative results from hacked studies are never published). This means that any published nonaffirmative results are from unhacked studies. Under these assumptions, RTMA involves analyzing only the published nonaffirmative results to essentially impute the full underlying distribution of all results prior to selection due to p-hacking and/or publication bias. The package also provides diagnostic plots described in Mathur (2022). 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Offers methods for tree comparison, model selection and visualization of phylogenetic networks as described in Schliep et al. (2017). 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The functions AssignEffTox() and RandomizeEffTox() assign doses to patient cohorts during phase 12 and Reoptimize() determines the optimal dose to continue with during Phase 3. The functions ReturnMeansAgent() and ReturnMeanControl() gives the true mean survival for the agent doses and control and ReturnOCS() gives the operating characteristics of the design. Package: r-cran-phase12compare Architecture: arm64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 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_arm64.deb Size: 186230 MD5sum: 9bd56dd0e7e07e397401870c7d88e44a SHA1: b91372fd3254c355d87218f4a16efd4dd920860a SHA256: ecb5ef930579436828ebcc3b77efed3bc17f61b6303393860baff9d5eaa4423f SHA512: 7340461f189b2dca4a3939f61a395b7e0bc01325dfccec0b00d852dedef3e772ab0399b025df2645d3b3fcdb75364800fdddde29a3681d516ed5655c2760570b Homepage: https://cran.r-project.org/package=Phase12Compare Description: CRAN Package 'Phase12Compare' (Simulates SPSO and Efftox Phase 12 Trials with CorrelatedOutcomes) Simulating and conducting four phase 12 clinical trials with correlated binary bivariate outcomes described. Uses the 'Efftox' (efficacy and toxicity tradeoff, ) and SPSO (Semi-Parametric Stochastic Ordering) models with Utility and Desirability based objective functions for dose finding. Package: r-cran-phasetype Architecture: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 143 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), 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_arm64.deb Size: 65808 MD5sum: b37d622eee3426ed876f488ebd0d133f SHA1: 24b6062f757491836b949fe9b38bbb1fc44d6861 SHA256: 77fec80e092270d2dd5e0c22559be12ce91fd77707dcce834d07bd79ba3c9013 SHA512: 2a399cd18f696c8b95ea24ef38d7dfc14ecf9d0d156102f3c2ea0d222da7651e57a01226846607462635723c0cb1ee752df5c55dd70c6853080b4cfd7f4a7f2c Homepage: https://cran.r-project.org/package=PhaseType Description: CRAN Package 'PhaseType' (Inference for Phase-Type Distributions) Functions to perform Bayesian inference on absorption time data for Phase-type distributions. The methods of Bladt et al (2003) and Aslett (2012) are provided. Package: r-cran-phenex Architecture: arm64 Version: 1.4-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 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_arm64.deb Size: 152294 MD5sum: b088b15185229cb41ece3c891a3621b3 SHA1: ea246efd8982d3e280e471a6769774b634843b79 SHA256: 036e594180cd0b3b4574aa118bb84688880d4bd350a34eadb2283c8757514dff SHA512: 441c7c4a9eef932ceb5fff90b158976deb05f5c762b5e78433501476fc1261795c6f5fd7bae6c5fabe7dcaebb383c0f151d5a7f8c328c58c1650a38cf4c17efc Homepage: https://cran.r-project.org/package=phenex Description: CRAN Package 'phenex' (Auxiliary Functions for Phenological Data Analysis) Provides some easy-to-use functions for spatial analyses of (plant-) phenological data sets and satellite observations of vegetation. Package: r-cran-phenmod Architecture: arm64 Version: 1.2-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 272442 MD5sum: 6f361434f3ba84805fcf4d7249b3ac3f SHA1: 127529dfa3e21d3f83b6f92d02502d927bcd3079 SHA256: 8ad2db16c9cd5cefeadaa22f18c9131f93785b53fe3b61408761dca9fb0c5c33 SHA512: a54b325d589e8f3ecd1c44540c629c050b3af8a0677e7bf9ecb77d77987b60dc733cdd8469448807cde94cc85f4bf9a555a7da4669cc0471165469c8250300f9 Homepage: https://cran.r-project.org/package=phenmod Description: CRAN Package 'phenmod' (Auxiliary Functions for Phenological Data Processing, Modellingand Result Handling) Provides functions for phenological data preprocessing, modelling and result handling. For more information, please refer to Lange et al. (2016) . Package: r-cran-pheno Architecture: arm64 Version: 1.7-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-nlme, r-cran-sparsem, r-cran-quantreg Filename: pool/dists/resolute/main/r-cran-pheno_1.7-1-1.ca2604.1_arm64.deb Size: 96400 MD5sum: ec1ccc0dfbcc79bcd3ed896daaf738ca SHA1: b27c1e3dc7aaf8e82d135fa70f8a3d0466fa403c SHA256: edf4ef10c2d22559c98f227b32fe7ca7ccde17ecbb2b52c38d9bcc4a7f5d0b6c SHA512: 2cec541213ab0311e878d373ae729c4cfdeb6aa9358698dbeb6c84d88d733ec664b567fbe91ef734281a6674663feb3ff2203275877f81ed57f6dde9eecc48a1 Homepage: https://cran.r-project.org/package=pheno Description: CRAN Package 'pheno' (Auxiliary Functions for Phenological Data Analysis) Provides some easy-to-use functions for time series analyses of (plant-) phenological data sets. These functions mainly deal with the estimation of combined phenological time series and are usually wrappers for functions that are already implemented in other R packages adapted to the special structure of phenological data and the needs of phenologists. Some date conversion functions to handle Julian dates are also provided. Package: r-cran-phenofit Architecture: arm64 Version: 0.3.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1363 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_arm64.deb Size: 935986 MD5sum: 2fd1822c7c7f4efc0cfbce342eedc33c SHA1: 033e66605fc2153f4fe781271417b7ff0c11a2a1 SHA256: fee8f07ef9c9bad7ce176067f4d57d052e5890fd36a40e4cf2f70900c77a1217 SHA512: 4442f4e7347bab5ff4cb9d919e900aba956a74533e92315d1896475c5558f7bde116431d5bb1a3bd752a20b1bd0c38ed193592af9078376c589fc0fa23d7bb3e 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: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 623 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_arm64.deb Size: 433614 MD5sum: 07b00da5bb578572be902cf7e46cdc13 SHA1: 2b58f53cf42b63b9418e4ab47e7e770e972c3dcb SHA256: 1095d719717ab4b1fd42f8302ebfb752bfbd777b662f1a5b01ad6df377e305e0 SHA512: 4fed3fbe25fde498af838c92ff60eab6c227b08924f6bc1d74cdf852894252febfe328eb79f5c29289b9758da4f49140d7ef3a675638f65beb0cf1644e57124d Homepage: https://cran.r-project.org/package=PheVis Description: CRAN Package 'PheVis' (Automatic Phenotyping of Electronic Health Record at VisitResolution) Using Electronic Health Record (EHR) is difficult because most of the time the true characteristic of the patient is not available. Instead we can retrieve the International Classification of Disease code related to the disease of interest or we can count the occurrence of the Unified Medical Language System. None of them is the true phenotype which needs chart review to identify. However chart review is time consuming and costly. 'PheVis' is an algorithm which is phenotyping (i.e identify a characteristic) at the visit level in an unsupervised fashion. It can be used for chronic or acute diseases. An example of how to use 'PheVis' is available in the vignette. Basically there are two functions that are to be used: `train_phevis()` which trains the algorithm and `test_phevis()` which get the predicted probabilities. The detailed method is described in preprint by Ferté et al. (2020) . 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These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions. 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Package: r-cran-phonics Architecture: arm64 Version: 1.3.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: libc6 (>= 2.17), 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-bh Suggests: r-cran-testthat, r-cran-knitr, r-cran-markdown, r-cran-rmarkdown, r-cran-devtools Filename: pool/dists/resolute/main/r-cran-phonics_1.3.10-1.ca2604.1_arm64.deb Size: 129062 MD5sum: 814098e13ecb62f2c9c6ca06494ebcd5 SHA1: 0d41c5ff46290c3d0bf198aa341dbf3062b4e593 SHA256: dd3cb1e026ce733cff82e848c78300a6c6d1294dae8f18f02ec0de282a3fc852 SHA512: 81105dba56318f1bc0167b6c9d8fa7ebea0c7feaaf8a7ad734a18d9ece3e2fa07475e167669ab14aa1d116ea2f9d38db6d2d9acea9e0503e1396fd232e02180b Homepage: https://cran.r-project.org/package=phonics Description: CRAN Package 'phonics' (Phonetic Spelling Algorithms) Provides a collection of phonetic algorithms including Soundex, Metaphone, NYSIIS, Caverphone, and others. The package is documented in . Package: r-cran-phsmm Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 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_arm64.deb Size: 238394 MD5sum: f1b95090b1bf47e46d40407fb3f402c6 SHA1: 0bc6c7b96feccde0c53fbb2bbfc4971ed53f6378 SHA256: 41f605149eae18e6f1903b853c65649873bfc2a06ab9c08dc08e5ee9e4de7f54 SHA512: 1e7be8101610ffa35fe10e91f0bfd84365d903759e56284e1bcfbb045c418299bd9c6f272d765fa3b0e09f5eacab08e4f1a362bff3bb86ea2fb9abbf9164c5fc 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: arm64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 796 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_arm64.deb Size: 472904 MD5sum: 722c9d93be31dd4e3bee96dca91a4756 SHA1: 781234539010a7e25caf37a927c1d4d1ca92a52a SHA256: 6c103f63150a238f99ea1387d45199779dc0c669234232898b3a238bc3525900 SHA512: ef681c6a25a5c4fb52f418a0a6613f7db568df3f0f71a33835f072171feabd4b8860a3dff9740e300879b9bd3785371e999397423f68ed8472048ee85b546843 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: arm64 Version: 0.1-34-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1540 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_arm64.deb Size: 961110 MD5sum: 7c701b90f7bfb1b29173ef3de7e3a6a6 SHA1: 4d0d209175c3f2a16a54af88f3df88be23220abd SHA256: ddfe6771cac7f761be05a1c403a49f3ac0e188e9c0a8dfa4d003746d2feceae9 SHA512: 9cf57d884f68ec6c160d52eacd42b95cacce23fbcd2cac0b95a0ae030b97a4fcf436a4f6e61bc31c43d1d9a16039b1f74a4a41164a3895c623b3cab4b63a4c60 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: arm64 Version: 0.8.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1255 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 668118 MD5sum: f58999eca1729ffb112d7034d083cc9c SHA1: 12d69c43f4e8d44de2082db404b52628facf6765 SHA256: a405910bf889c5b06478e9db41f07da26e51bf7c2f1db40486509323e5fce9b4 SHA512: 5b8479086acd2b9a5c9ab3e1b7472871ec8e64a364a2a070fc5d3450c8087e4388810ff45eaa804e2c19adb9af822c4ee341593608a0c12c66f48c7509a1becf 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: arm64 Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1947 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_arm64.deb Size: 730722 MD5sum: 197bc4d69311532ca06fe853a04cbe30 SHA1: 72af72573f7770a8f864a942e3b5f7997267b885 SHA256: e3ac00558af4e89d4725df9a8364950215bee1b2b1952a0220e9d2999b041973 SHA512: aa1d86fb6e35ff9d22be78dc182727012fe66db77596cfab87d604c034432dae1b39cf0a66d49ee53b457269f8bab9198e7e0539e68fa51b1fb71a371f49e4c1 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: arm64 Version: 1.8.1-1.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), 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_arm64.deb Size: 1262288 MD5sum: 5845994abe66f40093f525628f12e1f6 SHA1: e37018cc1f5057efdb92c463aca2a5ff1b26945e SHA256: 011e4fe772725e2045b09e691662b2700af23702c2341be9e189d61901199ff6 SHA512: 48897c290595772fafbc89056137817a48aeb8515ce5f2c855bad55582b9714c5bf39e6080f162ca9588bc373209fda8a174f46d9a9a3d0d0f9a3871b1cc29c8 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: arm64 Version: 2.6.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 605 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 494988 MD5sum: 40821e79e6b92ad178edaa84017291c9 SHA1: 4118c09508b5495e27f87279d69eda0f4e101fbc SHA256: 947a6efbc2338f6767ffcc0ff082a92ad48519408e07e5e290ed653dd094afb2 SHA512: ec7cf415a6cdc52f88a2ab9b3ec18e343b76d96b35428f2c09e247e53e93c4070ca0d09bdfc6650fd99a3f8128b81200569dea323494f3a9c3dcb715e4f49570 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: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3025 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_arm64.deb Size: 883744 MD5sum: 311d4c3ff0855123dbcda21342b598d7 SHA1: c730607ba79f036fe7956120df5cdeb0980e7ced SHA256: 7bd9c9ad0ecaf244269e738d00e612188af4e52b117b8e018f015d5262504929 SHA512: 6be75000953f84bafdafb456f8039cf109ed0030f3f31cbf9222bfe05e1ba5ed8c1a557906f23863a2751691fcfe61c20b9ea653ce78d31d8fa9a2692e6743e6 Homepage: https://cran.r-project.org/package=phylopairs Description: CRAN Package 'phylopairs' (Comparative Analyses of Lineage-Pair Traits) Facilitates the testing of causal relationships among lineage-pair traits in a phylogenetically informed context. Lineage-pair traits are characters that are defined for pairs of lineages instead of individual taxa. Examples include the strength of reproductive isolation, range overlap, competition coefficient, diet niche similarity, and relative hybrid fitness. Users supply a lineage-pair dataset and a phylogeny. 'phylopairs' calculates a covariance matrix for the pairwise-defined data and provides built-in models to test for relationships among variables while taking this covariance into account. Bayesian sampling is run through built-in 'Stan' programs via the 'rstan' package. The various models and methods that this package makes available are described in Anderson et al. (In Review), Coyne and Orr (1989) , Fitzpatrick (2002) , and Castillo (2007) . Package: r-cran-phylosem Architecture: arm64 Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4075 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_arm64.deb Size: 1099782 MD5sum: 7e8a16a071c031265f130bd1c93fb53d SHA1: e70bd92ae8975f8429bf144c59e0cb76f318c6c5 SHA256: 76bb3b80bbebfca160168f5e1337b528a9619d2a8a8891b69317d71eab41b7e3 SHA512: 5c8fbdd6515c70ba29a285591bb7bbcbea6472f2b0aa296ab69f0e9ea12d6aade5fd4920c11cfb511014d8b776b1f77b8b5d310587faf9e7253d353507a328fa Homepage: https://cran.r-project.org/package=phylosem Description: CRAN Package 'phylosem' (Phylogenetic Structural Equation Model) Applies phylogenetic comparative methods (PCM) and phylogenetic trait imputation using structural equation models (SEM), extending methods from Thorson et al. (2023) . This implementation includes a minimal set of features, to allow users to easily read all of the documentation and source code. PCM using SEM includes phylogenetic linear models and structural equation models as nested submodels, but also allows imputation of missing values. Features and comparison with other packages are described in Thorson and van der Bijl (2023) . Package: r-cran-phylosignal Architecture: arm64 Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2972 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_arm64.deb Size: 1210270 MD5sum: f838aa5c939573bd23aec340cf253f36 SHA1: 60f07394626064198ab1c6b43f42f4039d7b11cb SHA256: 4190600041704f82968327fb010ee1a714db15abc128a95151562b2fa310ecb2 SHA512: 8b8b6a456cdae61b0005861520d352e1ac0b89b08209b57c369aaa8753ec075be78e4ba18e056d3f15191cce3442123d2e59cc34317e2980587f12e96a521ae9 Homepage: https://cran.r-project.org/package=phylosignal Description: CRAN Package 'phylosignal' (Exploring the Phylogenetic Signal in Continuous Traits) A collection of tools to explore the phylogenetic signal in univariate and multivariate data. The package provides functions to plot traits data against a phylogenetic tree, different measures and tests for the phylogenetic signal, methods to describe where the signal is located and a phylogenetic clustering method. Package: r-cran-phylotypr Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4722 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_arm64.deb Size: 1973238 MD5sum: bbb982cd72ba3e5c07b94075cd265e3b SHA1: dfbe34382ae94c1dbb7e8c86d009b0016c586802 SHA256: c0674e8d9b98cec4d9a27d849bdf8297be816d29a497f77bf67cc345204b6f5f SHA512: 8d783601dc398b0c79a3472b72a26694ba4d096b7106da43793084340366a01f7f86bb439447017f2c1b96ac89ae10376f218d1e428f16dfe5c5e0893a82dd00 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: arm64 Version: 0.9.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3729 Depends: libc6 (>= 2.17), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-ggplot2, r-cran-reshape2, r-cran-rfast, r-cran-rspectra, r-cran-rcppeigen, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-phylter_0.9.12-1.ca2604.1_arm64.deb Size: 2851316 MD5sum: e543eed19bb4a7ea85844a27b518ad06 SHA1: d00f9bb4004ff570c12889b304d89a5e359aa139 SHA256: 74e1694fb107eeb9a92a361d128328d6aa6b1a35956a61fb5644b32162ce65c2 SHA512: bbbe5863588e3b945ffa413cec92726ad212cc744bd270830728855f4a91a943a1177affa7354fbf82708bb49f299dcca1f5841e44fba098e34d6d7127d6cd80 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: arm64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3348 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1797926 MD5sum: b1215449e3478501187a73b2ae268fd4 SHA1: 8412bb3be680394b40d8a03fa316165bdf941a51 SHA256: 02639b7e74114ec594210f791f5d7c9945d2ace43d94656cf0b59ba33cf4687f SHA512: d39e178e75ab950f734f934aab9800844f0cc9e3a566f6690b8a180c77620fc892e2161880e9b7afeb34a5b2f8ca8be307fe0445d0852cfdfb3cbf3cff4e7467 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: arm64 Version: 1.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 586 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_arm64.deb Size: 463700 MD5sum: 1be2558c968fe36d04024e73799261d6 SHA1: 5edbf4d2db0c7745d7e87c5d3fd417fb4e44b0e6 SHA256: f3195f81c0e0df215f97049418171c169e693b51371c49f8724061ee6e36b561 SHA512: 41e1bd5905b1b4962ad57a2f025ae39d99067e7f2add68261799960d1f882b30dd8ad431a138f291692ead2bdef8290bcc837c2b0f25f059f2df4c9d585b4875 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: arm64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6084 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_arm64.deb Size: 3164700 MD5sum: 944832baef35452becedec8209a0dd92 SHA1: f477810f55a7893eac70935820961d62923a7255 SHA256: 7d0485f2ad1350af266cbc66d3c06dd3b9f09d9c087ffaca3c1f8b9877528068 SHA512: a2bc16b85010216c82ce065942ea2957a84270a5f1f5ce51e0c6126b21111124d23d47e89fd2c9f290638848c023bc794e05116fc461d9872d474159c431816b 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: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4707 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_arm64.deb Size: 4134878 MD5sum: 7d3a8c415e680e14b7157e9c39227b1e SHA1: 2eec19558782bbf8ec2664bb75d17017810a80e0 SHA256: 0f9c7d74a6906563c671bf0b6889a676ac9f9654b65d5b3abb4ceeace4e52524 SHA512: 30815de7fe19f63d1001ba6219b1bdbd4b7ba1b810b7d1070143b2ddd94a21839abf1cbde9f6e3aa0c6798aef442e6a525c0be1280ea51a8710dd85ba083cd13 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. To capture this range, data in HDR images is often stored as floating point numbers and in formats that capture more data and channels than standard image types. This package supports reading and writing two types of HDR images; PFM (Portable Float Map) and OpenEXR images. HDR images can be converted to lower dynamic ranges (for viewing) using tone-mapping. A number of tone-mapping algorithms are included which are based on Reinhard (2002) "Photographic tone reproduction for digital images" . Package: r-cran-pieceexpintensity Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 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_arm64.deb Size: 52430 MD5sum: e8b0f0f677736ebe3a99f66afd47d690 SHA1: 5cc9c7e4c66e9f5bb67e06a15fe1a6e413c65524 SHA256: d6188e54810ce9f87fcf147980f483a90a60e90af5aa3e22c809030ce99de8ee SHA512: 5c570433ee23a22785a70633cd2837b1070b9eaf42e6d4fc5d3c857774577e7488a1a54a3766ae769c1122a4f3dc2e8fa7077d14b1afa78d7f663722cb11062b 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: arm64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1702 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1129622 MD5sum: 4fe85d49b098cdcc8bf00db3d4633038 SHA1: 5a416eb69db3ebbc87c96d2feeec104cf78a2e51 SHA256: f26a0b6ab67e0fb5e681ee3add93cf01328cf8ac2f39e531eb3334406953bed1 SHA512: 5ccf0d45d5619f611a987a1594a0ad4d5bc33339021e7a891722634acd0dcb577d6859d2ba77d724519ee81be7ad2acc16199c28e1b9fa1bdf8b8c1449b6bd71 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: arm64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 73160 MD5sum: 66c19328b11beec4d531bee7b1729dd6 SHA1: e66623e467002c174ff9423ac609bc0f4bcd3267 SHA256: 116a7ffa80ec473c0e8cf674a992d276da5d3296e125fcd6b84dcf79183d7d58 SHA512: 3b79dbeef85214b74092078501d01ad2a4da03be6ed64ab61fd8b383af8df296432d377af80ca2fabc14477b862338c98f82fdf7d2cb03051f812c715b1db52a 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: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3077 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_arm64.deb Size: 1159256 MD5sum: 2d451a77f552a844d4da314573464c97 SHA1: 889eaf367497eff79a6d1e8c3bb19064ee0d1c96 SHA256: 18733fcaf5818ae494e4c221c877e6d06b671eea0fd9c28985b7cf04a8843a3a SHA512: e3e75b817a04dec30314025c78040f91281f15576668ead47d8ac7243b156250a0c9d680bc1529dab98f17af305fdfd1886ff8f448ac1a605dd95931618f8e0b Homepage: https://cran.r-project.org/package=pikchr Description: CRAN Package 'pikchr' (R Wrapper for 'pikchr' (PIC) Diagram Language) An 'R' interface to 'pikchr' (, pronounced "picture"), a 'PIC'-like markup language for creating diagrams within technical documentation. Originally developed by Brian Kernighan, 'PIC' has been adapted into 'pikchr' by D. Richard Hipp, the creator of 'SQLite'. 'pikchr' is designed to be embedded in fenced code blocks of Markdown or other documentation markup languages, making it ideal for generating diagrams in text-based formats. This package allows R users to seamlessly integrate the descriptive syntax of 'pikchr' for diagram creation directly within the 'R' environment. Package: r-cran-pimeta Architecture: arm64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 424 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_arm64.deb Size: 233024 MD5sum: a63a7fd120ab9a03c775404b86bbc394 SHA1: 120132276e1853f5b4b174deaeffbd2b0279645f SHA256: 91425cec3c7792eadada61d796693769e26a1843a93b7cbc4d16cb015546b4ac SHA512: e03c1ca8ea11be07fd487bd656ee55d1c53676d7df597995b92599316f32029beddc9749d0ec4ba47f249b21dc347764bb89caaf9a057d68e74c4dd6ee184a11 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: arm64 Version: 2.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 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_arm64.deb Size: 42036 MD5sum: e4aa47812ffb6685a98399c6b1f78540 SHA1: b565a4ad535c02ccbbe4be7ae7795ae09da45c94 SHA256: a7f21d92990d2ab69f91036dd8cad658084d38f8631d033061fabf209b46e79c SHA512: 512735d73e660879b9698d357b59817e758577146830409889a80f0b366a57ee0f76b21473b7364948803ea81eaffc4cace0f7b0774b59c2069d21bac3b0fbb1 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: arm64 Version: 2.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1001 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_arm64.deb Size: 815002 MD5sum: 337f05f2ba65accbd2bc9ea2a8fa6f46 SHA1: 52f271209fc3aaf23df9ca6469a906c3f4761286 SHA256: 08b68c27caba3e7bc716631cb17a6755a08a685874c9860213a7204b7852d3b4 SHA512: e8d32347d706d324780a52e2c6c67b8a80389a3147c0f6ebd7d9be99781274a37d0cf6345351d1518759b4bcb35d17dde54bca78dabdba07cf51d3bc6b704142 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. PINSPlus is fast and supports the analysis of large datasets with hundreds of thousands of samples and features. The software automatically determines the optimal number of clusters and then partitions the samples in a way such that the results are robust against noise and data perturbation (Nguyen et al. (2019) , Nguyen et al. (2017), Nguyen et al. (2021)). Package: r-cran-pintervals Architecture: arm64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2038 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 759088 MD5sum: a81ce77cbf31034f659b21d48b4facfc SHA1: 7d97e161639054cb7935fe8709c83f092a5f22b9 SHA256: e4f79c61723863c55d00ecb9fb016e1554c11b05e0beff101b7889b8e297f25e SHA512: 9efddf03bdcc4d94bfd3893f3f5956672c1388a42e5318d5baa4d2faa66ff0a896f50d5c39efd05326e2f264a857a39850e1461a5e5f86820678b9d14eb246e7 Homepage: https://cran.r-project.org/package=pintervals Description: CRAN Package 'pintervals' (Model Agnostic Prediction Intervals) Provides tools for estimating model-agnostic prediction intervals using conformal prediction, bootstrapping, and parametric prediction intervals. The package is designed for ease of use, offering intuitive functions for both binned and full conformal prediction methods, as well as parametric interval estimation with diagnostic checks. Currently only working for continuous predictions. For details on the conformal and bin-conditional conformal prediction methods, see Randahl, Williams, and Hegre (2026) . Package: r-cran-piqp Architecture: arm64 Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 277550 MD5sum: a8a119de4885a5692ee883f50d78be20 SHA1: 556d0a2daa0bff735e3039d734b4a0a514b0108e SHA256: 49f56ffd015821942278b24bca930bcb65f5a867dc2670bd65b406993ae29fbe SHA512: 7c63408946b5103ceff03c6a3455b81f6f6f9578c28c69031726771ff4a220060f792a75aee1e4929e0d9363f36515f8f23c91c84c6207bf94f4722400f1da0c Homepage: https://cran.r-project.org/package=piqp Description: CRAN Package 'piqp' (R Interface to Proximal Interior Point Quadratic ProgrammingSolver) An embedded proximal interior point quadratic programming solver, which can solve dense and sparse quadratic programs, described in Schwan, Jiang, Kuhn, and Jones (2023) . Combining an infeasible interior point method with the proximal method of multipliers, the algorithm can handle ill-conditioned convex quadratic programming problems without the need for linear independence of the constraints. The solver is written in header only 'C++ 14' leveraging the 'Eigen' library for vectorized linear algebra. For small dense problems, vectorized instructions and cache locality can be exploited more efficiently. Allocation free problem updates and re-solves are also provided. Package: r-cran-piton Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 694 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 82246 MD5sum: b8c97f07e2e52f3923a729c7f77d9b3f SHA1: a3b4fba5a00a995ade3d17c6dbcf89ee1ba2694a SHA256: feeacc219c73add46070d4b37ce8a54b44df74a0d64911b89943e9fab4c7543a SHA512: cae41b34825f0edfee6c1ae89242b62d415c4b3c1df9db0b7ae214856e7fb38851b1c15c4a0c5644f2c20d0831be8468b8624a0d1dcd82d0a1e1cd266434cf53 Homepage: https://cran.r-project.org/package=piton Description: CRAN Package 'piton' (Parsing Expression Grammars in Rcpp) A wrapper around the 'Parsing Expression Grammar Template Library', a C++11 library for generating Parsing Expression Grammars, that makes it accessible within Rcpp. With this, developers can implement their own grammars and easily expose them in R packages. Package: r-cran-pjfm Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1246 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_arm64.deb Size: 764028 MD5sum: a0a6905d8736deb5b92cbea264f32c03 SHA1: 4ddf1b4182cea4bbfab1347e879071560a4915ed SHA256: d5a8a60722a4835c179886d9e06b06a5db1759f52e9d25d2c02aa571507ed8d5 SHA512: 8f7c02043d420adaaf350f3b04d2db0064875e2040442167fe22f053e51784501d2d934208f6857d0bc1c64899d3f8db1ac4908158929154ae77dea26adff1e5 Homepage: https://cran.r-project.org/package=PJFM Description: CRAN Package 'PJFM' (Variational Inference for High-Dimensional Joint Frailty Model) Joint frailty models have been widely used to study the associations between recurrent events and a survival outcome. However, existing joint frailty models only consider one or a few recurrent events and cannot deal with high-dimensional recurrent events. This package can be used to fit our recently developed penalized joint frailty model that can handle high-dimensional recurrent events. Specifically, an adaptive lasso penalty is imposed on the parameters for the effects of the recurrent events on the survival outcome, which allows for variable selection. Also, our algorithm is computationally efficient, which is based on the Gaussian variational approximation method. 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Package: r-cran-pkgdepends Architecture: arm64 Version: 0.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3741 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_arm64.deb Size: 1843092 MD5sum: c78d20c0cba3e0655e580ade8bd69cc8 SHA1: 980bf36103ba7ad45edeb68c3faae432fae998c7 SHA256: 6a76fec023f3c3e74a35c0a4e02c096f288f12f738b26bcb5c59ce8c246856dd SHA512: 5429af3165e4116bcea4793c6c325ca5d1e946b1f934c3844a14aecaaabee03a692d804d942f2062d9bae12fb2dc5a9bdda69d0cd34e0b54130b567e8ddca245 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: arm64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1722 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 579588 MD5sum: 27edd9f7483f12f363fadeb099bb50a8 SHA1: 9d2106153fefd8d117d9b0fa74a06b31cd1fef1c SHA256: 69ed1ca0605830166f81dddc05c7a66c318cc369a609f902fbacdaab572c986c SHA512: 41d83e53f5a8565bcb4dbac75846343303906d764c4be8a8584d6a025746585aa7d457aa81fc4b239ac4f754cf591d5d684684ad51694eb377d2b4a97a5a72d6 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: arm64 Version: 0.1-15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 115662 MD5sum: e645d0d746621b7e02e976b767bd8a09 SHA1: 5c2373fb34c7db2e71ad3726bf95556222149626 SHA256: 32263b3c68ef63668e399d19c3829819da11d409c7efe03078ec87eecb9af0cd SHA512: 2088a9e071d41acb1b5a0c5f60f1e8c6c8876d49e60066ad9f39eddfb463cf86c4739e2bfb348be79428b21050b5e3402943cfd1ecd0f5cdd652a57e746f8a5e 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: arm64 Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1050 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_arm64.deb Size: 583010 MD5sum: 697cfac609efd1a617e4f9a963a5a466 SHA1: a29c0fd85c7d4d4954b1cce7d84e7b78c26e095a SHA256: 57cff744c85647ce9d6dc7f98c015620bfc52c1ff4dde9a6ef0f3da15b3db5cd SHA512: 7ab18a1bb2d878ceace462170500249ddc7d42c72cc981f599e0ca7b9473e31e7fec6c2227c94b8704808c1382c203def283edc7a0c071b027e71619067da01c 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: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 410 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_arm64.deb Size: 161558 MD5sum: 2a1414b85c37e97ab24298027b07b8a6 SHA1: 92cb91ab2164b0ebc58f13555bf71f72033a327a SHA256: b51af391c66de12d40fb90ae439fe0012f631294327751ac8230cc310ec3e872 SHA512: 32c0fbd2a4fb5055e78a9286167e2b71fb90941e8e59a98df434ae3196f8ee7ee556c3c06ec1edcadef7f85b2d5137fefe87e1e0041497dbb3fb6fa8cd6e922c 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: arm64 Version: 1.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 984 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-playerratings_1.1-0-1.ca2604.1_arm64.deb Size: 791750 MD5sum: 4e74d8b2e014bc81fa2f643c8ba82894 SHA1: dc780dcc16ff42b22c74465bffbd4a7dc30ac617 SHA256: 9e85d5a2e30e3ed84573fadd8a5953a45821185ad6e69d6a31fdf1546edfe373 SHA512: 1c2593add9b874f43519c37e2dd08df11e022102178c3356e30feb105db732b5692df8a46748e0e65d9f48a80ef85dc983b1c36209bf16ea04abcfd7d6a1a8f3 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: arm64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 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_arm64.deb Size: 87966 MD5sum: 9cab7807b58adb22bcb3995924ac8752 SHA1: ed1cdf35f87810f18831066a53c9c8831364327c SHA256: 5d5ccf659ff2b984c58ee04f93eb5472d37f232334cd14fd141752e7bb7d1466 SHA512: de47820d909f1e7ac556b3fce37dcd083fa81d67d8c711bc8c1ed5f34579e7aaa132c9eefd5843e3630a1f59ecb613cf3d95aa7a2ba26707b9698403551f3c80 Homepage: https://cran.r-project.org/package=PLFD Description: CRAN Package 'PLFD' (Portmanteau Local Feature Discrimination for Matrix-Variate Data) The portmanteau local feature discriminant approach first identifies the local discriminant features and their differential structures, then constructs the discriminant rule by pooling the identified local features together. This method is applicable to high-dimensional matrix-variate data. See the paper by Xu, Luo and Chen (2023, ). Package: r-cran-plfm Architecture: arm64 Version: 2.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 582 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sfsmisc, r-cran-abind Filename: pool/dists/resolute/main/r-cran-plfm_2.2.6-1.ca2604.1_arm64.deb Size: 400578 MD5sum: 11c467a65f4be6de26224a38fd19575f SHA1: e03cb2e4e817f471ee212eedf88eacda392038cc SHA256: 647b9beffbfe5708b7865be0c00b7ae3ccfe1dc65fe2ebe861396ab5af9b5f00 SHA512: f79e8644e00defdf611faa2c97e91ff1c4921c41fe668294312ad8b5553b717563e86f44b01a3396bf2fa6528a1052aa7f185c572ff62f2931697f1b12472bbd Homepage: https://cran.r-project.org/package=plfm Description: CRAN Package 'plfm' (Probabilistic Latent Feature Analysis) Functions for estimating probabilistic latent feature models with a disjunctive, conjunctive or additive mapping rule on (aggregated) binary three-way data. Package: r-cran-plgp Architecture: arm64 Version: 1.1-13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-tgp Suggests: r-cran-ellipse, r-cran-splancs, r-cran-interp, r-cran-mass Filename: pool/dists/resolute/main/r-cran-plgp_1.1-13-1.ca2604.1_arm64.deb Size: 212288 MD5sum: 41271c25ee185ac8ab7e0d015a4102f3 SHA1: a063d234ea9f72d572c2589b77bb8c84785d8982 SHA256: 2d16eaf157288c6e59e974997e550980ab339ef670f6e4a8cb92d9df60c42996 SHA512: 7cac79b1bb21e810ba994e5c499b3fcc400e67bcd6015f2042dc55ae02946aec71aa7037a9b3f8eed9c307caca55d92a4b3e07d0cc6c50d0088ff50e370e0d11 Homepage: https://cran.r-project.org/package=plgp Description: CRAN Package 'plgp' (Particle Learning of Gaussian Processes) Sequential Monte Carlo (SMC) inference for fully Bayesian Gaussian process (GP) regression and classification models by particle learning (PL) following Gramacy & Polson (2011) . The sequential nature of inference and the active learning (AL) hooks provided facilitate thrifty sequential design (by entropy) and optimization (by improvement) for classification and regression models, respectively. This package essentially provides a generic PL interface, and functions (arguments to the interface) which implement the GP models and AL heuristics. Functions for a special, linked, regression/classification GP model and an integrated expected conditional improvement (IECI) statistic provide for optimization in the presence of unknown constraints. Separable and isotropic Gaussian, and single-index correlation functions are supported. See the examples section of ?plgp and demo(package="plgp") for an index of demos. Package: r-cran-pliman Architecture: arm64 Version: 3.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3850 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_arm64.deb Size: 3455288 MD5sum: 90315c0331fe30ffdafe485c4ecc65b5 SHA1: 3b02f88c208aa6e29748ca041b314214a97a384d SHA256: 06093e9c2a79f8218e6a9cff8af4ff8a223490c7f14550f7895d6e772bdf4588 SHA512: 3f2a3e936c3e38029d80b5c440bf982e5295dce3aa72b26f4d93524e80aa03fe6da117eacf6d71320aeba7d349994c1bc36aad343890088ebb243739bddcf326 Homepage: https://cran.r-project.org/package=pliman Description: CRAN Package 'pliman' (Tools for Plant Image Analysis) Tools for both single and batch image manipulation and analysis (Olivoto, 2022 ) and phytopathometry (Olivoto et al., 2022 ). The tools can be used for the quantification of leaf area, object counting, extraction of image indexes, shape measurement, object landmark identification, and Elliptical Fourier Analysis of object outlines (Claude (2008) ). The package also provides a comprehensive pipeline for generating shapefiles with complex layouts and supports high-throughput phenotyping of RGB, multispectral, and hyperspectral orthomosaics. This functionality facilitates field phenotyping using UAV- or satellite-based imagery. Package: r-cran-plmix Architecture: arm64 Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 786 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_arm64.deb Size: 504788 MD5sum: 9eab69f22dcffd9deb68fa3815006fb1 SHA1: 80f145dc8899e02112a2f0c1b4d4cd249ed5c0bd SHA256: ee928c6774a791cacd09bdc2edbc633504b13c98958e650fb97df010290a3d6d SHA512: 9facd37ca504a121bc4560f57d5895779851c520f8f2b29161262f3d079698b27fe834d2df404111e34b148dfce256c6a9b7aacc7cea26b8e3def29d39d726fe Homepage: https://cran.r-project.org/package=PLMIX Description: CRAN Package 'PLMIX' (Bayesian Analysis of Finite Mixture of Plackett-Luce Models) Fit finite mixtures of Plackett-Luce models for partial top rankings/orderings within the Bayesian framework. It provides MAP point estimates via EM algorithm and posterior MCMC simulations via Gibbs Sampling. It also fits MLE as a special case of the noninformative Bayesian analysis with vague priors. In addition to inferential techniques, the package assists other fundamental phases of a model-based analysis for partial rankings/orderings, by including functions for data manipulation, simulation, descriptive summary, model selection and goodness-of-fit evaluation. Main references on the methods are Mollica and Tardella (2017) and Mollica and Tardella (2014) . Package: r-cran-plmmr Architecture: arm64 Version: 4.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3955 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_arm64.deb Size: 2661306 MD5sum: 3e9af995d3c90e15e30c2fea02fb5062 SHA1: d02f5ca9e0816ec52311a2e4d3a525c667371698 SHA256: e3ade22772b538da6a4bae7827eb13c48be3247f37dc6fd46b69c0d6646b7ac6 SHA512: 7917be7da79ec718d2f226817cb5db08e5a6db7f3de9f4acfb2ee57da528d56a90d53bd06320eb19ea4381f302fe4b80c213e51df2758e15fd8dcd608c3c5887 Homepage: https://cran.r-project.org/package=plmmr Description: CRAN Package 'plmmr' (Penalized Linear Mixed Models for Correlated Data) Fits penalized linear mixed models that correct for unobserved confounding factors. 'plmmr' infers and corrects for the presence of unobserved confounding effects such as population stratification and environmental heterogeneity. It then fits a linear model via penalized maximum likelihood. Originally designed for the multivariate analysis of single nucleotide polymorphisms (SNPs) measured in a genome-wide association study (GWAS), 'plmmr' eliminates the need for subpopulation-specific analyses and post-analysis p-value adjustments. Functions for the appropriate processing of 'PLINK' files are also supplied. For examples, see the package homepage. . Package: r-cran-pln Architecture: arm64 Version: 0.2-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pln_0.2-3-1.ca2604.1_arm64.deb Size: 85044 MD5sum: 2f86733d9a6ec4d0e0a3e1f0bb14f5ec SHA1: 65a132828a9ce4386b3f9312efc98f17d5b94937 SHA256: c736323338619d5201cf9cc9b2e097d95040eab5512f5beaba2111f1428baae3 SHA512: 016ba9024b4852e9f3d9fb5024d1a143dc35b83f71a687fa94f8cfc1d6e46162202a8089c887e2d69cf066dfe2c87b5c64ea6167108be238b7bd297ee0a28b0f 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: arm64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6232 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_arm64.deb Size: 4838758 MD5sum: 1e1bd95fad630fccf6df896150bbe5b8 SHA1: 19f4d030c42faed2c6117aa2cea18286648376bb SHA256: e58afae3ce56142f5a80acc46a80505129a7cb3629843238f90943afc1462cae SHA512: 9a887c992feed679025b9579eaf63a82ac76ae0537446596ddb89a8cb5c86e1c3a5f14e03dc7d97486fccdaf4159fbbff9af14b5318a650a7c24fef82e9183da 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. Implements variational algorithms to fit such models accompanied with a set of functions for visualization and diagnostic. Package: r-cran-plordprob Architecture: arm64 Version: 1.1-1.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-mnormt Filename: pool/dists/resolute/main/r-cran-plordprob_1.1-1.ca2604.1_arm64.deb Size: 49156 MD5sum: e9c0a050533c468498135e8072e74634 SHA1: 71fdec0b61dad11ee62f8e4cfacabd2e4cae1327 SHA256: 756103a5d27c49673ce037be0974fb244c6f79957927c2470573fde93b817c9b SHA512: c39920f79dede423b357b78a2b97d3cb0db47a49b8a50ad9ccfd99a940484009cd1e07bd3b22a95a303f259b7fdd1ed32595530916fe6b3d01ee0e17ac286f08 Homepage: https://cran.r-project.org/package=PLordprob Description: CRAN Package 'PLordprob' (Multivariate Ordered Probit Model via Pairwise Likelihood) Multivariate ordered probit model, i.e. the extension of the scalar ordered probit model where the observed variables have dimension greater than one. Estimation of the parameters is done via maximization of the pairwise likelihood, a special case of the composite likelihood obtained as product of bivariate marginal distributions. The package uses the Fortran 77 subroutine SADMVN by Alan Genz, with minor adaptations made by Adelchi Azzalini in his "mvnormt" package for evaluating the two-dimensional Gaussian integrals involved in the pairwise log-likelihood. Optimization of the latter objective function is performed via quasi-Newton box-constrained optimization algorithm, as implemented in nlminb. Package: r-cran-plotcli Architecture: arm64 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_arm64.deb Size: 477506 MD5sum: 27d3397964dcd9f5b97c8dd421139508 SHA1: 15b16d861ce909756b218eca7415a096e0089fe4 SHA256: 252147b08129abb8dc60c28ff530a4ea218be64de89b0a060ff801b4dbbaef99 SHA512: 7f8e5cf965d3fb906a9714ee4e439a6fc3e6161bbe2eea00a0bbfa9d29ac02469c7575c8d72a743a7f5d773b20048bf8ae0b577c849ece3eb8d1b3b24aa2ce9d 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. It supports colored scatter plots, line plots, bar plots, boxplots, histograms, density plots, and more. The 'ggplotcli()' function is a universal converter that renders any 'ggplot2' plot in the terminal using Unicode Braille characters or ASCII. Features include support for 15+ geom types, faceting (facet_wrap/facet_grid), automatic theme detection, legends, optimized color mapping, and multiple canvas types. Package: r-cran-plothmm Architecture: arm64 Version: 2023.8.28-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 455 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, r-cran-knitr, r-cran-markdown, r-cran-r.utils, r-cran-covr, r-cran-depmixs4, r-cran-data.table, r-cran-ggplot2, r-cran-neuroblastoma, r-cran-microbenchmark Filename: pool/dists/resolute/main/r-cran-plothmm_2023.8.28-1.ca2604.1_arm64.deb Size: 249778 MD5sum: 546808688591a91410d7d28417d89aeb SHA1: 9cf63caf0d103b668cc887eab58c25651011e3fd SHA256: 410ac0e5b926e832176e7047b74f7bed82ba46f4923805a811233b0d40e53371 SHA512: 13b4c3690e3b13c7ac5d3bdcd1589d42a97c089fabb71bf7eb500057ecf54c58d83f34b0b98be8a118cb0ddad7a4c7814de4bb2e2f51d171dc661bf28ea7c98e Homepage: https://cran.r-project.org/package=plotHMM Description: CRAN Package 'plotHMM' (Plot Hidden Markov Models) Hidden Markov Models are useful for modeling sequential data. This package provides several functions implemented in C++ for explaining the algorithms used for Hidden Markov Models (forward, backward, decoding, learning). Package: r-cran-plotsemm Architecture: arm64 Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-plyr, r-cran-shiny, r-cran-mplusautomation, r-cran-rcpp, r-cran-plotrix Filename: pool/dists/resolute/main/r-cran-plotsemm_2.4-1.ca2604.1_arm64.deb Size: 170222 MD5sum: 026e9f7ad0cf26ed41ff4a91cff9a3a2 SHA1: 290fd2b0fb603c230382e889a402c14ee56f38a3 SHA256: 562e2bea75b8ebf4ef23c0f8884353fc68600637c9d46575321a471b81c8e17c SHA512: e8afc168e8a9c0c82a91fc41df380d71e01e1d6a21216136817cf43e027ac1ed71a078e44ed4388b9d0d55031bddf91907d10464a2077650e32b698ef4c0302c Homepage: https://cran.r-project.org/package=plotSEMM Description: CRAN Package 'plotSEMM' (Graphing Nonlinear Relations Among Latent Variables fromStructural Equation Mixture Models) Contains a graphical user interface to generate the diagnostic plots proposed by Bauer (2005; ), Pek & Chalmers (2015; ), and Pek, Chalmers, R. Kok, & Losardo (2015; ) to investigate nonlinear bivariate relationships in latent regression models using structural equation mixture models (SEMMs). Package: r-cran-plpoisson Architecture: arm64 Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 371 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-plpoisson_0.3.1-1.ca2604.1_arm64.deb Size: 58602 MD5sum: d3b21c1ce7b27cc49b301dd664eeb9f7 SHA1: 74124fa9d3ae809b8f8d826e9eb953e4f202a3e4 SHA256: b01329540a7fc1eb01ce918a56354960c7011c3df9d36c25036a21e5748e3b20 SHA512: 8b1401743b3354d1248df7887981556cc6237190e33e56de800ab52bfe2dc4dd5a13441a4080b77d61773aff725206170f3bf941fc5a3aba25d8af61b2248b64 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. Limiting results are provided in a Bayesian setting with uniform, Jeffreys and gamma as prior distributions. More details on the methodology are discussed in Bejleri and Nandram (2018) and Bejleri, Sartore and Nandram (2021) . 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It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available. Package: r-cran-plugdensity Architecture: arm64 Version: 0.8-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 113 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-plugdensity_0.8-5-1.ca2604.1_arm64.deb Size: 19276 MD5sum: 7128715569ae63811bfe4ce41043a162 SHA1: ed614cb236fd39c750b7f1347ec78882a04eaeba SHA256: ffb10bd7c68cadb64c9307dbdf4cbe7b76316a7d24b07ac17fae4f87f9682a91 SHA512: abf7bbf1a1c50c65f8fb4cd152598bea5afaadf6747da5aadd05cc0cd7a41bd37d4ee9189b7021dc1e987d6d5fb0820fd1e05366cc3137d654c53fbc8788a809 Homepage: https://cran.r-project.org/package=plugdensity Description: CRAN Package 'plugdensity' (Plug-in Kernel Density Estimation) Kernel density estimation with global bandwidth selection via "plug-in". Package: r-cran-ply Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 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-ply_0.1.0-1.ca2604.1_arm64.deb Size: 165838 MD5sum: f92e8085e0146f1cb38d2c36fa51f9c5 SHA1: 4bce6cdf1bdf185d6dbcc1f52302c66776822ac3 SHA256: 85c5e653d806d89927dcbe2ea4558c8747c7d6656462fa7bbb714ab869be3b5a SHA512: 6940a41d30ea05f0431de5a11f6782cf52519a9d420a8a7d43bbcaf2052b50f68f5219a49698faebbfbb595250517e94de537a96c12eecc782534579048d7e58 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-plyr Architecture: arm64 Version: 1.8.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 877 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-abind, r-cran-covr, r-cran-doparallel, r-cran-foreach, r-cran-iterators, r-cran-itertools, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-plyr_1.8.9-1.ca2604.1_arm64.deb Size: 771204 MD5sum: 12ba5402c2e93a2d4a37fd9e8522501a SHA1: 40e4a8bcb76941af61bb833b39786216856a22ab SHA256: 867f3b46770dfaab070df3857b32d1aa6979c072e60eea3393675f4478695a8e SHA512: 2a446ff5dd10afa7a61894ebbbd851f8541c19e77c927621d8e76d959c4454362ec9417e879e9748088ae8349709f0635ed96d7d05915bd38ae34c818d67aa6d Homepage: https://cran.r-project.org/package=plyr Description: CRAN Package 'plyr' (Tools for Splitting, Applying and Combining Data) A set of tools that solves a common set of problems: you need to break a big problem down into manageable pieces, operate on each piece and then put all the pieces back together. For example, you might want to fit a model to each spatial location or time point in your study, summarise data by panels or collapse high-dimensional arrays to simpler summary statistics. The development of 'plyr' has been generously supported by 'Becton Dickinson'. Package: r-cran-pma Architecture: arm64 Version: 1.2-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pma_1.2-4-1.ca2604.1_arm64.deb Size: 263294 MD5sum: abd93ccbbe69ad02cbe273ea35c5bd5c SHA1: c173ce4762970fa99f2c8bc13da03f903ac2edac SHA256: 45a60b5d5d9d0acd6d3d696f556384e422ae88c646c2c66c80404aaaaa44778c SHA512: ae952109bddf7f6fff743bbe8ad3268e68e21e97d780de1e8f09fda3b6c59f5def4b5a73e0609b799848ab4dae33d4cde04ce6c4b96b72d0c9597425cac0ae11 Homepage: https://cran.r-project.org/package=PMA Description: CRAN Package 'PMA' (Penalized Multivariate Analysis) Performs Penalized Multivariate Analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis, described in Witten, Tibshirani and Hastie (2009) and Witten and Tibshirani (2009) Extensions of sparse canonical correlation analysis, with applications to genomic data . Package: r-cran-pmartr Architecture: arm64 Version: 2.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2881 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 2349602 MD5sum: e2ff0d779283c817fd2fa3a502cf18f2 SHA1: 51cd2db3c02c8099ca7fc6f98b4f8f6ceae1669d SHA256: db7ca42a1fe1604d7b7cdf4327da0f0bf0ff956b9209ff691a65947daedd98e3 SHA512: 1b20edf643697a0837bcbf6cc8e22a8198b4e1282cc1b109ed5f2b0ca91d435d67f8bc306e18534845b3f06a1e082b72d65940720105be10394718815cffde92 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-pmcmrplus Architecture: arm64 Version: 1.9.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1427 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1234458 MD5sum: e2e427e337fcd9bb52e8ea7fd582d37c SHA1: 5d4888d86004c98c1832c0808eddbf87d96da8fa SHA256: 74f0b4ece2d16f1f738d704438f57623ca029fa220fbc55bec09ca7c4274f2d8 SHA512: b0d33e7a34761d137a57c2d79659fcf5013488c3588b81c5d9a19ead14dbee0174a41ecd20de72d61d677ec653d7fe5dc35fc2dc0204056d661d2e60bf3f7b64 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: arm64 Version: 1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 508 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_arm64.deb Size: 193804 MD5sum: 094dbd3d9707692455b5b9eb56362909 SHA1: 43fe5958b6294611247e44c38087e64430ddba23 SHA256: 76ae29bd8bb1680f16369ebfc44dd47b9971058007f86ce5d0323922d4662a4d SHA512: 9809995c3a71b7add30bdfcbd0b76b998553f5ba772c738e2bfb19bb4ba8cb89a16e3da976abcc0755e45660abec13a6012fd48427f35ec78e99de67fb02bae7 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). The package implements multiple pMEM types (e.g., exponential, Gaussian, linear) and features a modular architecture that allows programmers to define custom weighting functions. Designed for ecologists, geographers, and spatial analysts working with spatially-structured data. 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Package: r-cran-podbay Architecture: arm64 Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 946 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_arm64.deb Size: 493854 MD5sum: ea902aedf723d8df36f8a4f20df161ca SHA1: cb81909e97085baf38a7f4caaae347094a5c5cb2 SHA256: a1834704cc6984fae40f05a2f0f8196b95917996a4e8eacc8590cf8c9a848075 SHA512: 11f2835bf04415a8127ec50294f8aae798d85a2ff8570fc74a57c08c178745045977ae4325680f61e477b3b82b887c82fce2e2bae9226a030552990ad261981b 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: arm64 Version: 1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-poibin_1.6-1.ca2604.1_arm64.deb Size: 28502 MD5sum: a05e4cbfe15ce3dbbb6e756566ad6f62 SHA1: e17e3c42076b4d7c522ec06897e0bb136bef4986 SHA256: 286cf69961a13838022d78d178107b625ac8e92f569ed0a5a4c2d67a4231fc4b SHA512: bdd93c811778a2032c2f8207404a2ba9a3dd9e33a3c1287f70f30492214cb4bea0487d2c25d6d5655a856e356804926cae8deeafd1e80d3063624b3b493f7790 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: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 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_arm64.deb Size: 49198 MD5sum: ccd5c9ab5ffb23f88db8231c4cc1d09b SHA1: 09afcdaf9834f4333bee5d34309203acc83875ec SHA256: f19a7e9b78129f47a10a81a0164ddcb0aaa25c627d9a7a5c3584781a3796dd26 SHA512: a273be2dc9284add1b5ce1daa194bbb39e9f19307c056208c165fdea55014668b911a92d3e384a23f3869260df0d611a3db365d1f765fd55931a66fac8e5c2df 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: arm64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 163 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_arm64.deb Size: 84686 MD5sum: f51666038f7ad81fbc21d5513f40ee6f SHA1: 445be2bcbeb4273881068b3cb275bc87434cfd10 SHA256: 4ffa64881aa0eb1fc64599139974ca997a4dbf01bd7307d1b4fbd958c8a1fde1 SHA512: d4531def72bb3a1455412a7ab207de06658f9f1600fbb24305024e723c61f5f67211ba5a25972bc9643b2c13d40d2d6a056256b7a5af999abaec29b83237b66b Homepage: https://cran.r-project.org/package=poisDoubleSamp Description: CRAN Package 'poisDoubleSamp' (Confidence Intervals with Poisson Double Sampling) Functions to create confidence intervals for ratios of Poisson rates under misclassification using double sampling. Implementations of the methods described in Kahle, D., P. Young, B. Greer, and D. Young (2016). "Confidence Intervals for the Ratio of Two Poisson Rates Under One-Way Differential Misclassification Using Double Sampling." Computational Statistics & Data Analysis, 95:122–132. Package: r-cran-poismf Architecture: arm64 Version: 0.4.0-4-1.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_arm64.deb Size: 98614 MD5sum: a6535037e68d51e2150d67000f389ce8 SHA1: a6eff0580999e464b863cb9b221085b613639c38 SHA256: 96aa9a8878edf71b85ba3b248735792e63db794e915a2983e46bfa319d5b43ca SHA512: 4b0359290066fd614569d716526ece512756ef82e504f80c8f938aee1e692b71ec0887e695e0912e34adc2f99fda59d5b50c507ddcd35e7b622231981697b191 Homepage: https://cran.r-project.org/package=poismf Description: CRAN Package 'poismf' (Factorization of Sparse Counts Matrices Through PoissonLikelihood) Creates a non-negative low-rank approximate factorization of a sparse counts matrix by maximizing Poisson likelihood with L1/L2 regularization (e.g. for implicit-feedback recommender systems or bag-of-words-based topic modeling) (Cortes, (2018) ), which usually leads to very sparse user and item factors (over 90% zero-valued). Similar to hierarchical Poisson factorization (HPF), but follows an optimization-based approach with regularization instead of a hierarchical prior, and is fit through gradient-based methods instead of variational inference. Package: r-cran-poissonbinomial Architecture: arm64 Version: 1.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 831 Depends: libc6 (>= 2.29), libfftw3-double3 (>= 3.3.10), libgcc-s1 (>= 4.0), libstdc++6 (>= 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_arm64.deb Size: 189592 MD5sum: 5ea80a08095702b241d996357523c12d SHA1: d4e1113a06a33ac16ac6c4f4ef780283948ada0f SHA256: 9cc4ab67e0d6bbc9c8badfcac214e16f5313160790031f441e132543afb6d420 SHA512: 6591d5cf0324fc531f11bc2d082cecdaa750162fbf81de38894f5885c800c1ed6451aad72333a586fa2a18090f01b33b4942951721e1f460315cae5c1d7ebf59 Homepage: https://cran.r-project.org/package=PoissonBinomial Description: CRAN Package 'PoissonBinomial' (Efficient Computation of Ordinary and Generalised PoissonBinomial Distributions) Efficient implementations of multiple exact and approximate methods as described in Hong (2013) , Biscarri, Zhao & Brunner (2018) and Zhang, Hong & Balakrishnan (2018) for computing the probability mass, cumulative distribution and quantile functions, as well as generating random numbers for both the ordinary and generalised Poisson binomial distribution. Package: r-cran-poissoned Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 23326 MD5sum: cc8300cad43ab5b27f990b2f69039c10 SHA1: ad6dba67490a73cf2cac8cb104881a6b0e4c5898 SHA256: a4959020d22e4a6d00e01eb10d5a475f0e09e5a9b4178b28349def437863eff7 SHA512: 52e5cfe31505f2bd09422266fe8410251951f22751f644e4244b141117600c79f332e7c39f24406d1ea176996c022ff7f10d15fc94ff96dcedf9660844776970 Homepage: https://cran.r-project.org/package=poissoned Description: CRAN Package 'poissoned' (Poisson Disk Sampling in 2D and 3D) Poisson disk sampling is a method of generating blue noise sample patterns where all samples are at least a specified distance apart. Poisson samples may be generated in two or three dimensions with this package. The algorithm used is an implementation of Bridson's "Fast Poisson disk sampling in arbitrary dimensions" . Package: r-cran-poissonmultinomial Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 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_arm64.deb Size: 70886 MD5sum: eb59dc7f4ee0830761a608d175c9c1fc SHA1: 1cacfd7f0bf51f80af5617bafb3c7c827a9ffda2 SHA256: 7da80e7ee37afc2b9cb6c0a8361b3c7f90808c910e4edf8a2b36c69779be6516 SHA512: ed61b840374ac98ac5b4f831a6a52613e966c3b7935ad62763175807ffb067a9164b1aac07e17bd4a09cf3327831f407164871552382d675b36e661e43187b3d Homepage: https://cran.r-project.org/package=PoissonMultinomial Description: CRAN Package 'PoissonMultinomial' (The Poisson-Multinomial Distribution) Implementation of the exact, normal approximation, and simulation-based methods for computing the probability mass function (pmf) and cumulative distribution function (cdf) of the Poisson-Multinomial distribution, together with a random number generator for the distribution. The exact method is based on multi-dimensional fast Fourier transformation (FFT) of the characteristic function of the Poisson-Multinomial distribution. The normal approximation method uses a multivariate normal distribution to approximate the pmf of the distribution based on central limit theorem. The simulation method is based on the law of large numbers. Details about the methods are available in Lin, Wang, and Hong (2022) . Package: r-cran-poissonpca Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 268 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), libstdc++6 (>= 4.3), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-poissonpca_1.0.3-1.ca2604.1_arm64.deb Size: 155878 MD5sum: 7baf0064ce72e97d97958482aecad8f0 SHA1: 36b15fe0921a4716e665c50a159e8f5f57f6318c SHA256: d3b1ed1f52f701b18966b25f9b7934a276ac13a62dbbf36a423908d45a505df6 SHA512: a78d28d9dbd20004dc629f6ca4519390d097845e2b54b8d00fa1bb64be46c13b9fa779ac5d138ea1dd0d8f808440797e34bc94fd28e6adc72abb9481ff1ab7fa Homepage: https://cran.r-project.org/package=PoissonPCA Description: CRAN Package 'PoissonPCA' (Poisson-Noise Corrected PCA) For a multivariate dataset with independent Poisson measurement error, calculates principal components of transformed latent Poisson means. T. Kenney, T. Huang, H. Gu (2019) . 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The package supports estimation of survival functions and absolute risk predictions from fitted cause-specific hazard models. For the Super Learner framework see van der Laan, Polley and Hubbard (2007) . 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Package: r-cran-polcaparallel Architecture: arm64 Version: 1.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 601 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 279218 MD5sum: bde58101b49d18d6544656d0a17b2905 SHA1: 7781eaa9702b803ab5277e00771bd94cc2420267 SHA256: 54b6563fe001c12f2c82fddde89c6665603f95a3be263473265d6b6e310f9792 SHA512: 2cdacc298221d5825900b5461683ac776c7e639bdc35c2c6f2445d38ae5a7b39c6cfd35af5cc55273bc8b75934fdce63ef908e87598843762c793f192a00aadd 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: arm64 Version: 1.2.4-1.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-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_arm64.deb Size: 134170 MD5sum: 23a98a7763bb260e68192514bb649c40 SHA1: c563b6cb84d9754fe3ac47776a30c2f1e2f45820 SHA256: 8644a67f75dc0421b21f48ad56b2703e7225e4adb89d756baf8e2d9cfe381375 SHA512: b6c37020ccd330fa420096690f13b18e4a4376371f8df6ea978fc6cf5d3538a1f92f810e8df60c9501208fbbe3817de2d70561982dd2ea79d5fa380fbc85c550 Homepage: https://cran.r-project.org/package=policytree Description: CRAN Package 'policytree' (Policy Learning via Doubly Robust Empirical Welfare Maximizationover Trees) Learn optimal policies via doubly robust empirical welfare maximization over trees. Given doubly robust reward estimates, this package finds a rule-based treatment prescription policy, where the policy takes the form of a shallow decision tree that is globally (or close to) optimal. Package: r-cran-polspline Architecture: arm64 Version: 1.1.25-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 777 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_arm64.deb Size: 573978 MD5sum: d63efaa91e9bde5bb50d351ba038a562 SHA1: 5320e27d062409b850776a784bc0807e1f94ffb6 SHA256: 744f4f235b255b506639d7ea6997380b489cd1a7873a88f551790419176abb43 SHA512: 366138688f6d36e6ffef33e18d338265118e4f650fc12fe841aa5cd4220722774933d764df8d8c635f08588fe66ba979b3818d2397852211d648b90f999d668c Homepage: https://cran.r-project.org/package=polspline Description: CRAN Package 'polspline' (Polynomial Spline Routines) Routines for the polynomial spline fitting routines hazard regression, hazard estimation with flexible tails, logspline, lspec, polyclass, and polymars, by C. Kooperberg and co-authors. Package: r-cran-polyapost Architecture: arm64 Version: 1.7-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 552 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_arm64.deb Size: 387692 MD5sum: 92153bced30819ad32652f89c79f26d5 SHA1: 50a2b0ec9d1917110d80da2f941c93df6f4acbe4 SHA256: 977e05f7734a21356b59ce0defd33616d491e2256c3579b6506e43bf1964a40e SHA512: 7d59650dade1316beba538e93c32b9c90d48909e50b9a832bb0c570d228ac265dd75a8ce3df3bef05a5a08696bc5c6b5c52b2fe6f66c1ddf8a1b889baee8c587 Homepage: https://cran.r-project.org/package=polyapost Description: CRAN Package 'polyapost' (Simulating from the Polya Posterior) Simulate via Markov chain Monte Carlo (hit-and-run algorithm) a Dirichlet distribution conditioned to satisfy a finite set of linear equality and inequality constraints (hence to lie in a convex polytope that is a subset of the unit simplex). Package: r-cran-polyclip Architecture: arm64 Version: 1.10-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 111410 MD5sum: 6459d9496dac811bccd82d4f29092411 SHA1: a542c11dc0bc9b6108c0fa26b0d59ed7a1f12944 SHA256: e7a495c2162c964704ec0e4a91ceacf945b3e45e6ab961a30eb08cb56155a3e1 SHA512: af6b14664cbab6f5892629b972db985261156215127db1f945d20ff0faf429a43eea5e5940645f3c0fcfa1d10a7ae5f5d512295633e8b6733bdc445fb9a35de7 Homepage: https://cran.r-project.org/package=polyclip Description: CRAN Package 'polyclip' (Polygon Clipping) R port of Angus Johnson's open source library 'Clipper'. Performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. Computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines. Computes Minkowski Sum of general polygons. There is a function for removing self-intersections from polygon data. Package: r-cran-polycub Architecture: arm64 Version: 0.9.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 389 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sp Suggests: r-cran-spatstat.geom, r-cran-lattice, r-cran-mvtnorm, r-cran-statmod, r-cran-sf, r-cran-cubature, r-cran-litedown, r-cran-microbenchmark Filename: pool/dists/resolute/main/r-cran-polycub_0.9.4-1.ca2604.1_arm64.deb Size: 226930 MD5sum: c8dce29f414d1fecc130d6b40de9aed2 SHA1: 66843a351db762719e34e25fe40aa934f292c1e3 SHA256: fa170313aa2529bf44003bbf881157f76fb1b34615d8a16fc9bede5524045e97 SHA512: 3f16b4033f453000067a42ef0edf696306381595fa7a20c23ceaa344cdacbd28363bdf60e631211de4022533f8ccfd721fff9080f9b918c8c51f326506df1cd3 Homepage: https://cran.r-project.org/package=polyCub Description: CRAN Package 'polyCub' (Cubature over Polygonal Domains) Numerical integration of continuously differentiable functions f(x,y) over simple closed polygonal domains. The following cubature methods are implemented: product Gauss cubature (Sommariva and Vianello, 2007, ), the simple two-dimensional midpoint rule (wrapping 'spatstat.geom' functions), and adaptive cubature for radially symmetric functions via line integrate() along the polygon boundary (Meyer and Held, 2014, , Supplement B). For simple integration along the axes, the 'cubature' package is more appropriate. Package: r-cran-polykde Architecture: arm64 Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4361 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_arm64.deb Size: 4044192 MD5sum: 256ea82ae51b6d227665768c2643707a SHA1: 43009bb2c421aa58ab4d966a105eafcf0b196f31 SHA256: 2d467b14addcfbad2632025c1f1439ccc7671ab002f8969e59d97274e6149609 SHA512: 85aecff80ac85fd01532cf9da9debb0fdf65b67d6dd89012945ea3228d0c797276b068aaa155af04f50b520833137f6c3c671f6add1f3de09461ca2dcb25d885 Homepage: https://cran.r-project.org/package=polykde Description: CRAN Package 'polykde' (Polyspherical Kernel Density Estimation) Kernel density estimation on the polysphere, (hyper)sphere, and circle. Includes functions for density estimation, regression estimation, ridge estimation, bandwidth selection, kernels, samplers, and homogeneity tests. Companion package to García-Portugués and Meilán-Vila (2025) and García-Portugués and Meilán-Vila (2023) . Package: r-cran-polylabelr Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 282 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 67798 MD5sum: 84d88fb8c8166650c00e81594b67ac48 SHA1: f1e6795999b27758b1118e9957283503c3b1b907 SHA256: 0b457ca3e7ecd9bdde536157ed1a6255976cde97525fee78808156050938cd17 SHA512: 40c516e1882e11251b8c41afb3e5e68dda753badd17c8de28b2aab44df2594fffe1d2ff383a725f4aef4f18b219f9ce098b54e469056953814fb43a9c7f1fab4 Homepage: https://cran.r-project.org/package=polylabelr Description: CRAN Package 'polylabelr' (Find the Pole of Inaccessibility (Visual Center) of a Polygon) A wrapper around the C++ library 'polylabel' from 'Mapbox', providing an efficient routine for finding the approximate pole of inaccessibility of a polygon, which usually serves as an excellent candidate for labeling of a polygon. Package: r-cran-polynomf Architecture: arm64 Version: 2.0-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 976 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 594492 MD5sum: 10a9b201477c021c4b3d1dda472fc15b SHA1: 6356facaf5f2ed46c3f406d6f2de9496f1d2aa44 SHA256: 3cf008136aca1840c4b861ca93f60fdc306e4307a3036af3137b8e1c076d92c3 SHA512: cb9a51858f315c88a484099c9f97655a953438b73f0fa1417e166a1ea51a03b4e5461fd5e43e90af7ea5d2011ba5753355be76b01ea2e38659690572480b3ef0 Homepage: https://cran.r-project.org/package=PolynomF Description: CRAN Package 'PolynomF' (Polynomials in R) Implements univariate polynomial operations in R, including polynomial arithmetic, finding zeros, plotting, and some operations on lists of polynomials. Package: r-cran-polyqtlr Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5076 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 3968336 MD5sum: d948eb49ad3c105561fd6e46939a4a91 SHA1: e8620eb69c020d7ff37e4d613d7fe085a495db46 SHA256: 37e2c6417d95d005b2cbb7d3a2959d66f58b190a6484e7f050d3bf29bd3bc431 SHA512: 0771155d49b414f771f6f458cf44140bcb3bd4d954cfa90fd4d66e0dbcad9819139823a5e4cdad82b181d1a2e81bc782d245efd2c46b61a73443f822cd7bbd84 Homepage: https://cran.r-project.org/package=polyqtlR Description: CRAN Package 'polyqtlR' (QTL Analysis in Autopolyploid Bi-Parental F1 Populations) Quantitative trait loci (QTL) analysis and exploration of meiotic patterns in autopolyploid bi-parental F1 populations. For all ploidy levels, identity-by-descent (IBD) probabilities can be estimated. Significance thresholds, exploring QTL allele effects and visualising results are provided. For more background and to reference the package see . Package: r-cran-polyrad Architecture: arm64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4648 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_arm64.deb Size: 2898070 MD5sum: 1082ac2894bddaec3d18744b0706e0d9 SHA1: 0201ce5e6d8894d465d8d2b1650dd43fd3e306ee SHA256: 90eb86187969d8d9a9c9534708c8d3f6ce669991da3588de77ebdabc4f8cfae1 SHA512: 7885adfbf6dadd8da06e63b9ea1c65f8adec582e08361a34cfcc1bacd0e7d641f9bf4052b93554e224b8cd1e6927fd2e27b36468c092cbf101bb351cd172469c Homepage: https://cran.r-project.org/package=polyRAD Description: CRAN Package 'polyRAD' (Genotype Calling with Uncertainty from Sequencing Data inPolyploids and Diploids) Read depth data from genotyping-by-sequencing (GBS) or restriction site-associated DNA sequencing (RAD-seq) are imported and used to make Bayesian probability estimates of genotypes in polyploids or diploids. The genotype probabilities, posterior mean genotypes, or most probable genotypes can then be exported for downstream analysis. 'polyRAD' is described by Clark et al. (2019) , and the Hind/He statistic for marker filtering is described by Clark et al. (2022) . A variant calling pipeline for highly duplicated genomes is also included and is described by Clark et al. (2020, Version 1) . Package: r-cran-polysat Architecture: arm64 Version: 1.7-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1882 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1283058 MD5sum: 356f1b924f3c3e3990cdd9426e272ab4 SHA1: 03a540efd9eb092f1eee4786c5d5f5823267f4ac SHA256: 4783f6e4de9c69a86b70c25bdd3831158e49498330ab86cab777fedc2e796e08 SHA512: dddb36ef13aaacb83da34dbde5a27e09a4a642eef20c583703937d5f6e3e90ed0da11576655e4a37ad5417c1b33684b1b99ebbcbec63080f609b1b207ebad608 Homepage: https://cran.r-project.org/package=polysat Description: CRAN Package 'polysat' (Tools for Polyploid Microsatellite Analysis) A collection of tools to handle microsatellite data of any ploidy (and samples of mixed ploidy) where allele copy number is not known in partially heterozygous genotypes. It can import and export data in ABI 'GeneMapper', 'Structure', 'ATetra', 'Tetrasat'/'Tetra', 'GenoDive', 'SPAGeDi', 'POPDIST', 'STRand', and binary presence/absence formats. It can calculate pairwise distances between individuals using a stepwise mutation model or infinite alleles model, with or without taking ploidies and allele frequencies into account. These distances can be used for the calculation of clonal diversity statistics or used for further analysis in R. Allelic diversity statistics and Polymorphic Information Content are also available. polysat can assist the user in estimating the ploidy of samples, and it can estimate allele frequencies in populations, calculate pairwise or global differentiation statistics based on those frequencies, and export allele frequencies to 'SPAGeDi' and 'adegenet'. Functions are also included for assigning alleles to isoloci in cases where one pair of microsatellite primers amplifies alleles from two or more independently segregating isoloci. polysat is described by Clark and Jasieniuk (2011) and Clark and Schreier (2017) . Package: r-cran-polywog Architecture: arm64 Version: 0.4-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 361 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_arm64.deb Size: 181938 MD5sum: 5c75b816903778e116ad2fc6f592f026 SHA1: 65faa7b0fbb9260d78ad00fa86d96aa5114243a6 SHA256: 1017db8b9384879c36928568e280cc89613ce03af1be8245be208071ccd26c77 SHA512: 78dae82fff8dfa8aabaaf23c1af88a3d163168491df26d9f11719d7ffef36ef3cb13dba84fc02e10d3838190edaadb853f4cedadda4b5678b8d522e97f1f3565 Homepage: https://cran.r-project.org/package=polywog Description: CRAN Package 'polywog' (Bootstrapped Basis Regression with Oracle Model Selection) Routines for flexible functional form estimation via basis regression, with model selection via the adaptive LASSO or SCAD to prevent overfitting. Package: r-cran-pomaspu Architecture: arm64 Version: 1.0.0-1.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_arm64.deb Size: 67836 MD5sum: a2b87a2f94ffca201fea4fd2d0d9e430 SHA1: 0d8b5520e29a832d1e695e6330d93dfa59c1362f SHA256: a97c8b1c8e8887bd519628cbfc94705bc6f83a34b84c77d59002233ee6ebc726 SHA512: 701da4779f3c038710c9fe66ffd642e3ba06193c44cf63e8ab04797536e32fd4004d2f63d7055d29fd948d643e330541c1dec40e65f4bc7529a459abdf6aeed2 Homepage: https://cran.r-project.org/package=POMaSPU Description: CRAN Package 'POMaSPU' (Adaptive Association Tests for Multiple Phenotypes usingProportional Odds Model (POM-aSPU)) POM-aSPU test evaluates an association between an ordinal response and multiple phenotypes, for details see Kim and Pan (2017) . Package: r-cran-pomdp Architecture: arm64 Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1915 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1315506 MD5sum: e6d29915591be0076667c00a5623ee26 SHA1: 2569be4595cf9ad819dd6655d6a8e9a50673afaa SHA256: 5ccffa5dfdc59af52cc5015e877a2678dc689f18619fe6df8f0185b7070acf19 SHA512: 299cf1d504508ffde18b60a03cdf22eed534625208518ad605343b1f5b81876f8ceb0faa96f7346a1935f52c5f4a82514fa6b4f7fb9f8d5eb1a0f40290a8e699 Homepage: https://cran.r-project.org/package=pomdp Description: CRAN Package 'pomdp' (Infrastructure for Partially Observable Markov DecisionProcesses (POMDP)) Provides the infrastructure to define and analyze the solutions of Partially Observable Markov Decision Process (POMDP) models. Interfaces for various exact and approximate solution algorithms are available including value iteration, point-based value iteration and SARSOP. Hahsler and Cassandra . Package: r-cran-pomdpsolve Architecture: arm64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 413 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-pomdp Filename: pool/dists/resolute/main/r-cran-pomdpsolve_1.0.6-1.ca2604.1_arm64.deb Size: 152630 MD5sum: 975a2c5c60c1ee501889119b0cbbfbe0 SHA1: f092eb06971bb9c95d64e56b31c9bd2437a47b63 SHA256: 94fe9234ec9bf031ee4c403cb7b4e0a219e37cd40ef807baaab71de52cf33de0 SHA512: cd73d1318c8f42248e423eaa78c2bd7864b44d2a90d0b6c570a17681425f8aae2ba83f96db6be0b18e6f1fb5bfdf18f3bd09967afbff8c7b214765e115000e70 Homepage: https://cran.r-project.org/package=pomdpSolve Description: CRAN Package 'pomdpSolve' (Interface to 'pomdp-solve' for Partially Observable MarkovDecision Processes) Installs an updated version of 'pomdp-solve' and provides a low-level interface. Pomdp-solve is a program to solve Partially Observable Markov Decision Processes (POMDPs) using a variety of exact and approximate value iteration algorithms. A convenient R infrastructure is provided in the separate package pomdp. Hahsler and Cassandra . Package: r-cran-pomp Architecture: arm64 Version: 6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2055 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_arm64.deb Size: 1448486 MD5sum: ee81aef47b90e660a954b3e0421494c8 SHA1: 59a9b8de980c0caf6c8411aafabaf7c9e3829bad SHA256: 8b16e678b7bc7986977f20f6b831815ec834f739ffcc9c36ee718ae67fde3ecf SHA512: 6cecf1810dfae48d1e5d89e555c3dac252fd3b2f8dd4140257ca355e9473d4e7103a2d2b58db3825dfd198871081069473207019d08a4d57905c158f423a923b Homepage: https://cran.r-project.org/package=pomp Description: CRAN Package 'pomp' (Statistical Inference for Partially Observed Markov Processes) Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models. Package: r-cran-pompp Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 749 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_arm64.deb Size: 394654 MD5sum: 79af0f329df0e1836c08e3fe9293f142 SHA1: 706783ecc50b6b1a89d0d98e75cc0763bebefef9 SHA256: b2799d0dc48f1b4e8ee7e4016a6cce13ddc490c16d40d5af9114deaea2e8f2ed SHA512: 97e654598edc11b0280b6e2e14832411a561cb0d1a4583ef15a5c53d746c040fb319ec975582acbaea054139da378f808146f106adcfc9d374179ddfde53ad43 Homepage: https://cran.r-project.org/package=pompp Description: CRAN Package 'pompp' (Presence-Only for Marked Point Process) Inspired by Moreira and Gamerman (2022) , this methodology expands the idea by including Marks in the point process. Using efficient 'C++' code, the estimation is possible and made faster with 'OpenMP' enabled computers. This package was developed under the project PTDC/MAT-STA/28243/2017, supported by Portuguese funds through the Portuguese Foundation for Science and Technology (FCT). Package: r-cran-pooh Architecture: arm64 Version: 0.3-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-pooh_0.3-2-1.ca2604.1_arm64.deb Size: 19314 MD5sum: 7629fb2c1003e8ce668d732b4f6909d1 SHA1: 1f0a0d6d330174de862eeab42983dfce9f7502de SHA256: bfdb23beaba1565b45f3290f9556d1e7f8ac57ef8c05954251349569f700c0bc SHA512: 76641e8d3cb74bcaae0c143397a6fde50105a8fba648d263f4d5ba6cfb8c84fadf595f3548261175e543efb2145f3e5ef34595f78f0432e83962928df29da84e Homepage: https://cran.r-project.org/package=pooh Description: CRAN Package 'pooh' (Partial Orders and Relations) Finds equivalence classes corresponding to a symmetric relation or undirected graph. Finds total order consistent with partial order or directed graph (so-called topological sort). Package: r-cran-poolfstat Architecture: arm64 Version: 3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3328 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_arm64.deb Size: 2915242 MD5sum: f0f748b16622de892a562daf43faddc5 SHA1: 5196ac074123f7fadd731c6b7077e078a1473d3e SHA256: 14950bbf27081d4d5bcd2fba093727eab4f03ae681e22575fa6b16c4e55fed0a SHA512: 49f806786d452d65c2f4fe3386bb8c11fda23a3783461d2e8635b7e467bc87a5e581d488975757340409f9199052548e01973706662bf2aa6b80913b59e41bfc Homepage: https://cran.r-project.org/package=poolfstat Description: CRAN Package 'poolfstat' (Computing f-Statistics and Building Admixture Graphs Based onAllele Count or Pool-Seq Read Count Data) Functions for the computation of F-, f- and D-statistics (e.g., Fst, hierarchical F-statistics, Patterson's F2, F3, F3*, F4 and D parameters) in population genomics studies from allele count or Pool-Seq read count data and for the fitting, building and visualization of admixture graphs. The package also includes several utilities to manipulate Pool-Seq data stored in standard format (e.g., such as 'vcf' files or 'rsync' files generated by the the 'PoPoolation' software) and perform conversion to alternative format (as used in the 'BayPass' and 'SelEstim' software). As of version 2.0, the package also includes utilities to manipulate standard allele count data (e.g., stored in 'TreeMix', 'BayPass' and 'SelEstim' format, see the Package vignette for details). Package: r-cran-pooltestr Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2919 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_arm64.deb Size: 861808 MD5sum: 5f590277d40460feefd9fcad9bc54e55 SHA1: 8bad8658c2682bb4d94a03cc23d4c91f9d17e2c7 SHA256: 026d803f277e827a663271b22cff7d10995ec51c84b6143ccde3deaf06e77902 SHA512: cf3c588d61204b3a3fdfb8a6fd9dc0b474902e6a769fcd21a272c3e2b4868249ea08d5967583ce2032cdbe49d1dcf021dddf8653b06ce482f3a5533a78c6b2c3 Homepage: https://cran.r-project.org/package=PoolTestR Description: CRAN Package 'PoolTestR' (Prevalence and Regression for Pool-Tested (Group-Tested) Data) An easy-to-use tool for working with presence/absence tests on 'pooled' or 'grouped' samples. The primary application is for estimating prevalence of a marker in a population based on the results of tests on pooled specimens. This sampling method is often employed in surveillance of rare conditions in humans or animals (e.g. molecular xenomonitoring). The package was initially conceived as an R-based alternative to the molecular xenomonitoring software, 'PoolScreen' . However, it goes further, allowing for estimates of prevalence to be adjusted for hierarchical sampling frames, and perform flexible mixed-effect regression analyses (McLure et al. Environmental Modelling and Software. ). The package is currently in early stages, however more features are planned or in the works: e.g. adjustments for imperfect test specificity/sensitivity, functions for helping with optimal experimental design, and functions for spatial modelling. Package: r-cran-pop.lion Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 122 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 42176 MD5sum: 49a7cba70751d496d56c5e5246c49be3 SHA1: f7bae0d77c40110935d1b4fe0eb810f4238ebf2f SHA256: f73e157880853cae54a268e907e8e85486794ab031a1c8faf5d8bbeb25c60b43 SHA512: 55b6e74c58443ed4956572494bd34d6ff4d67ed0bd20f68267ff552739710f982d2d069c0e87556ba5ea28303e4203e0e00c2c1f228add82b937bcf46bb968d3 Homepage: https://cran.r-project.org/package=pop.lion Description: CRAN Package 'pop.lion' (Models for Simulating Lion Populations) Simulate the dynamic of lion populations using a specific Individual-Based Model (IBM) compiled in C. Package: r-cran-pop.wolf Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 33224 MD5sum: 4c32fbbad491452afe33e87a356b2d6d SHA1: 1ee1c279c9926947bf5900166780799306ac9ac5 SHA256: a6fdbea4c2b91a21162798e3e27904835d97b28356e8dc65413e39800794ba4c SHA512: ac4ca021403e7d918ba8f7647d5da7854a94247cf249be0edb604b8b6890ad07a19e2490844c0a0e0a875dbd21a59e07d72b194cc9d9b28da8a4df0ea1cec387 Homepage: https://cran.r-project.org/package=pop.wolf Description: CRAN Package 'pop.wolf' (Models for Simulating Wolf Populations) Simulate the dynamic of wolf populations using a specific Individual-Based Model (IBM) compiled in C, see Chapron et al. (2016) . Package: r-cran-poppcr Architecture: arm64 Version: 0.1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 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_arm64.deb Size: 229622 MD5sum: df1889cbdde45fc2b29b9266f57e2633 SHA1: 4ba6271a49e8436d793254e3375c65d2287c5180 SHA256: 14eff1c6acf6e3ee81532608888f70e4cff41deed9569ef13ef75e8717f1720b SHA512: 6805f42d059faf67d6803fb2a269d4624e75ed3847ce402b224f9da412e7a8d8c1771a54b7e64fed77023c21106c1f594ef14c0ac2b605b4e04c37a59ccf2cd7 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: arm64 Version: 2.9.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2314 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1759854 MD5sum: 7bce449adb113258b27f830b8772703e SHA1: 603dbb9c22f087dd51fece1b827ed148ce05ef1d SHA256: a6fc29ab16dd0a8e5d0745357cdcf498a1437231eacbdbdcec80b62389164881 SHA512: 223d7d7c2f381e58539f6d87635bbe6e4a3a8b2070fd2219b4ea1dd366f5f89bf0506ab171a4c074ed451d7a6ca589a6ed559ffa1ba1c660bfc8c56acd0bfd4d 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: arm64 Version: 7.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 97592 MD5sum: 3b4ab6b9ebbb6000ec64cdfab29c8536 SHA1: 686a22224c8376e1ffe9078a0665a1d26727ca8d SHA256: 42ba34ffcdcecaf6a9d5d7901554f2f8b38c63b0032574d18853554b9a8323f6 SHA512: df91f928a2f7d33bf3e4f008e3c000b18b6072166ac85f407dfeb65f728f4ef51017b101d425c34cf379a5776db338d93a2f18eaedfa47557685f94d2fc21937 Homepage: https://cran.r-project.org/package=popsom7 Description: CRAN Package 'popsom7' (A Fast, User-Friendly Implementation of Self-Organizing Maps(SOMs)) Methods for building self-organizing maps (SOMs) with a number of distinguishing features such automatic centroid detection and cluster visualization using starbursts. For more details see the paper "Improved Interpretability of the Unified Distance Matrix with Connected Components" by Hamel and Brown (2011) in . The package provides user-friendly access to two models we construct: (a) a SOM model and (b) a centroid based clustering model. The package also exposes a number of quality metrics for the quantitative evaluation of the map, Hamel (2016) . Finally, we reintroduced our fast, vectorized training algorithm for SOM with substantial improvements. It is about an order of magnitude faster than the canonical, stochastic C implementation . Package: r-cran-population Architecture: arm64 Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 31370 MD5sum: bc72b88122dcb64ec7ee0201b3a538a3 SHA1: 231f527d0dd477c19b513c3185437a209e8e329f SHA256: f74ba2af010757b65e78253e30b889ae2aec533e888bb3601bdd8f006f7a20fb SHA512: d0afbcff57696bdaa7e07e6dfc46faf92b9d3c204b8748af9db268c5e3cef9bf07482ae406b184005ad80b5bc5002591d922b529d0c23d9f9fb973af251c3669 Homepage: https://cran.r-project.org/package=population Description: CRAN Package 'population' (Models for Simulating Populations) Run population simulations using an Individual-Based Model (IBM) compiled in C. Package: r-cran-poputils Architecture: arm64 Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1228 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-lifecycle, r-cran-rlang, r-cran-rvec, r-cran-tibble, r-cran-tidyselect, r-cran-vctrs, r-cran-cpp11 Suggests: r-cran-bookdown, r-cran-covr, r-cran-dplyr, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-poputils_0.6.1-1.ca2604.1_arm64.deb Size: 1028512 MD5sum: 10313bfed6bfc66f3d33c8b3d3698cf1 SHA1: 2fcb5dbd9c1b9fc913fe67ef10a98b356d4e66fb SHA256: ff6af619916a6ee835abe0a8a1e1ac452f72192d48beb4b3e479abcf3a3686ee SHA512: 24bf4852a3c18dffae3a7d7e2742b92cf63c4759e7b09fab6a5a5c475340cc8b68800db5a9d421e9f3dd4313b01920745c3b17cfb3b00b6eae33e58900bf97bf Homepage: https://cran.r-project.org/package=poputils Description: CRAN Package 'poputils' (Demographic Analysis and Data Manipulation) Perform tasks commonly encountered when preparing and analysing demographic data. Some functions are intended for end users, and others for developers. Includes functions for working with life tables. Package: r-cran-porridge Architecture: arm64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 667 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-matrix, r-cran-mvtnorm, r-cran-rcpp, r-cran-pracma, r-cran-rcpparmadillo Suggests: r-cran-rags2ridges Filename: pool/dists/resolute/main/r-cran-porridge_0.3.3-1.ca2604.1_arm64.deb Size: 378474 MD5sum: a1e57df2a1f717ef33cbee39b3b767c1 SHA1: 8e530add906ef03cd6435a2626bdea4f56c5386f SHA256: 518202079f096943a3e92f879781e0ed98535d92fe67a3fcfee604dbc4975a3b SHA512: 76d65ce03d57cfa72d91eba58121ebf8eba9b86d5a4c1e2810ee71f7f730be07bbe901e9958979c6c1552450e7644a4423fd4d70e56de85d738b2d76e143d892 Homepage: https://cran.r-project.org/package=porridge Description: CRAN Package 'porridge' (Ridge-Type Penalized Estimation of a Potpourri of Models) The name of the package is derived from the French, 'pour' ridge, and provides functionality for ridge-type estimation of a potpourri of models. Currently, this estimation concerns that of various Gaussian graphical models from different study designs. Among others it considers the regular Gaussian graphical model and a mixture of such models. The porridge-package implements the estimation of the former either from i) data with replicated observations by penalized loglikelihood maximization using the regular ridge penalty on the parameters (van Wieringen, Chen, 2021) or ii) from non-replicated data by means of either a ridge estimator with multiple shrinkage targets (as presented in van Wieringen et al. 2020, ) or the generalized ridge estimator that allows for both the inclusion of quantitative and qualitative prior information on the precision matrix via element-wise penalization and shrinkage (van Wieringen, 2019, ). Additionally, the porridge-package facilitates the ridge penalized estimation of a mixture of Gaussian graphical models (Aflakparast et al., 2018). On another note, the package also includes functionality for ridge-type estimation of the generalized linear model (as presented in van Wieringen, Binder, 2022, ). 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For more details see Scrucca and Serafini (2019) . Package: r-cran-ppmiss Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 141 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-copula, r-cran-pracma, r-cran-zoo Filename: pool/dists/resolute/main/r-cran-ppmiss_0.1.2-1.ca2604.1_arm64.deb Size: 45748 MD5sum: ad6291b4370e741b251c483b5a4b75fb SHA1: 84f72295271cf9dc676252c1f17d034a321a7c3b SHA256: 9b97ecd683f3e3e979088b68b46a1a637b685f30e80d0986a5e591bff68cb118 SHA512: 30bc3262c0b98a820774047d7642fc4b1613cae81475ee29d85be25657d9071702488b842a24226f5eb8d225dd305fa014417a402181961d10d22f1b81f5eae1 Homepage: https://cran.r-project.org/package=PPMiss Description: CRAN Package 'PPMiss' (Copula-Based Estimator for Long-Range Dependent Processes underMissing Data) Implements the copula-based estimator for univariate long-range dependent processes, introduced in Pumi et al. (2023) . Notably, this estimator is capable of handling missing data and has been shown to perform exceptionally well, even when up to 70% of data is missing (as reported in ) and has been found to outperform several other commonly applied estimators. Package: r-cran-ppmr Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 171912 MD5sum: e680e3efa308c0646df620be7fdbc17a SHA1: 02bdd040ecf14dc70e6737fc7cfe1a5d4d92c859 SHA256: 384fceb6d95cc91352669e800a5d455443e01df0788e2b3a184e0e386d84dad8 SHA512: 51cdeb941cf42410d5a622e8852a6f276c15b7e5250ecc015520a0f2fa13ace2a8d08dfe749d6bea210c2392154d80cda12461d391c33cd56dca8546c3b5c7ca Homepage: https://cran.r-project.org/package=PPMR Description: CRAN Package 'PPMR' (Probabilistic Two Sample Mendelian Randomization) Efficient statistical inference of two-sample MR (Mendelian Randomization) analysis. It can account for the correlated instruments and the horizontal pleiotropy, and can provide the accurate estimates of both causal effect and horizontal pleiotropy effect as well as the two corresponding p-values. There are two main functions in the 'PPMR' package. One is PMR_individual() for individual level data, the other is PMR_summary() for summary data. Package: r-cran-ppmsuite Architecture: arm64 Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 480 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_arm64.deb Size: 284852 MD5sum: 1b2425b196b05134eac5d68df8c67891 SHA1: 1ec8c07c319d7cd6c4e30eda1b162dc30f875b62 SHA256: b7ef7efe31e16f931d91d0ec1b67ea6b055b2bbc6e96bb8e9e8f1e09a46aef20 SHA512: 952e09fa3ffdc5efe82f1b85f46e2b783cd5baaa89096c10eb32c9f0574a8a1aba79c8c5c1c47cf2faf1a337f4844e177b5362f7d24f3a8154591a1f65e87df7 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: arm64 Version: 0.3.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1599 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_arm64.deb Size: 355652 MD5sum: ec9b0226b8818464f06f3686dc5ce438 SHA1: 5e17601636bac574024148be7d718573a706c7a7 SHA256: d2d7b213b438940aaa1ef61a1af2d26fb1eec0e53c55c81f885db9ec88d8f55d SHA512: c5cc86d87f2df5222bd2d1796e371e3303e3088619ef044117ffd22919565da709c1f01b55087debdd9e64db4406614318322cd47591a1de0fe2afaae14b49bc 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: arm64 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_arm64.deb Size: 1298636 MD5sum: 40f722750ee2258aeaba333a4725c0d3 SHA1: d67f1128f473ffd39a1c45597751d19600566694 SHA256: 99b27b74cc6e0527e0afc0aa322e51cbab3bb4094ef286083bef58a5329cb3f3 SHA512: 031fe369847e8b09c38a6057c5b0338bc7fc7854a4fbd6897742d3fed6afb489eed29e06b74fcbc8b501925f413ea7e7005d3837eb1c3ebbec4f9b04119c850d 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: arm64 Version: 0.1.3-1.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), 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_arm64.deb Size: 68086 MD5sum: 29f4d8c7114446f318f03f7e0f7ca1a0 SHA1: 0b4e0a01359bc62d3eab053ce404ec7ecd6205d2 SHA256: bf7f77aa9b928323a299a9a69c9add510014bffc352cd9506810c1c570e276c2 SHA512: c0e377b002522551f8266173ac2ec455f41ae502edd8d08c23f7e5d00d59dbc9b9c43e7ce6d45585860f41ddd88d4f00d8dd163c770e3d2751ebd1a6da65de49 Homepage: https://cran.r-project.org/package=PPSFS Description: CRAN Package 'PPSFS' (Partial Profile Score Feature Selection in High-DimensionalGeneralized Linear Interaction Models) This is an implementation of the partial profile score feature selection (PPSFS) approach to generalized linear (interaction) models. The PPSFS is highly scalable even for ultra-high-dimensional feature space. See the paper by Xu, Luo and Chen (2022) . Package: r-cran-pptreeext Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 990 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 685934 MD5sum: 4d728c294be9068a532560213448e5ec SHA1: 837bd318056b475323222a4a83cff4326838437e SHA256: 1deffa49c8ba868e2af592da85ea203445054d22deed90d21c72f74ebf87abe2 SHA512: bc249b526d489d73ec58ae35eabdce8f7aa7ea5999604e220c8b722c716d6843391c857b6670c1aed2c4aeaa313e44a855f12d98bf2ab6bbba9a03134170e13c Homepage: https://cran.r-project.org/package=PPtreeExt Description: CRAN Package 'PPtreeExt' (Projection Pursuit Classification Tree Extensions) Implements extensions to the projection pursuit tree algorithm for supervised classification, see Lee, Y. (2013), and Lee, E-K. (2018) . The algorithm is changed in two ways: improving prediction boundaries by modifying the choice of split points-through class subsetting; and increasing flexibility by allowing multiple splits per group. Package: r-cran-pptreeviz Architecture: arm64 Version: 2.0.4-1.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-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_arm64.deb Size: 187370 MD5sum: b69fc1d5c78f7d1f988a09dd48f28908 SHA1: 1e57f9f288a76248a2193eb4fd73fc8c8c45f65d SHA256: fcec9919ce2372fecac180a332e41b819c677b2ea6e846f7cd78b057c79fad62 SHA512: 1cc757772cb2d5a3972aa108f0d99eece6dbd19f6be3b21077d463411606acdacb47b4c7591953e6a53bf6f116cfd41cfeedce444ec719b4734c19f67f8c9090 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: arm64 Version: 1.2.2-1.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 (>= 4.9), 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_arm64.deb Size: 342900 MD5sum: 4cdc2ec2ac11826500d170bda3d48f32 SHA1: 0d277fadc7ebe5a82a101e00a83f108e5308ed62 SHA256: 2ddee49a22f7f061b73c71970551138dadb136ec37d68883cf27014e3607dc2c SHA512: 20c9dd369d7494ebfbeee83dfffd31e76235757eef3a97df35ab90da2b20bf7a05621403700eb24ee1bd42d6ab628078cf51eaa6c7b83d2b6c6e1d86d72c6958 Homepage: https://cran.r-project.org/package=pqrBayes Description: CRAN Package 'pqrBayes' (Bayesian Penalized Quantile Regression) Bayesian regularized quantile regression utilizing two major classes of shrinkage priors (the spike-and-slab priors and the horseshoe family of priors) leads to efficient Bayesian shrinkage estimation, variable selection and valid statistical inference. In this package, we have implemented robust Bayesian variable selection with spike-and-slab priors under high-dimensional linear regression models (Fan et al. (2024) and Ren et al. (2023) ), and regularized quantile varying coefficient models (Zhou et al.(2023) ). In particular, valid robust Bayesian inferences under both models in the presence of heavy-tailed errors can be validated on finite samples. Additional models with spike-and-slab priors include robust Bayesian group LASSO and robust binary Bayesian LASSO (Fan and Wu (2025) ). Besides, robust sparse Bayesian regression with the horseshoe family of (horseshoe, horseshoe+ and regularized horseshoe) priors has also been implemented and yielded valid inference results under heavy-tailed model errors (Fan et al.(2026) ). The Markov chain Monte Carlo (MCMC) algorithms of the proposed and alternative models are implemented in C++. Package: r-cran-pqrfe Architecture: arm64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 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_arm64.deb Size: 101412 MD5sum: 4073ece4f55abd75029bd91bdf8ec0b0 SHA1: 0aa94dc16e884ce60a6b325a2e66f417f150afa5 SHA256: ba8fc89b70f287324711fe591dfd2f4bec7945e49c95cb0509f75fce58ff8de4 SHA512: a3aa60f73050b12e3c0cb8b893ca50695b7e0b50e440fdb36a53f5c41d5adf6cde3c1a490fa174bff55001c41cc4a1259107f087303e514182cdfcf19571abac Homepage: https://cran.r-project.org/package=pqrfe Description: CRAN Package 'pqrfe' (Penalized Quantile Regression with Fixed Effects) Quantile regression with fixed effects is a general model for longitudinal data. Here we proposed to solve it by several methods. The estimation methods include three loss functions as check, asymmetric least square and asymmetric Huber functions; and three structures as simple regression, fixed effects and fixed effects with penalized intercepts by LASSO. Package: r-cran-praznik Architecture: arm64 Version: 12.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 693 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 543536 MD5sum: 235b2e203795ad20a93850a62f73cf7e SHA1: 2c43446870a37483e0a3c083fad56bb568311e6e SHA256: 5e6280b742c8f0f269e5babf1a1110225a5df7ac0bfea16d68c18dea603f0a39 SHA512: f1b30572432b1b23def716f95a6f9e0a1121095c7ef1bb27cabbad8c9b0f9984e99cb084d92e81deec98ff4a4ede11f873fe6e279400af84b8369ce09f82b8ac Homepage: https://cran.r-project.org/package=praznik Description: CRAN Package 'praznik' (Tools for Information-Based Feature Selection and Scoring) A toolbox of fast, native and parallel implementations of various information-based importance criteria estimators and feature selection filters based on them, inspired by the overview by Brown, Pocock, Zhao and Lujan (2012) . Contains, among other, minimum redundancy maximal relevancy ('mRMR') method by Peng, Long and Ding (2005) ; joint mutual information ('JMI') method by Yang and Moody (1999) ; double input symmetrical relevance ('DISR') method by Meyer and Bontempi (2006) as well as joint mutual information maximisation ('JMIM') method by Bennasar, Hicks and Setchi (2015) . Package: r-cran-prcbench Architecture: arm64 Version: 1.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1045 Depends: libc6 (>= 2.17), 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-assertthat, r-cran-gridextra, r-cran-ggplot2, r-cran-memoise, r-cran-rocr, r-cran-prroc, r-cran-precrec Suggests: r-cran-microbenchmark, r-cran-rjava, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-vdiffr, r-cran-patchwork Filename: pool/dists/resolute/main/r-cran-prcbench_1.1.10-1.ca2604.1_arm64.deb Size: 734454 MD5sum: bf300dce286b5bcd256dab95529b45f4 SHA1: bd18d38d12d366179b4e0da4b9057512c5b150f0 SHA256: 2a7602181191d89c55e71fa3b0f88e314743476ffaae0f8887e180fb8615475b SHA512: 5d6e7ebcc3a3f0c4600cb9d2a53b5022580f5f2ac30e9a7d2f73d2b4ee1d86f75cecab00a62af1f91a99f422d7fe17ed46d0f77605acfacda07a3c2cdd45ec6d Homepage: https://cran.r-project.org/package=prcbench Description: CRAN Package 'prcbench' (Testing Workbench for Precision-Recall Curves) A testing workbench to evaluate tools that calculate precision-recall curves. Saito and Rehmsmeier (2015) . Package: r-cran-prclust Architecture: arm64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 272 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 97182 MD5sum: b5bff8c2b2a8b993f5a4e636c7c9a464 SHA1: 7c64df9874dacb5d8ae7af1a176a7deacb3e5644 SHA256: 4a29f52d42ba5d94fc4c338bdb4466f00e9b0480b5a9d259565bae1a96f62910 SHA512: ab04ae18cdea3ae724d7e0750d7444e69c4fafdf9330fdfd43013d3cdf178a8f3ac58eed01f0292571cdaab837d95e0a6fe9d4d4c615fcdc40623895e73c2a92 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. Package: r-cran-prda Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 465 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-pbapply, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-covr, r-cran-devtools, r-cran-ggplot2, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-tidyverse Filename: pool/dists/resolute/main/r-cran-prda_1.0.2-1.ca2604.1_arm64.deb Size: 185288 MD5sum: 0d1e19db91eed3c087cc0991f63b4db5 SHA1: f097e7daa52cc50f3da4f76380f96e2fd36d7812 SHA256: bc0f5a94386dac600339fff0a3ce8c942e7daf554db5a2a4197f4284a6dbcf34 SHA512: 2637db51d929fb12f1033ae5067da46085977888a87112f79b742cbd6b11faa01b62053475eac77e89a82207f381f6a84f33dbcc971a6a028a90723c40b1fe61 Homepage: https://cran.r-project.org/package=PRDA Description: CRAN Package 'PRDA' (Conduct a Prospective or Retrospective Design Analysis) An implementation of the "Design Analysis" proposed by Gelman and Carlin (2014) . 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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Package: r-cran-prioriactions Architecture: arm64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5195 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2610708 MD5sum: b479516c26ff147e1aa6d750d34e6622 SHA1: 0fa4047bd4cf673c5721fda791ebe9c2b7c085ee SHA256: 1c50151f81df0fb1cf3cd7cb3904e2199eb1128e7207a66429e0d54bf5041708 SHA512: 5a7e892c11cbe2770b3ff90d69426140c248b11182ae3c76c1e3c345f5971fd8e3e0d2009bb21e42dcccaf2ec8e3e2f12d8e97d98c5e1d299ea0c054017033a8 Homepage: https://cran.r-project.org/package=prioriactions Description: CRAN Package 'prioriactions' (Multi-Action Conservation Planning) This uses a mixed integer mathematical programming (MIP) approach for building and solving multi-action planning problems, where the goal is to find an optimal combination of management actions that abate threats, in an efficient way while accounting for spatial aspects. Thus, optimizing the connectivity and conservation effectiveness of the prioritized units and of the deployed actions. The package is capable of handling different commercial (gurobi, CPLEX) and non-commercial (symphony, CBC) MIP solvers. Gurobi optimization solver can be installed using comprehensive instructions in the 'gurobi' installation vignette of the prioritizr package (available in ). Instead, 'CPLEX' optimization solver can be obtain from IBM CPLEX web page (available here ). Additionally, the 'rcbc' R package (available at ) can be used to obtain solutions using the CBC optimization software (). Methods used in the package refers to Salgado-Rojas et al. (2020) , Beyer et al. (2016) , Cattarino et al. (2015) and Watts et al. (2009) . See the prioriactions website for more information, documentations and examples. Package: r-cran-prioritizr Architecture: arm64 Version: 8.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9677 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_arm64.deb Size: 5757376 MD5sum: 90d4ea0138ded475112d16efa7d9570a SHA1: 2bf814cc42894767f37c674dd3f5269c2533efa9 SHA256: e08ffc6141b564d2c13895385966308cf7c9eee5fe2872f2f417cae68ac763b1 SHA512: 233c914674439794ea462641c5805d4bb11a5432e68238e091765ccd7772959cab12e146211ba91270107f44c0d7b13cd3bfc13c81f637ae3ea58e2eb470d5d2 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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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: arm64 Version: 2026.03.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 610 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 523138 MD5sum: b0fd791e5c4017e404ec5b7d0eafc5e1 SHA1: 488bfcdbd5c1abc7b02bfc8b8232a22c59b8af53 SHA256: 8c37da56a8fd8cbbcfb3768607aed95022865b7fcdf7d7c4a81341a6e4eaa131 SHA512: 98c5acc7ee59fd1714b0df0105f9d4aaa8194ab9ce1bb737b7e53b53329168686b5844763f450fa73119853367baa9400ef01db1a07b49ad692fe83e6985c006 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. Package: r-cran-profast Architecture: arm64 Version: 1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3409 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-gtools, r-cran-rcpp, r-cran-furrr, r-cran-future, r-cran-ggplot2, r-cran-dr.sc, r-cran-matrix, r-cran-mclust, r-cran-precast, r-cran-pbapply, r-cran-irlba, r-cran-seurat, r-cran-harmony, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-performance, r-cran-nnet, r-bioc-biomart, r-bioc-scater, r-cran-ggrepel, r-cran-rann Filename: pool/dists/resolute/main/r-cran-profast_1.8-1.ca2604.1_arm64.deb Size: 2607028 MD5sum: 3b018eac511dca5862b55c59f1dbbe2c SHA1: c753b68a9961dcf73921da76ef9511034906ca8c SHA256: 9b06a872b8029a687b3327ea175e0a4de9f66f644b61cb5c3d3c968af6908bb6 SHA512: e78ba10650eeeba314584618cddc5ecdfa8fb0dfb0077a26eb991acbb224b9e1257b7080f9c724425ccbe9c363f0999822ce988dc10bd9f7e15c923c8fb9b91a Homepage: https://cran.r-project.org/package=ProFAST Description: CRAN Package 'ProFAST' (Probabilistic Factor Analysis for Spatially-Aware DimensionReduction) Probabilistic factor analysis for spatially-aware dimension reduction across multi-section spatial transcriptomics data with millions of spatial locations. More details can be referred to Wei Liu, et al. (2023) . Package: r-cran-profileglmm Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1358 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1114292 MD5sum: b4067a0f1e3c52f25337d2a1aca810e8 SHA1: 8104a35efd8f43e089b684ef7593071fe718418b SHA256: e34e1c4ea60f9039abd1e06f6466c839600ee506b5cff884d851ab2836b6d554 SHA512: 581be58ce875b6ab41ba5e9fadbafb7e05d61b50deea03e25859e6bb4e958a8428de0cc057a8e03e970b364675e1f5e5ae102beb4c98156d0446cade57ae9bd1 Homepage: https://cran.r-project.org/package=ProfileGLMM Description: CRAN Package 'ProfileGLMM' (Bayesian Profile Regression using Generalised Linear MixedModels) Implements a Bayesian profile regression using a generalized linear mixed model as output model. The package allows for binary (probit mixed model) and continuous (linear mixed model) outcomes and both continuous and categorical clustering variables. The package utilizes 'RcppArmadillo' and 'RcppDist' for high-performance statistical computing in C++. For more details see Amestoy & al. (2025) . Package: r-cran-profoc Architecture: arm64 Version: 1.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2959 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-abind, r-cran-lifecycle, r-cran-generics, r-cran-tibble, r-cran-ggplot2, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-splines2, r-cran-rcpptimer Suggests: r-cran-testthat, r-cran-gamlss.dist, r-cran-knitr, r-cran-rmarkdown, r-cran-dplyr Filename: pool/dists/resolute/main/r-cran-profoc_1.3.4-1.ca2604.1_arm64.deb Size: 1538816 MD5sum: aac20ed89b9e75d924cd938f0951d014 SHA1: 0e0ff594f72017bd46001401f5da14fa18d82b63 SHA256: be0553719f328847a4dc4fd5355a85bcc74e1662d6711baa6640b9b1e30c52ad SHA512: 5e021d917dbba547f34adbb3b0b5e2f89ef58d7c3246dc95d2add527c7caee5e579b40ac12f5786ca3141ec87d6c387774f184281fa883856dc7e2d25a0f287c Homepage: https://cran.r-project.org/package=profoc Description: CRAN Package 'profoc' (Probabilistic Forecast Combination Using CRPS Learning) Combine probabilistic forecasts using CRPS learning algorithms proposed in Berrisch, Ziel (2021) . The package implements multiple online learning algorithms like Bernstein online aggregation; see Wintenberger (2014) . Quantile regression is also implemented for comparison purposes. Model parameters can be tuned automatically with respect to the loss of the forecast combination. Methods like predict(), update(), plot() and print() are available for convenience. This package utilizes the optim C++ library for numeric optimization . Package: r-cran-profvis Architecture: arm64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1035 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 210162 MD5sum: 07e87332f5672aca3069758baf2e2948 SHA1: b6bfa740c01a7092419066ee90a8f0d112bf275b SHA256: 859e438a495cad39356ebb2b718470eda71253d68c57cb9451ff206b4b6ea427 SHA512: 43cffb589d515dfedc8843a3e1d5e29a9fae1ab0eda3325746027185668af60d3f4c4663e3f3ae440f7b7439a08f815bac33be3fc4a680d997d2d7731636158b Homepage: https://cran.r-project.org/package=profvis Description: CRAN Package 'profvis' (Interactive Visualizations for Profiling R Code) Interactive visualizations for profiling R code. Package: r-cran-proj4 Architecture: arm64 Version: 1.0-15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 26412 MD5sum: 2e6b5d4c26f98bf2ba3c03446c014a09 SHA1: 56ce752089f484c4f15870c48f6b238c49749724 SHA256: 8f7a307f24ae35ba7b2ab7d8e0dc349cbe780573f4bb26e31cf4ecd3ab17c0d3 SHA512: a56f3f3aa1bda0f941ce1fb967fa1deb18a6d21024bbcef975a6acef919b3728c616ab3b3e59a9789b08aa494770ba940d69981a9f300d34f2709991c3047cec Homepage: https://cran.r-project.org/package=proj4 Description: CRAN Package 'proj4' (A simple interface to the PROJ.4 cartographic projectionslibrary) A simple interface to lat/long projection and datum transformation of the PROJ.4 cartographic projections library. It allows transformation of geographic coordinates from one projection and/or datum to another. Package: r-cran-proj Architecture: arm64 Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 315 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 134588 MD5sum: f410a698356cb2385e6c4cb5e2d7706b SHA1: 20dfe8dc2f142f3ecd99c5e23854ddd51885995e SHA256: 4966d0780ff0836590e16e874ab314b6791fe2e166acd199e2df63c0051ac827 SHA512: 7c4f9f379bfb35b0e02f69e13d8cb32141f393accd37bcdff27a2441ead0ff5e6517b72cdc34e71f429a6a774d28754e7a04570803f7a7337d5966f82dbef9a4 Homepage: https://cran.r-project.org/package=PROJ Description: CRAN Package 'PROJ' (Generic Coordinate System Transformations Using 'PROJ') A wrapper around the generic coordinate transformation software 'PROJ' that transforms coordinates from one coordinate reference system ('CRS') to another. This includes cartographic projections as well as geodetic transformations. The intention is for this package to be used by user-packages such as 'reproj', and that the older 'PROJ.4' and version 5 pathways be provided by the 'proj4' package. Package: r-cran-projectionbasedclustering Architecture: arm64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 614 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 384484 MD5sum: b82cdb108a986694ccba2fc691ee878a SHA1: 7889eef9810482ef4e58febb44b8622e077123ce SHA256: d5d0a3a98575fba6362c333c0325223ce47db6dd77dce0bab50f1b02b2eb0d39 SHA512: 2a55f1238015e73d34a19ce8bc336431ee19dd12d2b3a56882df93e3a60ebc578d7afe77765e21ff523376543f5197d6db6e6e713afa817178e96bec9c33a31d Homepage: https://cran.r-project.org/package=ProjectionBasedClustering Description: CRAN Package 'ProjectionBasedClustering' (Projection Based Clustering) A clustering approach applicable to every projection method is proposed here. The two-dimensional scatter plot of any projection method can construct a topographic map which displays unapparent data structures by using distance and density information of the data. The generalized U*-matrix renders this visualization in the form of a topographic map, which can be used to automatically define the clusters of high-dimensional data. The whole system is based on Thrun and Ultsch, "Using Projection based Clustering to Find Distance and Density based Clusters in High-Dimensional Data" . Selecting the correct projection method will result in a visualization in which mountains surround each cluster. The number of clusters can be determined by counting valleys on the topographic map. Most projection methods are wrappers for already available methods in R. By contrast, the neighbor retrieval visualizer (NeRV) is based on C++ source code of the 'dredviz' software package, and the Curvilinear Component Analysis (CCA) is translated from 'MATLAB' ('SOM Toolbox' 2.0) to R. Package: r-cran-projpred Architecture: arm64 Version: 2.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1489 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 959436 MD5sum: 7a5afd627a91acd514dc5b4afaa98b80 SHA1: c0cebd0bb952e16039c82ef9eb972d414bf998e8 SHA256: a4b3b299bb54b18b52ab065599f3e6c1e3226b4b06a1ad2636b75caf6a5fc681 SHA512: b4635f86d80276a99192606425366c1b08cf07e9efcb66d8bef20ee3c311b94ba0ddef2fd480fa1a08fe597e4968202c2275d0d315d6e51a1368a76a825f0d25 Homepage: https://cran.r-project.org/package=projpred Description: CRAN Package 'projpred' (Projection Predictive Feature Selection) Performs projection predictive feature selection for generalized linear models (Piironen, Paasiniemi, and Vehtari, 2020, ) with or without multilevel or additive terms (Catalina, Bürkner, and Vehtari, 2022, ), for some ordinal and nominal regression models (Weber, Glass, and Vehtari, 2025, ), and for many other regression models (using the latent projection by Catalina, Bürkner, and Vehtari, 2021, , which can also be applied to most of the former models). The package is compatible with the 'rstanarm' and 'brms' packages, but other reference models can also be used. See the vignettes and the documentation for more information and examples. Package: r-cran-prome Architecture: arm64 Version: 4.0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 213 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bi, r-cran-rstan, r-cran-bridgesampling, r-cran-rcpp, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-posterior Filename: pool/dists/resolute/main/r-cran-prome_4.0.2.5-1.ca2604.1_arm64.deb Size: 104612 MD5sum: 4a08a13a2a512045dcd013589416f84a SHA1: ab3cd072e3771a158490834f314b2f2627eb3f6e SHA256: 6796aa1c25cc100f6431e322250af8b40e60c154fee40298fdd0e7d9d25509a5 SHA512: 26b9d33e59040299cb5d77a96d5e5020c7cfbab484f9f7d934cdf37b2db957e9cf1c57a8a1636f5789dd35f5e544a2c8963f2f8a5ce5a8fd23ce135f19238dfe Homepage: https://cran.r-project.org/package=prome Description: CRAN Package 'prome' (Patient-Reported Outcome Data Analysis with Stan) Estimation for blinding bias in randomized controlled trials with a latent continuous outcome, a binary response depending on treatment and the latent outcome, and a noisy surrogate subject to possibly response-dependent measurement error. Implements EM estimators in R backed by compiled C routines for models with and without the restriction delta0 = 0, and Bayesian Stan wrappers for the same two models. Functions were added for latent outcome models with differential measurement error. Package: r-cran-propagate Architecture: arm64 Version: 1.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 359 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-minpack.lm, r-cran-copula, r-cran-hdf5r, r-cran-crayon Filename: pool/dists/resolute/main/r-cran-propagate_1.1-0-1.ca2604.1_arm64.deb Size: 267168 MD5sum: 76e0a90dad25ef3633e2cc54b8de59fa SHA1: e32232c4a7e71c7ca55dab6fbea6a5dae763c2c0 SHA256: 700d5d66848519ac40bba388b4f8e726cd29181f89b4ded72feb41038e14fe26 SHA512: d779c9064623c46457e68ef52670ac3dc491bfb4561de151d8806911cd864d5a79cde3c10980198e5cedb23cdb7a4cbbc6d5765e289e5abf52bdbcd6a71a0823 Homepage: https://cran.r-project.org/package=propagate Description: CRAN Package 'propagate' (Propagation of Uncertainty) Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo simulation. Calculations of propagated uncertainties are based on matrix calculus including covariance structure according to Arras 1998 (first order), Wang & Iyer 2005 (second order) and BIPM Supplement 1 (Monte Carlo) . Package: r-cran-propclust Architecture: arm64 Version: 1.4-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-fastcluster, r-cran-dynamictreecut Filename: pool/dists/resolute/main/r-cran-propclust_1.4-7-1.ca2604.1_arm64.deb Size: 93200 MD5sum: 7cfa7e972be02405cc18054db5c22d15 SHA1: abd8d0bb5b1a5129f961ff39c45396d147791d20 SHA256: b20bc4b1f50c9a69d40c8df70a34a5983c271a628d26b26c54e9f7a08b05a752 SHA512: b6a3a7ddbf8eb27460f6db93d872b19a7f8bbf08121639e7b99497cac7c4d49f6a3b147abdca63d8905fb47ef7e1550c713b79731bbce45c1094347428cdfcab Homepage: https://cran.r-project.org/package=PropClust Description: CRAN Package 'PropClust' (Propensity Clustering and Decomposition) Implementation of propensity clustering and decomposition as described in Ranola et al. (2013) . Propensity decomposition can be viewed on the one hand as a generalization of the eigenvector-based approximation of correlation networks, and on the other hand as a generalization of random multigraph models and conformity-based decompositions. Package: r-cran-prophet Architecture: arm64 Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1975 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-rlang, r-cran-dplyr, r-cran-dygraphs, r-cran-extradistr, r-cran-ggplot2, r-cran-lubridate, r-cran-rstan, r-cran-rstantools, r-cran-scales, r-cran-stanheaders, r-cran-tidyr, r-cran-xts, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-posterior, r-cran-knitr, r-cran-testthat, r-cran-readr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-prophet_1.1.7-1.ca2604.1_arm64.deb Size: 975050 MD5sum: 09edc08709e658f3bf9f27627de4d32d SHA1: 8c6d85b16f35465d876867a5b43e51769c423cca SHA256: 115b0479e91918f29dd41ac33a0ddcbd409f399515bc30b9491029e358344878 SHA512: 7e84820e14c3b3d71ece178df7826ee5929c4e1faedc00a8ca5ad9e3652f0e3240a32b563a4e7d4fcec939b02e6039d7befc24563caa6e3b816c20e6ec28c7f0 Homepage: https://cran.r-project.org/package=prophet Description: CRAN Package 'prophet' (Automatic Forecasting Procedure) Implements a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well. 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Package: r-cran-protoclust Architecture: arm64 Version: 1.6.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-protoclust_1.6.4-1.ca2604.1_arm64.deb Size: 49804 MD5sum: 6c95ff37c1d6b97b97f78c3a45247d7e SHA1: cba4c655d82b6e320b3b773383165e255e715774 SHA256: 906542e4d44a9c0f751ecf4c643f3f3242e761e593e9985e4b775f29ea10ca92 SHA512: 66007ad917327d0d69c80af5214657fa2a05948f1a86da6edd3680bacc9dd32dc5b9ae6c2bd1ad28ed1704e6fbaede2754495f19c684f30f84b2cdebde66a09e 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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A model of individual life history has to be implemented specifying the individual-level functions that determine the life history, such as development and mortality rates and fecundity. M.A. Kirkilionis, O. Diekmann, B. Lisser, M. Nool, B. Sommeijer & A.M. de Roos (2001) . O.Diekmann, M.Gyllenberg & J.A.J.Metz (2003) . A.M. de Roos (2008) . 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Package: r-cran-pssubpathway Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4778 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-gsva, r-cran-igraph, r-cran-mpmi, r-cran-pheatmap Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pssubpathway_0.1.3-1.ca2604.1_arm64.deb Size: 4623724 MD5sum: 2f7c855d3da2584cce51c03548c75953 SHA1: 03c3d3c832fe800ce8a82d9a3fa817acb29cfd05 SHA256: 1a836b9117a09eb1756060a485a11beee50cf203101021696bf45724761d0e35 SHA512: 97ed56dcb158079da50dac932de4fb304b26eee01487f6361ac9d776d8d7060980f74d2c07ba221ff7c1f139e1102642faf80955dc9881364c6efef05a9870e4 Homepage: https://cran.r-project.org/package=psSubpathway Description: CRAN Package 'psSubpathway' (Flexible Identification of Phenotype-Specific Subpathways) A network-based systems biology tool for flexible identification of phenotype-specific subpathways in the cancer gene expression data with multiple categories (such as multiple subtype or developmental stages of cancer). Subtype Set Enrichment Analysis (SubSEA) and Dynamic Changed Subpathway Analysis (DCSA) are developed to flexible identify subtype specific and dynamic changed subpathways respectively. The operation modes include extraction of subpathways from biological pathways, inference of subpathway activities in the context of gene expression data, identification of subtype specific subpathways with SubSEA, identification of dynamic changed subpathways associated with the cancer developmental stage with DCSA, and visualization of the activities of resulting subpathways by using box plots and heat maps. Its capabilities render the tool could find the specific abnormal subpathways in the cancer dataset with multi-phenotype samples. Package: r-cran-psvd Architecture: arm64 Version: 1.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 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_arm64.deb Size: 33636 MD5sum: dd2222164c230583ca1a5bf09a5c7bcd SHA1: a7e9a68095ca338226783a7c8b478497d7703be2 SHA256: 5b1aca8a41b4c2c9511ba38c7cbec9ee78a43d1c3d6b0674813b5610655b8fde SHA512: c28e6469c68ade715065d67f07f1763bfb92974c50f54eaf23d8c93787ed852875cc681451f75a306d53c1b54de681ee49266d2a24f9a3161163e976ed95dd96 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: arm64 Version: 0.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3947 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_arm64.deb Size: 2139310 MD5sum: 60e8c73a3aeaff20a6e34f70fc86a724 SHA1: 688ffd27ea7707ab6928bcbb43cbf5b88b09b6a0 SHA256: 525c78738ae5ac72e9c04e2eae97998af850a2085842500ba5ef174c36788b15 SHA512: fd1bd8b2001637034404866e1364b9c9197d5a65066f68d958f3ba0ea0625a97028cf48701d921a8b7b45a74c0c00e981c2ca082745d9526a159ddd03cbe12d8 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: arm64 Version: 0.7-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1277 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1075304 MD5sum: 4fe01bc27eecae8a125df6ea0aae08c4 SHA1: cce90f91332e0adaa1c4c7bc9791ee97b4190ba4 SHA256: 82cc7445f604966abb86233a46eb65e8cdd65724b00afb3380c2efe8977e1591 SHA512: a2bc3eea7326e78d8c4e5fdc45dfe43e7b66c1ba488def14780f8b99448c0bbb264b9bed7c6b672737d6e197b171e83da86e0dd744fd96a8d004181b4fe0b5a9 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: arm64 Version: 2.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 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_arm64.deb Size: 156400 MD5sum: 9dbc21b1399bbd3428ad49b40aa57e22 SHA1: 4bb59ddb88f6782622326a7ab1233fc2faea1980 SHA256: 171552ec352c92f378f7ddfe2fef110246ad05c054188bf4c32bdb0dfbf2771a SHA512: ea344ee5ce6ed30f43bc3847806fc71b3df69a90c5c1f982bf7c2241078c87a759b19a7bb80f23dbb27d8af5c71b1c11af03f9514993d53fbb88e022c63799d7 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) . Package: r-cran-ptinpoly Architecture: arm64 Version: 2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 230 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-misc3d Suggests: r-cran-rgl, r-cran-geometry Filename: pool/dists/resolute/main/r-cran-ptinpoly_2.8-1.ca2604.1_arm64.deb Size: 94510 MD5sum: 3302bd91c1e39e70886c581556d6abd9 SHA1: eab3b360fd0c684ad8d4edcf8fb8ea6bffacc441 SHA256: ce1d4a8e95929fadea0e2b08570d5d4a782862925ce75803d038db94be60a9c0 SHA512: 41583154fa7352fce74ace2fbc50528486e140959e603d10429d1d3013c326e9e3992f6521271178e9417148f047a0229f7a1b5ecac809fb3884ac07da5b23f6 Homepage: https://cran.r-project.org/package=ptinpoly Description: CRAN Package 'ptinpoly' (Point-in-Polyhedron Test (2D and 3D)) Function pip3d() tests whether a point in 3D space is within, exactly on, or outside an enclosed surface defined by a triangular mesh. 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. Package: r-cran-ptw Architecture: arm64 Version: 1.9-17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4689 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcppde Filename: pool/dists/resolute/main/r-cran-ptw_1.9-17-1.ca2604.1_arm64.deb Size: 4175098 MD5sum: bb931bd12b16d34f76197aadc55e7424 SHA1: d49c9892c38beee7f78316593a5b874d4b2c75ac SHA256: 1d15f23f06962cb951750554499b16925961135ab413f5898d2fd27a166f0c1e SHA512: 04f68a4e8840995efc372f7f0e884b8f85e3df4560f2c0c7073132845cc66cfdaac0cde30f5fb74c5d967f38695b80be013b3e75b5a8c37836bc9b0752b6181c Homepage: https://cran.r-project.org/package=ptw Description: CRAN Package 'ptw' (Parametric Time Warping) Parametric Time Warping aligns patterns, i.e., it aims to put corresponding features at the same locations. The algorithm searches for an optimal polynomial describing the warping. It is possible to align one sample to a reference, several samples to the same reference, or several samples to several references. One can choose between calculating individual warpings, or one global warping for a set of samples and one reference. Two optimization criteria are implemented: RMS (Root Mean Square error) and WCC (Weighted Cross Correlation). Both warping of peak profiles and of peak lists are supported. A vignette for the latter is contained in the inst/doc directory of the source package - the vignette source can be found on the package github site. See `citation("ptw")` for more details. 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For publication bias, this package implements a variant of the p-value based selection model of Hedges (1992) with discrete selection probabilities. It also implements the mixture of truncated normals model for p-hacking described in Moss and De Bin (2019) . Package: r-cran-pugmm Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-clusterr, r-cran-doparallel, r-cran-foreach, r-cran-igraph, r-cran-manlymix, r-cran-mass, r-cran-matrix, r-cran-mclust, r-cran-mcompanion, r-cran-ppclust, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-pugmm_0.1.2-1.ca2604.1_arm64.deb Size: 281066 MD5sum: 835c51eab71683e5810a0f22f8d3d437 SHA1: 1514d6e8b2f1d3afddbab921c23e5a9c0b4afd35 SHA256: b53ca7cbec65b71bd75ce68dde6be12f3970c6dc47266cb60b9a7539867d0a29 SHA512: aa7195148896b7a9d0faa62f0c3ce932edd729083e32ba273f3da6f249ab10032041fb9fe686b205febe5a797fad80660d810da1fcaa9aa165bab327e4e1e0a7 Homepage: https://cran.r-project.org/package=PUGMM Description: CRAN Package 'PUGMM' (Parsimonious Ultrametric Gaussian Mixture Models) Parsimonious Ultrametric Gaussian Mixture Models via grouped coordinate ascent (equivalent to EM) algorithm characterized by the inspection of hierarchical relationships among variables via parsimonious extended ultrametric covariance structures. The methodologies are described in Cavicchia, Vichi, Zaccaria (2024) , (2022) and (2020) . Package: r-cran-pulasso Architecture: arm64 Version: 3.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1154 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-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-pulasso_3.2.6-1.ca2604.1_arm64.deb Size: 544198 MD5sum: 772a0ad6ac18b50244179c6649cfd7bb SHA1: a3cecddfec8813ff65e4ef8f7873f24d90c39e85 SHA256: 1e726a9aa54f9465a40960baa435889b7acf505b764bde508549695a42d992bc SHA512: 0e18f0f52e08c8f03d9c2938238f11b190a4f302b4ef7cc74bf35f6093467c493a4e539cf5d1ffb7e5f9deeac1ab45e931dcbeab8e9a3650fa515c81b0e04dc4 Homepage: https://cran.r-project.org/package=PUlasso Description: CRAN Package 'PUlasso' (High-Dimensional Variable Selection with Presence-Only Data) Efficient algorithm for solving PU (Positive and Unlabeled) problem in low or high dimensional setting with lasso or group lasso penalty. The algorithm uses Maximization-Minorization and (block) coordinate descent. Sparse calculation and parallel computing are supported for the computational speed-up. See Hyebin Song, Garvesh Raskutti (2018) . Package: r-cran-pullword Architecture: arm64 Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcurl Filename: pool/dists/resolute/main/r-cran-pullword_0.3-1.ca2604.1_arm64.deb Size: 28580 MD5sum: 7fbcfbb8c06e73a0ba00abc75c85f274 SHA1: 3332a00103e0395d66c91727a9ec42b0622f8c76 SHA256: 714ba988914960988dbd7fd5bbd0404061e2d24a462504584cb4598911942b3b SHA512: 9207deb918870f3b98c40e8ccdba75465ab359001c72fcfe92b5ee7341c2525c1d2826b97cd0d58f922dc445be8a52eb8f9c0f30475dd05f6a9bca3ad077efef Homepage: https://cran.r-project.org/package=pullword Description: CRAN Package 'pullword' (R Interface to Pullword Service) R Interface to Pullword Service for natural language processing in Chinese. It enables users to extract valuable words from text by deep learning models. For more details please visit the official site (in Chinese) . Package: r-cran-pumbayes Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 540 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 303864 MD5sum: 01b604a885baab6d89c6ccf3824e7da5 SHA1: e016c98c94779fad25cb073414ef22c56fb298ab SHA256: eb7e082a84bdde42c5fa648a49c6a76f1ee700f1bacd91fca082868916f837a1 SHA512: 463ba2c67f8665a9bce02dd0ab2849819fd21c9779dbf55a93e3e73c6e10f2a561b5000f98bae6b26b22b54f36c312d68cebd38ac28df3bbd4482c69a2bc8695 Homepage: https://cran.r-project.org/package=pumBayes Description: CRAN Package 'pumBayes' (Bayesian Estimation of Probit Unfolding Models for BinaryPreference Data) Bayesian estimation and analysis methods for Probit Unfolding Models (PUMs), a novel class of scaling models designed for binary preference data. These models allow for both monotonic and non-monotonic response functions. The package supports Bayesian inference for both static and dynamic PUMs using Markov chain Monte Carlo (MCMC) algorithms with minimal or no tuning. Key functionalities include posterior sampling, hyperparameter selection, data preprocessing, model fit evaluation, and visualization. The methods are particularly suited to analyzing voting data, such as from the U.S. Congress or Supreme Court, but can also be applied in other contexts where non-monotonic responses are expected. For methodological details, see Shi et al. (2025) . Package: r-cran-puniform Architecture: arm64 Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 304654 MD5sum: 7b03e4195460007cd43f60c90d8ce657 SHA1: 0528ad6f1de3e684a8ce89423f8066b5f9af9696 SHA256: 33d2c48e80feba193e7246af1aa9acdedebb88e16252dbb16fe4d2f6b00e70fc SHA512: 466619c6feaf5c2c57cd6abe7fb2390e306f8c0b6595ef7044d34e6dca81fd11c61e3fd0f7f706d8242fadd509959dc3479e895d97d806c81b48c6732e21f508 Homepage: https://cran.r-project.org/package=puniform Description: CRAN Package 'puniform' (Meta-Analysis Methods Correcting for Publication Bias) Provides meta-analysis methods that correct for publication bias and outcome reporting bias. Four methods and a visual tool are currently included in the package. The p-uniform method as described in van Assen, van Aert, and Wicherts (2015) can be used for estimating the average effect size, testing the null hypothesis of no effect, and testing for publication bias using only the statistically significant effect sizes of primary studies. The second method in the package is the p-uniform* method as described in van Aert and van Assen (2023) . This method is an extension of the p-uniform method that allows for estimation of the average effect size and the between-study variance in a meta-analysis, and uses both the statistically significant and nonsignificant effect sizes. The third method in the package is the hybrid method as described in van Aert and van Assen (2018) . The hybrid method is a meta-analysis method for combining a conventional study and replication/preregistered study while taking into account statistical significance of the conventional study. This method was extended in van Aert (2025) such that it allows for the inclusion of multiple conventional and replication/preregistered studies. The p-uniform and hybrid method are based on the statistical theory that the distribution of p-values is uniform conditional on the population effect size. The fourth method in the package is the Snapshot Bayesian Hybrid Meta-Analysis Method as described in van Aert and van Assen (2018) . This method computes posterior probabilities for four true effect sizes (no, small, medium, and large) based on an original study and replication while taking into account publication bias in the original study. The method can also be used for computing the required sample size of the replication akin to power analysis in null-hypothesis significance testing. The meta-plot is a visual tool for meta-analysis that provides information on the primary studies in the meta-analysis, the results of the meta-analysis, and characteristics of the research on the effect under study (van Assen et al., 2023). Helper functions to apply the Correcting for Outcome Reporting Bias (CORB) method to correct for outcome reporting bias in a meta-analysis (van Aert & Wicherts, 2023). Package: r-cran-pureseqtmr Architecture: arm64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 609 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 395184 MD5sum: 7c454e50c7afa332b3067b4bbd48aaf9 SHA1: c3676c6b9b39f2e28e931c5a48dc4205b6ace1dd SHA256: 64ba5a4283a0335cc0283a347b9e7ba17add8c94e7774ca18ce7f3bd02d351b6 SHA512: 41e711dabde2baddd668dfc4e464ea113b538020588896931abe373969ceb5b60e07b45d74eb144a82a633a2df37e82d07ca6b7e87658ff7c370a4d90897478d 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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In addition, functions to calculate the effective population size and other parameters relevant to population genetics are included. See López-Cortegano E. (2021) . 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Package: r-cran-qch Architecture: arm64 Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 602 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-copula, r-cran-dplyr, r-cran-ks, r-cran-purrr, r-bioc-qvalue, r-cran-rcpp, r-cran-stringr, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-qch_2.1.2-1.ca2604.1_arm64.deb Size: 484134 MD5sum: 4fde1af5b95742557e649153dd91d4e5 SHA1: ef6d798d64876505f60a0ddf3e2f1ec037bee90d SHA256: da6b3fef87f5dcc5c739c9c058a31b3891e6bdfc6beb11cddc1d0b53b897b091 SHA512: 1946430ef614db7c53a36203aefef5dce33e48068ca2a5d5a154b125324c454dd3e8f782c959ac09b5ba7242812a54cdf6c4701d046b80c3a87f3ac898e55367 Homepage: https://cran.r-project.org/package=qch Description: CRAN Package 'qch' (Query Composite Hypotheses) Provides functions for the joint analysis of Q sets of p-values obtained for the same list of items. 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. Package: r-cran-qcluster Architecture: arm64 Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-doparallel, r-cran-foreach, r-cran-iterators Suggests: r-cran-rhpcblasctl, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-qcluster_2.0.0-1.ca2604.1_arm64.deb Size: 254200 MD5sum: e9d6591118ead69d6316479fdbb052e5 SHA1: 75dca7fcbddc1de382bf64b316de454e3497ba17 SHA256: f9ea505a41054f925c5bf330a2826b15e999abdac6c085b383e1d1791735aca9 SHA512: d6203114103f4ab69d056a69d8c3e569a3c2d50344617de62daf65047477d2ce63b8996013fc212b7870568b4dc852a158e0b84537dce289a4660cbf140bac88 Homepage: https://cran.r-project.org/package=qcluster Description: CRAN Package 'qcluster' (Clustering via Quadratic Scoring) Performs tuning of clustering models, methods and algorithms including the problem of determining an appropriate number of clusters. Validation of cluster analysis results is performed via quadratic scoring using resampling methods, as in Coraggio, L. and Coretto, P. (2023) . Package: r-cran-qest Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 531 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pch, r-cran-survival, r-cran-matrixstats Filename: pool/dists/resolute/main/r-cran-qest_1.0.2-1.ca2604.1_arm64.deb Size: 448946 MD5sum: c17b190a00ad2bf11a02b6252e85e88a SHA1: 6681f4a6efba11fcc16a4b23b8efaad7826e1b33 SHA256: daaab16569c070306a44f386d8c31b301752f59c11a96fbf98429c8ee8ec96a2 SHA512: 330853ff10f61adc4f5367d061334bc23c775dcb0c779271977183083362a45049b79b2f3db917defb95d5231734b6ecc6c3f3e471512e15fceee0bb2fe72190 Homepage: https://cran.r-project.org/package=Qest Description: CRAN Package 'Qest' (Quantile-Based Estimator) Quantile-based estimators (Q-estimators) can be used to fit any parametric distribution, using its quantile function. Q-estimators are usually more robust than standard maximum likelihood estimators. The method is described in: Sottile G. and Frumento P. (2022). Robust estimation and regression with parametric quantile functions. . Package: r-cran-qf Architecture: arm64 Version: 0.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 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-rcppgsl Filename: pool/dists/resolute/main/r-cran-qf_0.0.9-1.ca2604.1_arm64.deb Size: 133900 MD5sum: df23a939d72e7205d116f7016c6e5f54 SHA1: 2ee1b7925912b093b4fa3b8c8f368174bc476bbd SHA256: 2cc9a3150d5f2cd58729fa3261c953d2a0a12042ab15ac47e1275694884e6122 SHA512: ef75d544ca6d1d541436a67a7aa80f7153e9a569278aeed4a1f08c343c858d4edbb9ceb0f4d44679f7d7ff682f2fb10954e10940c9d17f1fd2fa823805cc10a2 Homepage: https://cran.r-project.org/package=QF Description: CRAN Package 'QF' (Density, Cumulative and Quantile Functions of Quadratic Forms) The computation of quadratic form (QF) distributions is often not trivial and it requires numerical routines. The package contains functions aimed at evaluating the exact distribution of quadratic forms (QFs) and ratios of QFs. In particular, we propose to evaluate density, quantile and distribution functions of positive definite QFs and ratio of independent positive QFs by means of an algorithm based on the numerical inversion of Mellin transforms. Package: r-cran-qfa Architecture: arm64 Version: 5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 722 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-rhpcblasctl, r-cran-doparallel, r-cran-fields, r-cran-foreach, r-cran-matrix, r-cran-sparsem, r-cran-mgcv, r-cran-nlme, r-cran-quantreg, r-cran-colorramps, r-cran-mass, r-cran-osqp, r-cran-piqp, r-cran-boot Filename: pool/dists/resolute/main/r-cran-qfa_5.0-1.ca2604.1_arm64.deb Size: 628518 MD5sum: f0aebb38f779a848eea7db038844cf4a SHA1: 85aaf432dbd7b36ea41c49cfb22f23f36dc19a8f SHA256: 4e982d331c284ab2b0ca58537d4f1f4e2272f17d2e7d39ef77b3b449903d30a0 SHA512: c740093e0f23c237d524fc81445817f568ee2ddec9f40843ea97c87401612e45688956443869d492af3659e53137a199a06da1240bdf0ed20025bd329a1ed326 Homepage: https://cran.r-project.org/package=qfa Description: CRAN Package 'qfa' (Quantile-Frequency Analysis (QFA) of Time Series and SplineQuantile Regression (SQR)) Implementation of quantile frequency analysis (QFA) for time series based on trigonometric quantile regression and of spline quantile regression (SQR) for estimating the coefficients in linear quantile regression models as smooth functions of the quantile level. References: [1] Li, T.-H. (2012). ''Quantile periodograms,'' J. of the American Statistical Association, 107, 765–776. [2] Li, T.-H. (2014). Time Series with Mixed Spectra, CRC Press. [3] Li, T.-H. (2025). ''Quantile Fourier transform, quantile series, and nonparametric estimation of quantile spectra,'' Communications in Statistics: Simulation and Computation, 1–22. [4] Li, T.-H. (2025). ''Quantile-crossing spectrum and spline autoregression estimation,'' Statistical Inference for Stochastic Processes, 28, 20. [5] Li, T.-H. (2025). ''Spline autoregression method for estimation of quantile spectrum,'' J. of Computational and Graphical Statistics, 1-15. [6] Li, T.-H., and Megiddo, N. (2026). ''Spline quantile regression,'' J. of Statistical Theory and Practice, 20, 30. [7] Li, T.-H. (2026). ''Spline quantile regression with cubic and linear smoothing splines,'' . 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Package: r-cran-qfratio Architecture: arm64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2746 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 1395284 MD5sum: 709e821155b71875b8c36015bccb57ea SHA1: 445cb76d9865766d1076bb85620ff4e00a25fd23 SHA256: 3a540599f507ef03df691716f3fcbfcc6f46827743b9989bd20beaad605f7819 SHA512: 327fefd7be28a0aa6152843560a62af8351fbaa7cc598218ccc8e73102842b3196f5c2bdfb505105bb3e41f13c04a8f8fac34e859d4d07b85210feebe004a7a5 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-qgam Architecture: arm64 Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6072 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mgcv, r-cran-shiny, r-cran-plyr, r-cran-doparallel Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-mass, r-cran-rhpcblasctl, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-qgam_2.0.0-1.ca2604.1_arm64.deb Size: 4027542 MD5sum: 625a7339c95ef202b819078ea78b1004 SHA1: 482937b0c96ae39aab980852085bbc8d85366cce SHA256: 52a1a93f66dca17ac26a01d63c3ff565c59869d64407e9b95271a9605c101a4a SHA512: 623d55bf0d7a0c8b1faddc76fa7c9c814ed6522dec406f86b05008fc5ad244e4b17b50f46ae1b354118f465ef411a3637d2f18a130c19b67bc0109616a042ee4 Homepage: https://cran.r-project.org/package=qgam Description: CRAN Package 'qgam' (Smooth Additive Quantile Regression Models) Smooth additive quantile regression models, fitted using the methods of Fasiolo et al. (2020) . See Fasiolo at al. 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Rohde et al. (2019) . 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See Epskamp et al. (2012) . 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Like generalized estimating equations, this method is also a quasi-likelihood inference method. It has been showed that the method gives consistent estimators of the regression coefficients even if the correlation structure is misspecified, and it is more efficient than GEE when the correlation structure is misspecified. Based on Qu, A., Lindsay, B.G. and Li, B. (2000) . 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Package: r-cran-qshap Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 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-xgboost, r-cran-lightgbm, r-cran-viridislite, r-cran-ggplot2, r-cran-jsonlite, r-cran-progress, r-cran-rcppeigen Suggests: r-cran-shiny Filename: pool/dists/resolute/main/r-cran-qshap_1.0.1-1.ca2604.1_arm64.deb Size: 206900 MD5sum: 0aa02c79f50ba08cec91a6988caeb8eb SHA1: ab179c970da15fe3d8749470e031b5305afb927d SHA256: f240499a6c3628786bc3a4840e0084dc772c413cc5461d833c83354de38216cb SHA512: 575b73d0f7b6ed34d130e02625c47dcfb3d1ad020148831860140451beb4ea6dad208198f02f9ed5eaa86936bee2176063e2c307d2d760c71a98900eb45635df Homepage: https://cran.r-project.org/package=qshap Description: CRAN Package 'qshap' (Fast Calculation of Feature Contributions in Boosting Trees) Computes feature-specific R-squared (R2) contributions for boosting tree models using a Shapley-value-based decomposition of the total R-squared in polynomial time. 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Package: r-cran-qsplines Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1110 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_arm64.deb Size: 425030 MD5sum: 8f4029c8febb440e752d99ad7271dce6 SHA1: f35443619dee70caa34cf534379e32087a6dc149 SHA256: e6de270f187f1d927b21a821b70b8320c3cf444957f57fba153e3f8cfc7987ac SHA512: 69bc1215dd538c0e35d777bb3bf17e8aa4493093b8acc070afbbc823769d3038064478448825fada2204619a62928405c8bc358365e3fa0a83b140b2ae1a640d Homepage: https://cran.r-project.org/package=qsplines Description: CRAN Package 'qsplines' (Quaternions Splines) Provides routines to create some quaternions splines: Barry-Goldman algorithm, De Casteljau algorithm, and Kochanek-Bartels algorithm. The implementations are based on the Python library 'splines'. Quaternions splines allow to construct spherical curves. References: Barry and Goldman , Kochanek and Bartels . Package: r-cran-qsrutils Architecture: arm64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1885 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1645226 MD5sum: 445dfc2d898225b93d9b28cd88c90248 SHA1: 31ac117154bf1448a223539cd46bd90832af49b5 SHA256: 8d9c626a5137cb470a0c1664fe884bb1bd8b729adf0c73180bd2d8dd6faa7f95 SHA512: 85d2405ff96dc3bfc78f307a9e5532a8eca63b8ede56200ed3ce1743c0229024cdfc13c38a8551a4aa8a5938e93281e9f9e2c4d64e89e8594ab4fb48d0aac0cf Homepage: https://cran.r-project.org/package=QsRutils Description: CRAN Package 'QsRutils' (R Functions Useful for Community Ecology) A collection of utility functions for community ecology analyses, with emphasis on workflows using the 'phyloseq' and 'vegan' packages. Includes functions for normalizing OTU tables, computing alpha diversity via rarefaction (using a fast C++ implementation), differential abundance comparisons with compact letter displays, primer checking for amplicon sequencing, plotting QIIME 2/DADA2 generated transition stats and miscellaneous helpers for ordination plots and taxonomic name formatting. Package: r-cran-qtl.gcimapping.gui Architecture: arm64 Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2322 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-shiny, r-cran-mass, r-cran-qtl, r-cran-rcpp, r-cran-openxlsx, r-cran-stringr, r-cran-data.table, r-cran-glmnet, r-cran-doparallel, r-cran-foreach, r-cran-qtl.gcimapping Filename: pool/dists/resolute/main/r-cran-qtl.gcimapping.gui_2.1.1-1.ca2604.1_arm64.deb Size: 1151300 MD5sum: 4c683fca2bea44062e91f6f83797d73f SHA1: 2a89217d9c7255fd0d2fbaf872ce35b71a184cb0 SHA256: bb2e94eaf381eca5869b935b4119347f7d7b5641f2123c3390119d1956117129 SHA512: c8a3fa739f8f549d09c0f4c180e1336727f46aeb3019efd5490422a91de419968e58c0f2c0ff7234015e675ed12ee833434f2889c13503f12fb3065f2be060de Homepage: https://cran.r-project.org/package=QTL.gCIMapping.GUI Description: CRAN Package 'QTL.gCIMapping.GUI' (QTL Genome-Wide Composite Interval Mapping with Graphical UserInterface) Conduct multiple quantitative trait loci (QTL) mapping under the framework of random-QTL-effect linear mixed model. First, each position on the genome is detected in order to obtain a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve are viewed as potential QTL, all the effects of the potential QTL are included in a multi-QTL model, their effects are estimated by empirical Bayes in doubled haploid population or by adaptive lasso in F2 population, and true QTL are identified by likelihood radio test. See Wen et al. (2018) . Package: r-cran-qtl.gcimapping Architecture: arm64 Version: 3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4212 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-openxlsx, r-cran-readxl, r-cran-lars, r-cran-stringr, r-cran-data.table, r-cran-glmnet, r-cran-doparallel, r-cran-foreach, r-cran-mass, r-cran-qtl Filename: pool/dists/resolute/main/r-cran-qtl.gcimapping_3.4-1.ca2604.1_arm64.deb Size: 2097316 MD5sum: ea1f5b848f478f92ca5097924b60b9ca SHA1: 2a3bb45ae342efebdb97e420b073159a9578aa2c SHA256: 948ce1696dad1be74489f3e0c65ab4e895e99152744dedc50b0ddba856f97d8b SHA512: 8c56b0222aaa3ea58e6671b99d3448fd6196d7093c55d700638bad0cccfbb430f7b118c2fdad5b9702ef479186b9b5a5f4d59558df3b0a75c3c0a55bf9f65495 Homepage: https://cran.r-project.org/package=QTL.gCIMapping Description: CRAN Package 'QTL.gCIMapping' (QTL Genome-Wide Composite Interval Mapping) Conduct multiple quantitative trait loci (QTL) and QTL-by-environment interaction (QEI) mapping via ordinary or compressed variance component mixed models with random- or fixed QTL/QEI effects. First, each position on the genome is detected in order to obtain a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve or on each locus curve are viewed as potential main-effect QTLs and QEIs, all their effects are included in a multi-locus model, their effects are estimated by both least angle regression and empirical Bayes (or adaptive lasso) in backcross and F2 populations, and true QTLs and QEIs are identified by likelihood radio test. See Zhou et al. (2022) and Wen et al. (2018) . Package: r-cran-qtl2 Architecture: arm64 Version: 0.40-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6638 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-cran-yaml, r-cran-jsonlite, r-cran-data.table, r-cran-rsqlite, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-devtools, r-cran-roxygen2, r-cran-vdiffr, r-cran-qtl Filename: pool/dists/resolute/main/r-cran-qtl2_0.40-1.ca2604.1_arm64.deb Size: 2505960 MD5sum: 0313d20973ec0a7b6a0cd034994c1833 SHA1: bf8ab1783742b86e486d6a1bea66341a4c890683 SHA256: 6239258387b6c3335ab7aed5fc0f0719545d1d3cbe4a55366622a7609518041b SHA512: 10e3a5b92e119fa9dcbd1df4b3d374e2340b7afda5b64710ce54c76fc267b79c22c74de02d77611861408479eca8622ceab5a70167786ffbd0f9d28fc2ee4845 Homepage: https://cran.r-project.org/package=qtl2 Description: CRAN Package 'qtl2' (Quantitative Trait Locus Mapping in Experimental Crosses) Provides a set of tools to perform quantitative trait locus (QTL) analysis in experimental crosses. It is a reimplementation of the 'R/qtl' package to better handle high-dimensional data and complex cross designs. Broman et al. (2019) . Package: r-cran-qtl2convert Architecture: arm64 Version: 0.32-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-qtl, r-cran-qtl2 Suggests: r-cran-testthat, r-cran-devtools, r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-qtl2convert_0.32-1.ca2604.1_arm64.deb Size: 123932 MD5sum: 9320ed32b2201e84c116dc1125ee915e SHA1: 937250393ab33e70bff94b3260336358eb4b2620 SHA256: f5fa7a0551e560e197acaa6731d73812e0b98f4534c8dad6f53d0ebc547625a9 SHA512: 4c3d9718fb08a12a6156ae4b51926c1f731a1f764430ba8ace83ffb85cc289822ba03a90be6464163fbe83864d53e44034dead4327918f5d4a55ed6528a39f78 Homepage: https://cran.r-project.org/package=qtl2convert Description: CRAN Package 'qtl2convert' (Convert Data among QTL Mapping Packages) Functions to convert data structures among the 'qtl2', 'qtl', and 'DOQTL' packages for mapping quantitative trait loci (QTL). Package: r-cran-qtl2ggplot Architecture: arm64 Version: 1.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4734 Depends: libc6 (>= 2.17), 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-dplyr, r-cran-ggplot2, r-cran-purrr, r-cran-stringr, r-cran-tidyr, r-cran-rlang, r-cran-rcolorbrewer, r-cran-qtl2, r-cran-ggrepel Suggests: r-cran-devtools, r-cran-testthat, r-cran-roxygen2, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-qtl2ggplot_1.2.6-1.ca2604.1_arm64.deb Size: 3363464 MD5sum: f1e3bd1344ef37e5d02b02d66baee97a SHA1: 6085f25a5e24f1abdf91e056be925a823dd5a0a1 SHA256: c8932f17b56b48feb39a85fbc2dc592f7a8e4f505f6a54c67a3c4cb5653d808c SHA512: 7a9c64eb138335e464de53c23cd094f420dc13343a80555c3bb5723c7320ad8a352035b97c7cd3de5409cd49bb9fe3d8511389c23ddd67ee4b9c53a30d2b10ad Homepage: https://cran.r-project.org/package=qtl2ggplot Description: CRAN Package 'qtl2ggplot' (Data Visualization for QTL Experiments) Functions to plot QTL (quantitative trait loci) analysis results and related diagnostics. Part of 'qtl2', an upgrade of the 'qtl' package to better handle high-dimensional data and complex cross designs. Package: r-cran-qtl2pleio Architecture: arm64 Version: 1.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-gemma2, r-cran-ggplot2, r-cran-magrittr, r-cran-mass, r-cran-rcpp, r-cran-rlang, r-cran-tibble, r-cran-rcppeigen Suggests: r-cran-covr, r-cran-mvtnorm, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-broman, r-cran-devtools, r-cran-qtl2, r-cran-parallelly Filename: pool/dists/resolute/main/r-cran-qtl2pleio_1.4.4-1.ca2604.1_arm64.deb Size: 319198 MD5sum: b6b1d6d35bb8fe192b18fcafe32f6f4e SHA1: b4dd80dec5933fa6ccffda58fee13b0ae1c35867 SHA256: c11b4d337207c53f7d5cde1cd8a1f2072b937a989158f2bf00111481da8201bb SHA512: 1d7fecf33dbc0e4e0c6ce9f9ca6394e4d709b6076183f04c5f19b10f18ad10ab785064f710a16139621b5b4966ed8ac19ed8538d8ef9ee6fdcb3cde28ee6b905 Homepage: https://cran.r-project.org/package=qtl2pleio Description: CRAN Package 'qtl2pleio' (Testing Pleiotropy in Multiparental Populations) We implement an adaptation of Jiang & Zeng's (1995) likelihood ratio test for testing the null hypothesis of pleiotropy against the alternative hypothesis, two separate quantitative trait loci. The test differs from that in Jiang & Zeng (1995) and that in Tian et al. (2016) in that our test accommodates multiparental populations. Package: r-cran-qtl Architecture: arm64 Version: 1.74-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10239 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_arm64.deb Size: 5565814 MD5sum: 9f59ce3c5542abb7390c6a027c8aa69e SHA1: 1fdd2b2061876097157094d94894022a5f7a6968 SHA256: 25904c5b9dd27141f89a52ed4a4399b762edfcfe75183a607478b0138bf7c048 SHA512: 7504c04b336e891a615a7f7efcc9c1d7eebeb792b76c48ef28eba404e609a11e348606f351a04521f903124c15f7a4d689a11b8cced3914887eb6247db6b9fe7 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: arm64 Version: 1.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1073 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-gdata, r-cran-lattice Suggests: r-cran-qtl Filename: pool/dists/resolute/main/r-cran-qtlrel_1.15-1.ca2604.1_arm64.deb Size: 981656 MD5sum: b1747c44c01312e988b72f60db841598 SHA1: bd2e5cf781ffcb09b7ee2c9787bd7754627f4ea6 SHA256: 89e91f1e3b65c413ec03b1bd8fadb67e9ec66a5947b83fa00a925284368dbb82 SHA512: e74bfc329a2c14615855bec7e60adb3f9a61f6f9366da47f8b0b042aa0f89ff1ec2875961344d700245d68d21a346070d282dca03d4a634e5794856f4e993770 Homepage: https://cran.r-project.org/package=QTLRel Description: CRAN Package 'QTLRel' (Tools for Mapping of Quantitative Traits of Genetically RelatedIndividuals and Calculating Identity Coefficients fromPedigrees) This software provides tools for quantitative trait mapping in populations such as advanced intercross lines where relatedness among individuals should not be ignored. It can estimate background genetic variance components, impute missing genotypes, simulate genotypes, perform a genome scan for putative quantitative trait loci (QTL), and plot mapping results. It also has functions to calculate identity coefficients from pedigrees, especially suitable for pedigrees that consist of a large number of generations, or estimate identity coefficients from genotypic data in certain circumstances. Package: r-cran-qtools Architecture: arm64 Version: 1.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1148 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-conquer, r-cran-corpcor, r-cran-glmx, r-cran-gtools, r-cran-mass, r-cran-matrix, r-cran-np, r-cran-numderiv, r-cran-quantdr, r-cran-quantreg, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-mice, r-cran-rmarkdown, r-cran-survey Filename: pool/dists/resolute/main/r-cran-qtools_1.6.0-1.ca2604.1_arm64.deb Size: 766478 MD5sum: d6b84a7c78b058b4664f1aa770c965e9 SHA1: 41c6f021888b94e296c55a7eb0ebd290beb47a4f SHA256: 0a499465af831d42c9f4cd7f58ed1386a44243ababecc7f960982c8749f00ed9 SHA512: 5fd2665d7bb9a75da0d4885dc222b4fc9beffc6f5725dd9309240f0e310412c9b04ea7eff57da79adc08ef4651d7ebb2f2f9dd9b1cc412f6bb52e5c620bd1ef5 Homepage: https://cran.r-project.org/package=Qtools Description: CRAN Package 'Qtools' (Utilities for Quantiles) Functions for unconditional and conditional quantiles. These include methods for transformation-based quantile regression, quantile-based measures of location, scale and shape, methods for quantiles of discrete variables, quantile-based multiple imputation, restricted quantile regression, directional quantile classification, and quantile ratio regression. A vignette is given in Geraci (2016, The R Journal) and included in the package. Package: r-cran-quadprog Architecture: arm64 Version: 1.5-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-quadprog_1.5-8-1.ca2604.1_arm64.deb Size: 31238 MD5sum: 0441c3916d86f08d0ebbdaee69b478d9 SHA1: 27a15c8a177dedc41ad0cb52fdfc311e502cf193 SHA256: ed9d11adf43ecbaa673c4923a6157a1b468df660345a4fff1818bcdfd1ed35d6 SHA512: 00b95b0211e0c5c3ca1631566816bbbcb6442e9e7dbfe0cd0ef04d05f935051442924381d268015658d07652c53e1a89a3638e488bef8646cfb7fc704a93bb86 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. Package: r-cran-quadratik Architecture: arm64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1926 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-doparallel, r-cran-foreach, r-cran-ggplot2, r-cran-ggpubr, r-cran-moments, r-cran-mvtnorm, r-cran-rcpp, r-cran-rcppeigen, r-cran-rlecuyer, r-cran-sn, r-cran-rrcov, r-cran-scatterplot3d Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat, r-cran-rgl, r-cran-sphunif, r-cran-circular, r-cran-cluster, r-cran-clusterrepro, r-cran-mclust, r-cran-tinflex, r-cran-movmf Filename: pool/dists/resolute/main/r-cran-quadratik_1.2.0-1.ca2604.1_arm64.deb Size: 869212 MD5sum: 6709db843ec22944cda3abfb38946d4a SHA1: 90f128b118b980760790cc4ed8e5a77345ccc99f SHA256: 679e1bdf778302e31626fd73bc0e8d2143e4aee476e755dbf2bd0a3bcfa1d577 SHA512: 27d9089501d4ddd9d2b1edd4eccefa061a8f587e882f90dc1175e014cf99a2604dbabe4c8c67251fe08e5ad252ce2989e0245bd9d30d9e3a4850f257cf02d5e7 Homepage: https://cran.r-project.org/package=QuadratiK Description: CRAN Package 'QuadratiK' (Collection of Methods Constructed using Kernel-Based QuadraticDistances) It includes test for multivariate normality, test for uniformity on the d-dimensional Sphere, non-parametric two- and k-sample tests, random generation of points from the Poisson kernel-based density and clustering algorithm for spherical data. 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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Package: r-cran-quanda Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1776 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-hdqr, r-cran-proc Filename: pool/dists/resolute/main/r-cran-quanda_1.0.0-1.ca2604.1_arm64.deb Size: 1781510 MD5sum: 7e115b1f4000f981fdeb6e5ad1690b19 SHA1: 8fd140c523bf38f2d81a29e9388a0fefab962cb7 SHA256: 2c0b40dfe8ba437eede85d06d487cd47c8c5cb437fd5b84aa3a284bf04a30391 SHA512: 06402cc8551093ebf992e28a3178650db346bfb8931f3f48e3f31007e042282ad9ab3715c795c5366d457813b4ecaa9dbe5a803a26b15e06a9a94bb1c3ca9600 Homepage: https://cran.r-project.org/package=QuanDA Description: CRAN Package 'QuanDA' (Quantile-Based Discriminant Analysis for High-DimensionalImbalanced Classification) Implements quantile-based discriminant analysis (QuanDA) for imbalanced classification in high-dimensional, low-sample-size settings. 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Provides functionality for corpus management, creating and manipulating tokens and n-grams, exploring keywords in context, forming and manipulating sparse matrices of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and distances, applying content dictionaries, applying supervised and unsupervised machine learning, visually representing text and text analyses, and more. Package: r-cran-quantilepeer Architecture: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2175 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.2), 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-formula.tools, r-cran-matrix, r-cran-mass, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-rcppnumerical Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-partialnetwork Filename: pool/dists/resolute/main/r-cran-quantilepeer_0.0.1-1.ca2604.1_arm64.deb Size: 1002504 MD5sum: 6e43cb2e929b9c6403f248a174be38a9 SHA1: 8b31407a73b09c3f23e6f405729cd26bb2f1c6c1 SHA256: 810d0558ec7c0cca935d959818f5212c884f2104e9e77a9708309390fbd1134a SHA512: eae0fa85f0fe2c9f125b5d6fb3c0ac5abb85d00ad9dc39f971d3c04c38066316579e069a0dabb72f5259264f728c54e2ad4c77952cbcd067ebfb3233585ffd05 Homepage: https://cran.r-project.org/package=QuantilePeer Description: CRAN Package 'QuantilePeer' (Quantile Peer Effect Models) Simulating and estimating peer effect models including the quantile-based specification (Houndetoungan, 2025 ), and the models with Constant Elasticity of Substitution (CES)-based social norm (Boucher et al., 2024 ). 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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. Package: r-cran-quantreg Architecture: arm64 Version: 6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1654 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-sparsem, r-cran-matrix, r-cran-matrixmodels, r-cran-survival, r-cran-mass Suggests: r-cran-interp, r-cran-rgl, r-cran-logspline, r-cran-nor1mix, r-cran-formula, r-cran-zoo, r-cran-r.rsp, r-cran-conquer Filename: pool/dists/resolute/main/r-cran-quantreg_6.1-1.ca2604.1_arm64.deb Size: 1436684 MD5sum: 8566b67a001b483991c1a98d2606b9b5 SHA1: 9a58abf404758e59da99b834c7e18f4f6a091d7a SHA256: 00ef8371bc46c2c656d2263d13c2248b24fa62437d31e64acb4496f0d86d2543 SHA512: a1dea0ca353e66d7734787c38dbfbd0f5f05e22a1764c61ec18c10935d45019a22fa9f5781e1f1186e1bb974a233827b44696ce815c474961b637657d88e8f11 Homepage: https://cran.r-project.org/package=quantreg Description: CRAN Package 'quantreg' (Quantile Regression) Estimation and inference methods for models for conditional quantile functions: Linear and nonlinear parametric and non-parametric (total variation penalized) models for conditional quantiles of a univariate response and several methods for handling censored survival data. Portfolio selection methods based on expected shortfall risk are also now included. See Koenker, R. (2005) Quantile Regression, Cambridge U. Press, and Koenker, R. et al. (2017) Handbook of Quantile Regression, CRC Press, . 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Package: r-cran-r2bayesx Architecture: arm64 Version: 1.1-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2178 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1323626 MD5sum: 829843b9cb2366958f1fb7091439f09c SHA1: 6eb4c88e6419a5825003a1fbe2f89e9ced7e1a64 SHA256: 638c6a5acb21a99ca1d8de7b167c522fdfa9983f7746959323611ddf456ece9d SHA512: 98e20d2d5702f3bf4a17ad52564f49d5941915f6d040a7ab474932676cb3fab0faa972be546b2df76117b4695f38eb3ffd4f6bf9ab20a3e168d21c82789764f5 Homepage: https://cran.r-project.org/package=R2BayesX Description: CRAN Package 'R2BayesX' (Estimate Structured Additive Regression Models with 'BayesX') An R interface to estimate structured additive regression (STAR) models with 'BayesX'. Package: r-cran-r2d2ordinal Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1551 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_arm64.deb Size: 577300 MD5sum: e7256adbfba4d2c5ef48fa12cd961f57 SHA1: e4a2c4751ac0f31ee9070460818963fae952c487 SHA256: 7143c71db5058751884ac34ceff20d696ff617c7fe309d177680702ff2de4f7a SHA512: 58972544598409bf8f0e0268b42efdeabdfaf5edd8dc49719e59645c5a87ef17f3067bedf73d34f395883adb0394b4bcb32ff4824d2128df51c9504e0dd4e496 Homepage: https://cran.r-project.org/package=R2D2ordinal Description: CRAN Package 'R2D2ordinal' (Implements Pseudo-R2D2 Prior for Ordinal Regression) Implements the pseudo-R2D2 prior for ordinal regression from the paper "Pseudo-R2D2 prior for high-dimensional ordinal regression" by Yanchenko (2025) . In particular, it provides code to evaluate the probability distribution function for the cut-points, compute the log-likelihood, calculate the hyper-parameters for the global variance parameter, find the distribution of McFadden's coefficient-of-determination, and fit the model in 'rstan'. Please cite the paper if you use these codes. Package: r-cran-r2pmml Architecture: arm64 Version: 0.31.0-1.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_arm64.deb Size: 4161788 MD5sum: dbe3cc1c05ea73130f6a1468ea26d5fc SHA1: e5872c1baca2e1b037c7cc19258d319acba63c7c SHA256: cf0698986c383dab5fa405e6e162976972385221c53b6f1d711ffdbef1c59605 SHA512: 20b66f3be95c3f561b24a254aae88ce7bf57b161a2c824ff4ab8c26c04a29f1cb8c1d144c6d16e90b9d121e76a9688065d4dff6bb6954519cc1eafb5b0acb098 Homepage: https://cran.r-project.org/package=r2pmml Description: CRAN Package 'r2pmml' (Convert R Models to 'PMML') R wrapper for the 'JPMML-R' library , which converts R models to Predictive Model Markup Language ('PMML'). Package: r-cran-r2sample Architecture: arm64 Version: 4.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 763 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_arm64.deb Size: 345360 MD5sum: b54b6785aa2cfb27638e20800b62bf44 SHA1: 8aecd970de6f4d07b9d55d8490e1e59a3948b104 SHA256: 354ddbee349b08f187057dd2a793719aa474c2113f3f427f359cc7e19dac4788 SHA512: 0b916c4911e0fafcb498613de20c283fa51f4cecffc1598bab728d3fa0a8a9652e857c38d8c2949f6f6956bc865bd10bfec6b270bf717da572019fd41e424d17 Homepage: https://cran.r-project.org/package=R2sample Description: CRAN Package 'R2sample' (Various Methods for the Two Sample Problem) The routine twosample_test() in this package runs the two sample test using various test statistic. The p values are found via permutation or large sample theory. The routine twosample_power() allows the calculation of the power in various cases, and plot_power() draws the corresponding power graphs. The routine run.studies allows a user to quickly study the power of a new method and how it compares to some of the standard ones. Package: r-cran-r2sundials Architecture: arm64 Version: 7.2.1-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1412 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_arm64.deb Size: 322474 MD5sum: 35a7ae39caecc1cadd545764f854eaa1 SHA1: a7edca113c5cb64446da667e1bfc181d476dbc5e SHA256: 77d82883413a245f1c709a33b64cf11c4683129176d53c4e85f0fa337541a7a3 SHA512: fc54d6b152bf21d89fd8c57201a9821bdacc2bd5b68f991c29042d7da287eb63470a548e7846c9bfcb6487c2505664110ddf22eaacee6c07302da371910d5380 Homepage: https://cran.r-project.org/package=r2sundials Description: CRAN Package 'r2sundials' (Wrapper for 'SUNDIALS' Solving ODE and Sensitivity Problem) Wrapper for widely used 'SUNDIALS' software (SUite of Nonlinear and DIfferential/ALgebraic Equation Solvers) and more precisely to its 'CVODES' solver. It is aiming to solve ordinary differential equations (ODE) and optionally pending forward sensitivity problem. The wrapper is made 'R' friendly by allowing to pass custom parameters to user's callback functions. Such functions can be both written in 'R' and in 'C++' ('RcppArmadillo' flavor). In case of 'C++', performance is greatly improved so this option is highly advisable when performance matters. If provided, Jacobian matrix can be calculated either in dense or sparse format. In the latter case 'rmumps' package is used to solve corresponding linear systems. Root finding and pending event management are optional and can be specified as 'R' or 'C++' functions too. This makes them a very flexible tool for controlling the ODE system during the time course simulation. 'SUNDIALS' library was published in Hindmarsh et al. (2005) . Package: r-cran-r3pg Architecture: arm64 Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 815 Depends: libc6 (>= 2.39), 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_arm64.deb Size: 495122 MD5sum: 9fb52be4e6db44567ad1e8ab55453482 SHA1: e39d497f56b14cecf431bf88c99d4161550366f5 SHA256: 41b5f9d766c877879c260c2c700f67b79db03df3982bd6a4b9e6da66a6b37975 SHA512: ba34027ac21c1726ab10c0c9b770aa07a114706921d1659f6c7434e7c615286b751f191f2146d49e8463389ab9819597af8f368d57423b5a795c4c18c92afbaa Homepage: https://cran.r-project.org/package=r3PG Description: CRAN Package 'r3PG' (Simulating Forest Growth using the 3-PG Model) Provides a flexible and easy-to-use interface for the Physiological Processes Predicting Growth (3-PG) model written in Fortran. The r3PG serves as a flexible and easy-to-use interface for the 3-PGpjs (monospecific, evenaged and evergreen forests) described in Landsberg & Waring (1997) and the 3-PGmix (deciduous, uneven-aged or mixed-species forests) described in Forrester & Tang (2016) . Package: r-cran-raceid Architecture: arm64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9770 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_arm64.deb Size: 6193238 MD5sum: 58a3bdae6835b5c0d351f7292ac597b7 SHA1: eb3243a51bb699b677879c94fa48d94ddce129cd SHA256: 2a2558affeda3a232f804326a5c8e342458c6eac3e69ce7a8b8c0d76432ac952 SHA512: 83b211b7910cd1d7fcf320f17739d6adb1936aa008b7ef44fe268efa303ba1aed418ac2026b4df590596e36734ebca150a8df2da144d82046eb9908737233537 Homepage: https://cran.r-project.org/package=RaceID Description: CRAN Package 'RaceID' (Identification of Cell Types, Inference of Lineage Trees, andPrediction of Noise Dynamics from Single-Cell RNA-Seq Data) Application of 'RaceID' allows inference of cell types and prediction of lineage trees by the 'StemID2' algorithm (Herman, J.S., Sagar, Grun D. (2018) ). 'VarID2' is part of this package and allows quantification of biological gene expression noise at single-cell resolution (Rosales-Alvarez, R.E., Rettkowski, J., Herman, J.S., Dumbovic, G., Cabezas-Wallscheid, N., Grun, D. (2023) ). Package: r-cran-raceland Architecture: arm64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2179 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1360724 MD5sum: 23ff6f1e0b0e70e294ba57d54f28ac33 SHA1: 2b7597be3e9444e1a2321a826a8685aba1835b87 SHA256: fab6735700f371d7349e9510f66fcb7a13280401743ce20632df2f3d5c88cb5a SHA512: b14edd143933296d9d54778a70fb5c5c283bdf95be7335e82f26a4e42e7edd0275b8f3758411846573a9cf3414f644fa0d98b0c128d2af3b39eadb44f3ba028a Homepage: https://cran.r-project.org/package=raceland Description: CRAN Package 'raceland' (Pattern-Based Zoneless Method for Analysis and Visualization ofRacial Topography) Implements a computational framework for a pattern-based, zoneless analysis, and visualization of (ethno)racial topography (Dmowska, Stepinski, and Nowosad (2020) ). It is a reimagined approach for analyzing residential segregation and racial diversity based on the concept of 'landscape’ used in the domain of landscape ecology. Package: r-cran-radero Architecture: arm64 Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 157 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_arm64.deb Size: 77580 MD5sum: d9e4629d10963e9d559094e997c2f118 SHA1: 641714592dda1a57742f78bb86a5ff476a79b0a5 SHA256: c5860f2fe2252714d85e41710c1882027707fa2ab62975a7d03f5013e38f7278 SHA512: 4e80c7b7c6fd8b97a82d017675f3440977cf5890d58b7c16aaf89dfd029dbc6b007f56472d65131ecc5c2e3f476307ec8818e2c761136538e74b333f8dd8f6c0 Homepage: https://cran.r-project.org/package=RadEro Description: CRAN Package 'RadEro' (Cs-137 Conversion Model) A straightforward model to estimate soil migration rates across various soil contexts. Based on the compartmental, vertically-resolved, physically-based mass balance model of Soto and Navas (2004) and Soto and Navas (2008) . 'RadEro' provides a user-friendly interface in R, utilizing input data such as 137Cs inventories and parameters directly derived from soil samples (e.g., fine fraction density, effective volume) to accurately capture the 137Cs distribution within the soil profile. The model simulates annual 137Cs fallout, radioactive decay, and vertical diffusion, with the diffusion coefficient calculated from 137Cs reference inventory profiles. Additionally, it allows users to input custom parameters as calibration coefficients. The RadEro user manual and protocol, including detailed instructions on how to format input data and configuration files, can be found at the following link: . Package: r-cran-radviz Architecture: arm64 Version: 0.9.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4288 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-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_arm64.deb Size: 2924604 MD5sum: eb3b0eb4781eefbdb9d24cf0a805f766 SHA1: afbdede3bc60e281f81efae4aeae67d0c5a80a16 SHA256: 50f9e0e4eaa9c20d78f38d093c02b69dec18b2f4ffbedbc2b581103db92326fb SHA512: 736ea69157520dd4bdbd1743a71ed334ec27df0aefd7d81d407b7a709f05e156e001f12a1d59d717ef7dd4c465f416aebdbbf3262f274ce079b5c5f852386774 Homepage: https://cran.r-project.org/package=Radviz Description: CRAN Package 'Radviz' (Project Multidimensional Data in 2D Space) An implementation of the radviz projection in R. It enables the visualization of multidimensional data while maintaining the relation to the original dimensions. This package provides functions to create and plot radviz projections, and a number of summary plots that enable comparison and analysis. For reference see Hoffman *et al.* (1999) () for original implementation, see Di Caro *et al* (2012) (), for the original method for dimensional anchor arrangements, see Demsar *et al.* (2007) () for the original Freeviz implementation. Package: r-cran-ragg Architecture: arm64 Version: 1.5.2-1.ca2604.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2512 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_arm64.deb Size: 555994 MD5sum: 5c272cd6ef82d734f8cc05c0ba9cb8f8 SHA1: 83c4a13f8cbc6c63f7a72e38ea0a4393a4588ec1 SHA256: da75c2d327c4ed3388cb396300de5419ca3f894183328528bc4b26533b21e740 SHA512: 9634cdd4bf4a19442c5ec867a0a946b2708bf1fb4157911346e7aeeeb3917eed26771b1a9939e755c55eb8fc226de0bd91de5de7f600d48fe74a55460b216706 Homepage: https://cran.r-project.org/package=ragg Description: CRAN Package 'ragg' (Graphic Devices Based on AGG) Anti-Grain Geometry (AGG) is a high-quality and high-performance 2D drawing library. The 'ragg' package provides a set of graphic devices based on AGG to use as alternative to the raster devices provided through the 'grDevices' package. Package: r-cran-ragnar Architecture: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3685 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_arm64.deb Size: 3161294 MD5sum: 97da2f10f44a778353a54abbd6d62725 SHA1: 93e877fa418c4974e8deace629cab36edd86e242 SHA256: 2b454ac8d1e85361b497699ada8619f48ca8a2783a397b189101df386190fbd1 SHA512: f7398513a93726cd1f2cebee578b657942d3ad7e51ceb2ab637ec08a192aece5faddee2f68acf1685b5f04daa6105c072c6450741b03059022dd92dfdf9c0d10 Homepage: https://cran.r-project.org/package=ragnar Description: CRAN Package 'ragnar' (Retrieval-Augmented Generation (RAG) Workflows) Provides tools for implementing Retrieval-Augmented Generation (RAG) workflows with Large Language Models (LLM). Includes functions for document processing, text chunking, embedding generation, storage management, and content retrieval. Supports various document types and embedding providers ('Ollama', 'OpenAI'), with 'DuckDB' as the default storage backend. Integrates with the 'ellmer' package to equip chat objects with retrieval capabilities. Designed to offer both sensible defaults and customization options with transparent access to intermediate outputs. For a review of retrieval-augmented generation methods, see Gao et al. (2023) "Retrieval-Augmented Generation for Large Language Models: A Survey" . Package: r-cran-rags2ridges Architecture: arm64 Version: 2.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1485 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1192006 MD5sum: feb963b4efbc9bda83650d42b2b9a579 SHA1: cc117288ef9175c8ebd03efe4bad9ddaca737f7c SHA256: d5e85b40baa8c70ab89e0eb16a3794b740c5bd5ac54dc1b5cae99f02eb469667 SHA512: 67222dfa956971a689688e691779c5f3db4c39bd7686cec045cdb5fcaee964005aafc1198a5aeed91347ad848a9b33d9fef4e7815a733932804bdb007732da73 Homepage: https://cran.r-project.org/package=rags2ridges Description: CRAN Package 'rags2ridges' (Ridge Estimation of Precision Matrices from High-DimensionalData) Proper L2-penalized maximum likelihood estimators for precision matrices and supporting functions to employ these estimators in a graphical modeling setting. For details, see Peeters, Bilgrau, & van Wieringen (2022) and associated publications. Package: r-cran-rainbowr Architecture: arm64 Version: 0.1.38-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1964 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_arm64.deb Size: 1482270 MD5sum: bc188c2e332a854f3c0846bff12b9b60 SHA1: f36dd7e505d6ff1023c2488d5bdafb72a18fefa7 SHA256: f1e03262e10154ba92e9b42416576e99ecad075d9a7cea88ac10f6dcb1fe33a0 SHA512: d4b196415d03217d88766a8cff80ee8759c093eeabd5394ffddaecaa865176cd342d61d64b7510b4c4c513ec9c8c64af5750112d4b91dd20b6298dfde4ea38ac Homepage: https://cran.r-project.org/package=RAINBOWR Description: CRAN Package 'RAINBOWR' (Genome-Wide Association Study with SNP-Set Methods) By using 'RAINBOWR' (Reliable Association INference By Optimizing Weights with R), users can test multiple SNPs (Single Nucleotide Polymorphisms) simultaneously by kernel-based (SNP-set) methods. This package can also be applied to haplotype-based GWAS (Genome-Wide Association Study). Users can test not only additive effects but also dominance and epistatic effects. In detail, please check our paper on PLOS Computational Biology: Kosuke Hamazaki and Hiroyoshi Iwata (2020) . Package: r-cran-ramcmc Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1020 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-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ramcmc_0.1.2-1.ca2604.1_arm64.deb Size: 331056 MD5sum: 4af5dd6cf038f8260ca5420bfe817da2 SHA1: 51bead89ff018043d5e74487f0994e91af21b4c0 SHA256: 5dd77c49fcce7385309b8c849d3609a1a4cfff7c0b246c8bdc73013c6ff7a573 SHA512: 6be40e5632b2f0e3aec04b45be7d7ca6957653aaafe93a41a5955cbea2519419bedf798d5a8a3988448483d3f5d1342e29596a3fa1f208b71aee08e2851e55f7 Homepage: https://cran.r-project.org/package=ramcmc Description: CRAN Package 'ramcmc' (Robust Adaptive Metropolis Algorithm) Function for adapting the shape of the random walk Metropolis proposal as specified by robust adaptive Metropolis algorithm by Vihola (2012) . 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Package: r-cran-randomuniformforest Architecture: arm64 Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2221 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-iterators, r-cran-foreach, r-cran-ggplot2, r-cran-proc, r-cran-cluster, r-cran-mass Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-randomuniformforest_1.1.6-1.ca2604.1_arm64.deb Size: 1816508 MD5sum: 2338a0ef17f906b9c5817d55731c6eaa SHA1: 90f245d9f76dbc1a5cc151f6469cfa0b2a91677a SHA256: e0308912f06cf27f6ecad0a2190c6557151ce029ea24157a5a7bc0133ccf982c SHA512: 2e97778bc2b83429a8ea87bd49e8e0d45a5c7f3b3eb67ea453290eaa52cc15a2d995eabb4265a46a3b610d6889aad6061b3ee7968a302d9631e6a6f2d12c00c7 Homepage: https://cran.r-project.org/package=randomUniformForest Description: CRAN Package 'randomUniformForest' (Random Uniform Forests for Classification, Regression andUnsupervised Learning) Ensemble model, for classification, regression and unsupervised learning, based on a forest of unpruned and randomized binary decision trees. Each tree is grown by sampling, with replacement, a set of variables at each node. Each cut-point is generated randomly, according to the continuous Uniform distribution. For each tree, data are either bootstrapped or subsampled. The unsupervised mode introduces clustering, dimension reduction and variable importance, using a three-layer engine. Random Uniform Forests are mainly aimed to lower correlation between trees (or trees residuals), to provide a deep analysis of variable importance and to allow native distributed and incremental learning. Package: r-cran-randtoolbox Architecture: arm64 Version: 2.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2248 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rngwell Filename: pool/dists/resolute/main/r-cran-randtoolbox_2.0.5-1.ca2604.1_arm64.deb Size: 1282232 MD5sum: a135cd4ff32106efa2aff1648d80f30a SHA1: 648a7f759d835d9f47445bfdb7b35bcfad2b3fa6 SHA256: 45f35e50166d57b4f08bea366deca99e59ea815166fc6c5899e8bd589e16bf40 SHA512: 7d82561610ac25d3945718d54210df11acda0ec825bfa5cc1ca9c6db89f5fc5a8d22597416e3786ade942033ac59db6959eaccd5ac84c9e117457abdede118e1 Homepage: https://cran.r-project.org/package=randtoolbox Description: CRAN Package 'randtoolbox' (Toolbox for Pseudo and Quasi Random Number Generation and RandomGenerator Tests) Provides (1) pseudo random generators - general linear congruential generators, multiple recursive generators and generalized feedback shift register (SF-Mersenne Twister algorithm () and WELL () generators); (2) quasi random generators - the Torus algorithm, the Sobol sequence, the Halton sequence (including the Van der Corput sequence) and (3) some generator tests - the gap test, the serial test, the poker test, see, e.g., Gentle (2003) . Take a look at the Distribution task view of types and tests of random number generators. The package can be provided without the 'rngWELL' dependency on demand. Package in Memoriam of Diethelm and Barbara Wuertz. Package: r-cran-rangebuilder Architecture: arm64 Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1701 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-alphahull, r-cran-stringi, r-cran-sf, r-cran-terra, r-cran-pbapply, r-cran-units, r-cran-rnaturalearth, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-rangebuilder_2.2-1.ca2604.1_arm64.deb Size: 1655758 MD5sum: 222a0cb2e03f66e8423496d8142d4b87 SHA1: 2575f20e49ea6b11b082b655784c2974076c4b01 SHA256: e6ff63aa45515d389d003cbda4b602ca5a064b6e3eff869eeb9d7572a175ceac SHA512: 20f1b379e363e8afe06368bf451ba8c50d765a49eee24eab3842f39aa7311acdecc943cfa455da7bdbeeee23b63fbc3e29cccb9039de21eab6e3280c6938f305 Homepage: https://cran.r-project.org/package=rangeBuilder Description: CRAN Package 'rangeBuilder' (Occurrence Filtering, Geographic Standardization and Generationof Species Range Polygons) Provides tools for filtering occurrence records, generating alpha-hull-derived range polygons and mapping species distributions. Package: r-cran-rangen Architecture: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 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, r-cran-zigg Filename: pool/dists/resolute/main/r-cran-rangen_0.0.1-1.ca2604.1_arm64.deb Size: 110338 MD5sum: ecd5f836c5dae4f1f81a4e3159d31efd SHA1: 55500b3cb47b4f09ffd49e6cea33779ae0bf15a7 SHA256: 53934b2894ddda87201a84aa0f2ad2d2ce1d684f47145079ceb39fea47b10d26 SHA512: 81f4713c91eae197de5cd9ce655ef07a9555426f94ad9b8b2e61b874506da2e51e803ee7b09ddcb799aa2af04a0a5d19adc0b8c9cd01602e0eab87cb05e8e576 Homepage: https://cran.r-project.org/package=rangen Description: CRAN Package 'rangen' (Random Number Generators and Utilities) Provides a collection of random number generators for common and custom distributions, along with utility functions for sampling and simulation. Package: r-cran-ranger Architecture: arm64 Version: 0.18.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 868 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-rcppeigen Suggests: r-cran-survival, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ranger_0.18.0-1.ca2604.1_arm64.deb Size: 402172 MD5sum: 13851f28dcf0227d3939d11c7b2a1bd4 SHA1: 25852516d83c9b82481efdfb33219e6d4368faff SHA256: 302580a3d9c73d75ce43f4215ba68d89ad44ee60aa520a9426ee75bfab326162 SHA512: 5dc593253f59cef7b41bda11c6e877da76a228bfac24b68fece57bfae1db28b247b92358a31d6d96d20a3338bd3e9f549dea22136a3a5a9514ec149c11bb0cb7 Homepage: https://cran.r-project.org/package=ranger Description: CRAN Package 'ranger' (A Fast Implementation of Random Forests) A fast implementation of Random Forests, particularly suited for high dimensional data. Ensembles of classification, regression, survival and probability prediction trees are supported. Data from genome-wide association studies can be analyzed efficiently. In addition to data frames, datasets of class 'gwaa.data' (R package 'GenABEL') and 'dgCMatrix' (R package 'Matrix') can be directly analyzed. Package: r-cran-rankaggreg Architecture: arm64 Version: 0.6.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gtools Suggests: r-cran-xtable, r-cran-kohonen, r-cran-mclust, r-cran-clvalid Filename: pool/dists/resolute/main/r-cran-rankaggreg_0.6.6-1.ca2604.1_arm64.deb Size: 347394 MD5sum: df4ae242b6dbf2b3401a91f6dacedadd SHA1: c6e1257f917e8753741f6ea054685a50d862a50d SHA256: e07d9738af279e447c9e6ceea04be3f01026e030adccdf65d0f66e1bb699e0df SHA512: 6fcd61fc9e2a576f5d880dc2bdaa8b7c37a01cf7ae0013ca4f9e4bf0c25d07fd894bd8fd0355345f51f3efe9c267600c543a72879697ded2be6fe210759d0cc7 Homepage: https://cran.r-project.org/package=RankAggreg Description: CRAN Package 'RankAggreg' (Weighted Rank Aggregation) Performs aggregation of ordered lists based on the ranks using several different algorithms: Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a brute force algorithm (for small problems). Package: r-cran-rankaggsigfur Architecture: arm64 Version: 1.0.0-1.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-rfast, r-cran-combinat, r-cran-data.table, r-cran-plyr Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rankaggsigfur_1.0.0-1.ca2604.1_arm64.deb Size: 107596 MD5sum: 6d11f71dc4c81dab4b07d95cb45623d1 SHA1: 639b64cfbee6908339b0e814b1c570b07c3cc777 SHA256: a06fcc28d32b94fdfd3ef415513ee1798623922b13265523970b0e2b6474f6e5 SHA512: d7eec4a2994c25e47a0b8afbdc5ec339b8f471245ddeba7e9849507606b30d8e3e9b6d0090158dd226885e91df364ef516a2781dd5c2d7d907850ced20b21990 Homepage: https://cran.r-project.org/package=RankAggSIgFUR Description: CRAN Package 'RankAggSIgFUR' (Polynomially Bounded Rank Aggregation under Kemeny's AxiomaticApproach) Polynomially bounded algorithms to aggregate complete rankings under Kemeny's axiomatic framework. 'RankAggSIgFUR' (pronounced as rank-agg-cipher) contains two heuristics algorithms: FUR and SIgFUR. For details, please see Badal and Das (2018) . Package: r-cran-rankcluster Architecture: arm64 Version: 0.98.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 917 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-testthat Filename: pool/dists/resolute/main/r-cran-rankcluster_0.98.0-1.ca2604.1_arm64.deb Size: 564590 MD5sum: e8c23bca6ab55dcc7e1bfa0c8d7f526c SHA1: dbb3cebfd8fe3db849990856bed96c5d074dc0f1 SHA256: 599f875ee435fd9e76b2dbd4e154032f5c43bb7b356d93edd5c08c29fe0ef3d1 SHA512: 795ce21c6ea08b83f1d4f3d3cbb666be4a7328c51b7ffd93dc31816cb08a416b53b62808fe3bea582c7adc7912f43aa9dcf3a36a38f74ec68958fb4351fdd913 Homepage: https://cran.r-project.org/package=Rankcluster Description: CRAN Package 'Rankcluster' (Model-Based Clustering for Multivariate Partial Ranking Data) Implementation of a model-based clustering algorithm for ranking data (C. Biernacki, J. Jacques (2013) ). Multivariate rankings as well as partial rankings are taken into account. This algorithm is based on an extension of the Insertion Sorting Rank (ISR) model for ranking data, which is a meaningful and effective model parametrized by a position parameter (the modal ranking, quoted by mu) and a dispersion parameter (quoted by pi). The heterogeneity of the rank population is modelled by a mixture of ISR, whereas conditional independence assumption is considered for multivariate rankings. Package: r-cran-rankdist Architecture: arm64 Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 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-hash, r-cran-optimx, r-cran-permute Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rankdist_1.1.4-1.ca2604.1_arm64.deb Size: 271268 MD5sum: f44f83803603285c68c212965968dffb SHA1: 84664d55bb9680caeaf0aab25c4bb1c6fb81dd07 SHA256: 1d13d197db3d3f7fb26b5ae71625f519cbe5bb68e73c07ba73d4670e23999e61 SHA512: e574add55484c50fca02183fc9413f4797012b305cdf5bf727c71dd52473976598712248efdc88efe09b8c9cea4d69e527b0f96e67f59e8e735e70d218b9be87 Homepage: https://cran.r-project.org/package=rankdist Description: CRAN Package 'rankdist' (Distance Based Ranking Models) Implements distance based probability models for ranking data. The supported distance metrics include Kendall distance, Spearman distance, Footrule distance, Hamming distance, Weighted-tau distance and Weighted Kendall distance. Phi-component model and mixture models are also supported. Package: r-cran-ranks Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 638 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-graph, r-bioc-rbgl, r-bioc-limma, r-cran-netpreproc, r-cran-perfmeas Suggests: r-cran-bionetdata Filename: pool/dists/resolute/main/r-cran-ranks_1.1-1.ca2604.1_arm64.deb Size: 476080 MD5sum: cdd1ce9623f942519bbf127e07da287e SHA1: ff45794d062668a2b89042e8a7ef4942aa7a645d SHA256: f461c2e2f2c8bec3215439da57dee183103ef727d03b0f4499af0bddf4dd50ff SHA512: cbce0992bea5db9f1870d37db466ca308849e88000d637a755d83a33f6ac3dd5c3498c1eeb91402f98bdc6d18a08c7d900ef6a6ab7d6e3b25a8528d531fedb67 Homepage: https://cran.r-project.org/package=RANKS Description: CRAN Package 'RANKS' (Ranking of Nodes with Kernelized Score Functions) Implementation of Kernelized score functions and other semi-supervised learning algorithms for node label ranking to analyze biomolecular networks. RANKS can be easily applied to a large set of different relevant problems in computational biology, ranging from automatic protein function prediction, to gene disease prioritization and drug repositioning, and more in general to any bioinformatics problem that can be formalized as a node label ranking problem in a graph. The modular nature of the implementation allows to experiment with different score functions and kernels and to easily compare the results with baseline network-based methods such as label propagation and random walk algorithms, as well as to enlarge the algorithmic scheme by adding novel user-defined score functions and kernels. Package: r-cran-ranktreeensemble Architecture: arm64 Version: 0.24-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 811 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 689690 MD5sum: 4f114e8a3a6c346f61d12014e8c42c25 SHA1: 24f11ab8749594ba3dfb8c8a23fd51dd248dc27f SHA256: 75a3f622f1b558bb89f3cad4c26e6270613369095c660b213331f0ab7a57ec15 SHA512: bf957b72c0d3472cc18ae2636ec065d5f73035159fa04abe86940a9f521dc0d55e23c16ab8e8cfffdf698fc138831d4aae959ea5ca1fc2c586929f254ea8e120 Homepage: https://cran.r-project.org/package=ranktreeEnsemble Description: CRAN Package 'ranktreeEnsemble' (Ensemble Models of Rank-Based Trees with Extracted DecisionRules) Fast computing an ensemble of rank-based trees via boosting or random forest on binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. Decision rules can be extracted from trees. Package: r-cran-rann Architecture: arm64 Version: 2.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rann_2.6.2-1.ca2604.1_arm64.deb Size: 42680 MD5sum: b765fe5531de36b9bdc2321d2036a109 SHA1: 8e6284c2d4b6bc2e0764e4bb9a92ff54eb17ae35 SHA256: d9d3018c8edd2f33cb2820516f231fddf8db45bf9c9ffee07761d2a81b43e592 SHA512: f6831b6fca04b0c37cdff67a34ea9958321dd152f45e7de12427508926f6d320aa3ddc3b3bf42fcf93b8b134edc62eb961f9a8bb405832e84233b9fad680f24d 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: arm64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 219188 MD5sum: 60c7e88be613ba3c0b8704fba026d254 SHA1: 9fc8d31a541fdd5376cf35fbb0f0282a1b213ad2 SHA256: 0cb34a6b7a8c50a83dcc01946ee0032c41f624fe9eedb357f794b4a9eb464715 SHA512: 6d6b0a3b862c5886f2344b21fd4aab8e29ecd908a6707eac3b09b89fe696f0dea9b7d01c16266d38af0794aa8b18ee76c0ffd8ac6a7dc7a4aab3552ad177e739 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: arm64 Version: 0.0.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 128 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rapidatetime_0.0.11-1.ca2604.1_arm64.deb Size: 36700 MD5sum: f69598e48ce8ebab78d0ebb4444fb295 SHA1: 274943d9b3084e3edbf9bca8ca67af4610a5d224 SHA256: b44560eb55ce8655c1ca1f73ae689381505190f0ba6bf4cb25ace66f4345b463 SHA512: 2bb924320dce1abaca6770e118e44d8f6196482586a2f81a262df092a4430f8800523278fba89c8759b243d08a9732b8e46a30703298f469abba0ca3c5c101e1 Homepage: https://cran.r-project.org/package=RApiDatetime Description: CRAN Package 'RApiDatetime' (R API for 'Date' and 'Datetime') Access to the C-level R date and 'datetime' code is provided for C-level API use by other packages via registration of native functions. Client packages simply include a single header 'RApiDatetime.h' provided by this package, and also 'import' it. The R Core group is the original author of the code made available with slight modifications by this package. Package: r-cran-rapidfuzz Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 674 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), 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_arm64.deb Size: 249792 MD5sum: 146e012501b7c52eb9f9914d7a36edbe SHA1: a3932a3c2bc9ed7dee634ece38b30e3da685bda9 SHA256: bebd429e4c8a183ecd2e43e3083291756d63daad7604f314130fd307c48a0d1a SHA512: 5647e7b22a0c040bc26107649fafbad03a0fb9c34a0278de39bbc828f54d0cb1b9086af101fa0d339370f4df7c156f7cdfebbd93eb71e56bdc71a54641fef070 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' . This package integrates the 'C++' implementation, allowing 'R' users to access cutting-edge algorithms for fuzzy matching and text analysis. Supported metrics include Levenshtein, Damerau-Levenshtein, Hamming, Jaro, Jaro-Winkler, Longest Common Subsequence (LCS), Optimal String Alignment (OSA), Indel, Prefix, and Postfix distances and similarities, as well as multiple fuzzy matching ratios. Package: r-cran-rapidsplithalf Architecture: arm64 Version: 0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1125 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 767212 MD5sum: f96619c344ae8cfb03962211ea43e9d0 SHA1: 93657e4ef32941fb57efaf3629c0a28577e17d7e SHA256: bf3106896c2ce35bb60a7031afabc2ef6304d8d8c982ec32eaa3093f5c096705 SHA512: 88d50338c574b95bd56f5515b4914168985b8e18274abcaa36c2487e2d357f84e1cbaa6d92d440b3ccf89563d507e0b574de9c0662252f540adb29305821dc4d 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: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 117 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rapiserialize_0.1.4-1.ca2604.1_arm64.deb Size: 16908 MD5sum: fb85213effe5161d379d0ba0256181a4 SHA1: ae6d671a064bce6dea8d6d493c1554d7b32fbb3a SHA256: f516b048e486eedce1cfe9d235b43c1583b4a51bd1c88bd7976870c1c8f5cd53 SHA512: 45bca6f9d245a4b3ac6f0fba45e0cd3ec1a864710057348cdfd3f0eabad11d988c1d7c9690963c19a58d1dfddca4c6c32d4d08ab9132c54bd357e278aedff939 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: arm64 Version: 3.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 512 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_arm64.deb Size: 400906 MD5sum: d18992c2e26a2f49a1363ce135edb998 SHA1: d0f2dec7833d7631fb0addb1aeea5353706bd177 SHA256: cebe94702ae2283479181b2a8170ed620724aed77d25b4089ac6f6e02c145dc1 SHA512: d7313ea14c81b1edc0c4f2a4b5fb5aeefadb1b41463f105d6f1571363b2c3c6c39ed54f7646658fc9bc7a2ef4cb69e3c1531889a6ece3e9912fda269305bf0c6 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: arm64 Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 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_arm64.deb Size: 46558 MD5sum: a1ed730399eda8f0cffc6b2bacee7775 SHA1: 8a844a3dfcbb86c86c37e792662e6390c2b6c401 SHA256: a29a6c90eeafc5d7cb087a7640e8a81f735048bda21a849ad8cbb3880377ecf9 SHA512: 83cd35ce6ceeb3068548c6a8511f36c33c81ba835f6aee121928b31b03a9c0429cec508851605bb90cf246b765ae1d7b96116eb8241f196f1138c0b468fb4e91 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: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7303 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 4845800 MD5sum: 52fe54b2a41d9d4790aa7859a14ff948 SHA1: c560907530b8398d51f553f8982c7bb8f0838293 SHA256: 0af65210a7def59eb1632d06f808e4844de8df584d01a6c53aa24d18bfd13848 SHA512: 1be306dc5fb41e13733c9a946f40b3c1cf4856bc8a2fa66df9c8ef199f23b0473cc3c3edee7d82abf19a09466d4eb3bf3bbe64cbb862f505aa22e288fa444e6d 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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Package: r-cran-raschsampler Architecture: arm64 Version: 0.8-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 204510 MD5sum: a910aebaa9e7e5a363bceb01c1e39988 SHA1: 4e204995145d4c3658f9b87680ee27bb2dff1393 SHA256: b8d198c8a011fafd98da02480939d5fcc6574f8cb40e73cfa719d36f722eef8a SHA512: 3ded2c0efbee9c3bf44b63ea2c2d4e35d4f0ff8e66ed5ef775e0b2b59ae07240a0a8a0e479bbb0c5073915c64f2aae3247ccd7329629f9e85599cdc7c0b2b598 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 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. 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Package: r-cran-ravages Architecture: arm64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5640 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_arm64.deb Size: 4989304 MD5sum: badd86edb5141c6a326a44777e406b5c SHA1: 3cc7b34fdab62b89643a83b8750021b454263f6b SHA256: 02ef7dc73da478034715fc0bf2dd31003c6148aa2e621c4c6e31af497b539f20 SHA512: abf19c4497c801c936127536a38b700241d252568742b8dd1897ff51853153f1ce5f3903c445e0cc77bebb4798559f3d2ccffa1a79cd043f222f88eb8d233b0a Homepage: https://cran.r-project.org/package=Ravages Description: CRAN Package 'Ravages' (Rare Variant Analysis and Genetic Simulations) Rare variant association tests: burden tests (Bocher et al. 2019 ) and the Sequence Kernel Association Test (Bocher et al. 2021 ) in the whole genome using the RAVA-FIRST approach (Bocher et al. 2022 ). Ravages also enables to perform genetic simulations (Bocher et al. 2023 ). Package: r-cran-ravenr Architecture: arm64 Version: 2.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2954 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1476516 MD5sum: d80cc138861845a9e823b6c2305f498e SHA1: c7de9064902ece0c3dddfb82e8739a06e870ee74 SHA256: 80473d63bc7ed8ed1e923ecd506249438d1caf058d99abc7637074ffea7edfc2 SHA512: f8b00c7a2a7456b12549da12154c90800c5238ac227a7b31ef2c8021edd907503ec364dbc2d81b34ab4ce38c4d234b6b1532a29bcfe0652eefe3b38f291f9b05 Homepage: https://cran.r-project.org/package=RavenR Description: CRAN Package 'RavenR' (Raven Hydrological Modelling Framework R Support and Analysis) Utilities for processing input and output files associated with the Raven Hydrological Modelling Framework. Includes various plotting functions, model diagnostics, reading output files into extensible time series format, and support for writing Raven input files. The 'RavenR' package is also archived at Chlumsky et al. (2020) . The Raven Hydrologic Modelling Framework method can be referenced with Craig et al. (2020) . Package: r-cran-raverage Architecture: arm64 Version: 0.5-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 422 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-raverage_0.5-8-1.ca2604.1_arm64.deb Size: 290820 MD5sum: 213b3540a04cf785af93384df3e27b44 SHA1: 0cb243c3ca55d4820bfb9be2ffa352b3f7b5f7a0 SHA256: bb7208483738549d9b410e0434a43338be64fa049b276431658b203878f14b56 SHA512: d149f896bf06904d9cbeb66a9ca31c5a86245f4e8173fa5fbbdee4dc01ec78420602479c6fcd8497cb182dc01d5173b6d1e18dd0b9702ec9b4fb306163086055 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. 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Package: r-cran-rbacon Architecture: arm64 Version: 3.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1717 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_arm64.deb Size: 1088588 MD5sum: db118b32150165b909e2aee34bd044c9 SHA1: b0ede4ca2789dc86fdf889deaa451613a4b2b130 SHA256: ec2e5885800be2077282d4f7fc9c3488e83365ca49818b58681fa93e644fb364 SHA512: 9c3db9c8817ce7f973386e965163df57b565e49a2db4b45239497f1e9f161f7bf3a9adf40b29c6c9c355edb5a7e945d5a460f8cd653239d98f5149caee58b907 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 'rcontroll' R package is a wrapper of 'TROLL'. 'rcontroll' includes functions that generate inputs for simulations and run simulations. Finally, it is possible to analyse the 'TROLL' outputs through tables, figures, and maps taking advantage of other R visualisation packages. 'rcontroll' also offers the possibility to generate a virtual LiDAR point cloud that corresponds to a snapshot of the simulated forest. Package: r-cran-rcpp Architecture: arm64 Version: 1.1.1-1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4837 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_arm64.deb Size: 2049236 MD5sum: d253c52efe95a9925eabb63b80b37042 SHA1: 86f53b98f0af3ebaab6f4a15773361a1e33a24e1 SHA256: bcfcf6e0630d5d54ec7c6a6163b5984842d452af7982c52f3ed25fce230e6f1b SHA512: 99f2e63ddd63537ae1b3b16d4fd6140f8199691206721415f7bb9068c1c58c3c4c7abd10f5df1eeffbaab52202c83d88c30b2f6fd347a01b91e0c43443970d1f Homepage: https://cran.r-project.org/package=Rcpp Description: CRAN Package 'Rcpp' (Seamless R and C++ Integration) The 'Rcpp' package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about 'Rcpp' is provided by several vignettes included in this package, via the 'Rcpp Gallery' site at , the paper by Eddelbuettel and Francois (2011, ), the book by Eddelbuettel (2013, ) and the paper by Eddelbuettel and Balamuta (2018, ); see 'citation("Rcpp")' for details. Package: r-cran-rcppalgos Architecture: arm64 Version: 2.10.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4619 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_arm64.deb Size: 1356222 MD5sum: 7a9750e1b36a84db61385e09fc548ffc SHA1: 7ed76acfd337aa997edd1d43eae9565dcbeaf19c SHA256: e851124f02f44d72f65914e5fe4e0dff3972034cec56ff375a995b7db99ec1bf SHA512: 5cb080e06e8e9b1add322648c09097b0b6e1c37640cfa23eea604c8fdb1b93f8ef64c01e65c2d4485b070de7d2588feffac63f30366153484fe7cdea4acad051 Homepage: https://cran.r-project.org/package=RcppAlgos Description: CRAN Package 'RcppAlgos' (High Performance Tools for Combinatorics and ComputationalMathematics) Provides optimized functions and flexible iterators implemented in C++ for solving problems in combinatorics and computational mathematics. Handles various combinatorial objects including combinations, permutations, integer partitions and compositions, Cartesian products, unordered Cartesian products, and partition of groups. Utilizes the RMatrix class from 'RcppParallel' for thread safety. The combination and permutation functions contain constraint parameters that allow for generation of all results of a vector meeting specific criteria (e.g. finding all combinations such that the sum is between two bounds). Capable of ranking/unranking combinatorial objects efficiently (e.g. retrieve only the nth lexicographical result) which sets up nicely for parallelization as well as random sampling. Gmp support permits exploration where the total number of results is large (e.g. comboSample(10000, 500, n = 4)). Additionally, there are several high performance number theoretic functions that are useful for problems common in computational mathematics. Some of these functions make use of the fast integer division library 'libdivide'. The primeSieve function is based on the segmented sieve of Eratosthenes implementation by Kim Walisch. It is also efficient for large numbers by using the cache friendly improvements originally developed by Tomás Oliveira. Finally, there is a prime counting function that implements Legendre's formula based on the work of Kim Walisch. Package: r-cran-rcppannoy Architecture: arm64 Version: 0.0.23-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1024 Depends: libc6 (>= 2.17), 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-rcppannoy_0.0.23-1.ca2604.1_arm64.deb Size: 262178 MD5sum: 3efa37eeef4de1b9804337cd199ca3de SHA1: 935f0d56b1d197cbbae0c9007f5b7dcf299d309a SHA256: d5b1da0b2637539653a14c0d22adb4a43358fb0563e01d4c15d4370a657f7098 SHA512: 030f4d0fb7d35b01a5c287b57819f2dce2eb840f641d8c7ae001149346d494bd28f4edea247c3dfb0491f942c1d5404bed29820c376e5e3d2b7c3347560d2cad Homepage: https://cran.r-project.org/package=RcppAnnoy Description: CRAN Package 'RcppAnnoy' ('Rcpp' Bindings for 'Annoy', a Library for Approximate NearestNeighbors) 'Annoy' is a small C++ library for Approximate Nearest Neighbors written for efficient memory usage as well an ability to load from / save to disk. This package provides an R interface by relying on the 'Rcpp' package, exposing the same interface as the original Python wrapper to 'Annoy'. See for more on 'Annoy'. 'Annoy' is released under Version 2.0 of the Apache License. Also included is a small Windows port of 'mmap' which is released under the MIT license. Package: r-cran-rcppapt Architecture: arm64 Version: 0.0.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 383 Depends: libapt-pkg7.0 (>= 1.9~), libc6 (>= 2.17), 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_arm64.deb Size: 96682 MD5sum: 2f571eed71565f07bbc25a98c1f4ab79 SHA1: f1d99567730861dec51e1ca590cf5a11766ce40d SHA256: 849ff8e49650978a2566bf74c9a99e960bf7025ffc564c6a49ace1796964d667 SHA512: 8ed1e4788005e4af4b52373a55cb996c7a8d6b06e085b86a4a29501aa30d19e60cfd41137c5966bfa72523a7cd7646b4277a08401d4c18648f7df49ff7529b3c Homepage: https://cran.r-project.org/package=RcppAPT Description: CRAN Package 'RcppAPT' ('Rcpp' Interface to the APT Package Manager) The 'APT Package Management System' provides Debian and Debian-derived Linux systems with a powerful system to resolve package dependencies. This package offers access directly from R. This can only work on a system with a suitable 'libapt-pkg-dev' installation so functionality is curtailed if such a library is not found. Package: r-cran-rcpparmadillo Architecture: arm64 Version: 15.2.6-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6694 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 808226 MD5sum: 57897b1e23b8ed71f6733fcd2f9c011f SHA1: 63465dbb0210b7771c1e35b51c3403baf17ad1a5 SHA256: 30d06cdf123d6e4c9dd1fa81ba73ff0e052a2032e9d3b013bf13d57997ebb529 SHA512: 577ccb145371cf0978c99615bfd69fca1f01f888c1498f877f8f42c1d5947d4f92af199e31af07a42b62f6e26fe0c341f677bf28f4b6d35a2676ae7734cded7e Homepage: https://cran.r-project.org/package=RcppArmadillo Description: CRAN Package 'RcppArmadillo' ('Rcpp' Integration for the 'Armadillo' Templated Linear AlgebraLibrary) 'Armadillo' is a templated C++ linear algebra library aiming towards a good balance between speed and ease of use. It provides high-level syntax and functionality deliberately similar to Matlab. It is useful for algorithm development directly in C++, or quick conversion of research code into production environments. It provides efficient classes for vectors, matrices and cubes where dense and sparse matrices are supported. Integer, floating point and complex numbers are supported. A sophisticated expression evaluator (based on template meta-programming) automatically combines several operations to increase speed and efficiency. Dynamic evaluation automatically chooses optimal code paths based on detected matrix structures. Matrix decompositions are provided through integration with LAPACK, or one of its high performance drop-in replacements (such as 'MKL' or 'OpenBLAS'). It can automatically use 'OpenMP' multi-threading (parallelisation) to speed up computationally expensive operations. The 'RcppArmadillo' package includes the header files from the 'Armadillo' library; users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'. Starting from release 15.0.0, the minimum compilation standard is C++14. Since release 7.800.0, 'Armadillo' is licensed under Apache License 2; previous releases were under licensed as MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that; 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'. Package: r-cran-rcpparray Architecture: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 184 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 44104 MD5sum: 06236a62a7f0f21fca7c08656af54544 SHA1: 50f85dc5855d2ca0a9a8def837b2c400fe9f4725 SHA256: bed2ff486d7a2da94fa39950b08ff3fc06851f4aaddd6a5e60a100a1999e78cc SHA512: 2e7cfcb5684548729257be58441d590ea6faa1130bb2840a5b2b03c1ed54d36097edd597b6853af4705914c97a6ca2c77cf722a3a54059ac688455666e47f52f Homepage: https://cran.r-project.org/package=RcppArray Description: CRAN Package 'RcppArray' ('Rcpp' Meets 'C++' Arrays) Interoperability between 'Rcpp' and the 'C++11' array and tuple types. Linking to this package allows fixed-length 'std::array' objects to be converted to and from equivalent R vectors, and 'std::tuple' objects converted to lists, via the as() and wrap() functions. There is also experimental support for 'std::span' from 'C++20'. Package: r-cran-rcppbdt Architecture: arm64 Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1079 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_arm64.deb Size: 283618 MD5sum: dfaad27af079f32b7e5168a3e598edbc SHA1: 2b5310f9323aea4005615f8b486b559baf75cd3d SHA256: e40ca45170bd258f9c92021474a79ead0536a004d12a3dcc8684b1aaecfbda71 SHA512: 2eaf089853afbe71757e125efb5303bc1226f9865bffc17f3a72efb7a57c5b1410bd3f5554724fe973ea7dcaf71240e5ba193f977090ebbe72e735f878e89492 Homepage: https://cran.r-project.org/package=RcppBDT Description: CRAN Package 'RcppBDT' ('Rcpp' Bindings for the Boost Date_Time Library) Access to Boost Date_Time functionality for dates, durations (both for days and date time objects), time zones, and posix time ('ptime') is provided by using 'Rcpp modules'. The posix time implementation can support high-resolution of up to nano-second precision by using 96 bits (instead of 64 with R) to present a 'ptime' object (but this needs recompilation with a #define set). Package: r-cran-rcppbessel Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 666 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_arm64.deb Size: 132988 MD5sum: 1f811a6ecb88c8c0de9d41029fcaa227 SHA1: 647a0fc229a773f6df319eaa18dec4586f8055d7 SHA256: ff5c7c96659fdc425a3a4e0bc4ac65e3bcdaa2f8ced05082a0a653b90f45bf74 SHA512: 3ec8e5e7353cebfd785f131842ccb67ef517445a09a1bf7aac6b729a586aeffa2623c5afc56f4e3ca5cd30b4502a8bb8d8c71f4b18a67cb77b269b124e7fb2a4 Homepage: https://cran.r-project.org/package=RcppBessel Description: CRAN Package 'RcppBessel' (Bessel Functions Rcpp Interface) Exports an 'Rcpp' interface for the Bessel functions in the 'Bessel' package, which can then be called from the 'C++' code of other packages. For the original 'Fortran' implementation of these functions see Amos (1995) . Package: r-cran-rcppbigintalgos Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 358 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libstdc++6 (>= 11), r-base-core (>= 4.5.0), 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_arm64.deb Size: 122936 MD5sum: 8efcd7e23769a687c8751f2ea6d17c8e SHA1: d07fda26cead126ef78c5023dc369d2af2592205 SHA256: eaff826e013dfda405cda80fa0331174645303eba76b36caeff2195de2fa1b67 SHA512: 3ed7c8b997fb56b1c140e5d876c3cf158e0260fd2be4a62b612eaba1d0aa03b526ccf91ee963a5669b13b3b2f5eda2dfda1ba39c35503f2b88ed3c18d1269466 Homepage: https://cran.r-project.org/package=RcppBigIntAlgos Description: CRAN Package 'RcppBigIntAlgos' (Factor Big Integers with the Parallel Quadratic Sieve) Features the multiple polynomial quadratic sieve (MPQS) algorithm for factoring large integers and a vectorized factoring function that returns the complete factorization of an integer. The MPQS is based off of the seminal work of Carl Pomerance (1984) along with the modification of multiple polynomials introduced by Peter Montgomery and J. Davis as outlined by Robert D. Silverman (1987) . Utilizes the C library GMP (GNU Multiple Precision Arithmetic). For smaller integers, a simple Elliptic Curve algorithm is attempted followed by a constrained version of Pollard's rho algorithm. The Pollard's rho algorithm is the same algorithm used by the factorize function in the 'gmp' package. Package: r-cran-rcppblaze Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 36775 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 6), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1138420 MD5sum: e01b3034fae6a936c3115d238d9ff5ad SHA1: 1ab5173363b4e807cbc6c7c66657b8bc8005aee0 SHA256: 05006bb773575ff8cc1e4feba38da9f00d02926b4262c2c2afae3f634c91414c SHA512: f6173cfb7d32a98bf3b2c2b6366874ff0a4a553c6dfcb149e214ac5d9725f8ece5b5ef38a08fad3410cce29133099eae16dff457942b10495a0f5c0773be0017 Homepage: https://cran.r-project.org/package=RcppBlaze Description: CRAN Package 'RcppBlaze' ('Rcpp' Integration for the 'Blaze' High-Performance 'C++' MathLibrary) Blaze is an open-source, high-performance 'C++' math library for dense and sparse arithmetic. With its state-of-the-art Smart Expression Template implementation Blaze combines the elegance and ease of use of a domain-specific language with HPC-grade performance, making it one of the most intuitive and fastest 'C++' math libraries available. The 'RcppBlaze' package includes the header files from the 'Blaze' library with disabling some functionalities related to link to the thread and system libraries which make 'RcppBlaze' be a header-only library. Therefore, users do not need to install 'Blaze'. Package: r-cran-rcppcctz Architecture: arm64 Version: 0.2.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 396 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 126786 MD5sum: 152565a579b4ed6e5a4169e3369addf9 SHA1: 4027e4ee1802c4653228ad82a2fe7d0a8a21a1bb SHA256: 16581ac945e7ce67a71a89e252f007ffcebd4163cd48cbfcbe5a2a6f7588457f SHA512: 1fc26de980933da85725acf69ab1bffad0fca59a4c423a9d45e816ccf399f2cae372d5bfad865fc54d9d10edd765dcc616bea36bd38599c5de2c6e4fb5f02387 Homepage: https://cran.r-project.org/package=RcppCCTZ Description: CRAN Package 'RcppCCTZ' ('Rcpp' Bindings for the 'CCTZ' Library) 'Rcpp' access to the 'CCTZ' timezone library is provided. 'CCTZ' is a C++ library for translating between absolute and civil times using the rules of a time zone. The 'CCTZ' source code, released under the Apache 2.0 License, is included in this package. See for more details. Package: r-cran-rcppcensspatial Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 680 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_arm64.deb Size: 300950 MD5sum: 55b7c90e0e498da16f6567ddb6f44f99 SHA1: de280b270b9116bf8ed52d9be097de2dd7cc0a97 SHA256: a7051fdf8b1837f3fc6275bcd0efffedbb55f44813da9c328df32a0343064c04 SHA512: 3c94987bd826a0ae554a8d8181c4e82b3e000b1f131d0e533f879115d1b9f1c61261e12e6b5084931016d83985e5c811c7c628cf68bd2853bf5744a7e6aa9e34 Homepage: https://cran.r-project.org/package=RcppCensSpatial Description: CRAN Package 'RcppCensSpatial' (Spatial Estimation and Prediction for Censored/Missing Responses) It provides functions for estimating parameters in linear spatial models with censored or missing responses using the Expectation-Maximization (EM), Stochastic Approximation EM (SAEM), and Monte Carlo EM (MCEM) algorithms. These methods are widely used to obtain maximum likelihood (ML) estimates in the presence of incomplete data. The EM algorithm computes ML estimates when a closed-form expression for the conditional expectation of the complete-data log-likelihood is available. The MCEM algorithm replaces this expectation with a Monte Carlo approximation based on independent simulations of the missing data. In contrast, the SAEM algorithm decomposes the E-step into simulation and stochastic approximation steps, improving computational efficiency in complex settings. In addition, the package provides standard error estimation based on the Louis method. It also includes functionality for spatial prediction at new locations. References used for this package: Galarza, C. E., Matos, L. A., Castro, L. M., & Lachos, V. H. (2022). Moments of the doubly truncated selection elliptical distributions with emphasis on the unified multivariate skew-t distribution. Journal of Multivariate Analysis, 189, 104944 ; Valeriano, K. A., Galarza, C. E., & Matos, L. A. (2023). Moments and random number generation for the truncated elliptical family of distributions. Statistics and Computing, 33(1), 32 . Package: r-cran-rcppclassic Architecture: arm64 Version: 0.9.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 998 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 159954 MD5sum: 81f821b2ad78d22010bb8d376db37a8a SHA1: 4b8fb837b8720c50d3e518dbdf2f0d0e1e835d13 SHA256: aecad001c54f7423786c0f584019dad2b12bde40f195d09a768bc7af537d4fc7 SHA512: fa5b3a27d95332d7479bec53147a94091720427de3cb93a1657462ec1605052aa1aa3b824b69217c7e2d36e40a02fedde01dc529b45677508f7203716fbf6192 Homepage: https://cran.r-project.org/package=RcppClassic Description: CRAN Package 'RcppClassic' (Deprecated 'classic' 'Rcpp' 'API') The 'RcppClassic' package provides a deprecated C++ library which facilitates the integration of R and C++. New projects should use the new 'Rcpp' 'API' in the 'Rcpp' package. Package: r-cran-rcppclassicexamples Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 281 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 108354 MD5sum: 8188b395ccdb84a4630c4a9704cad8ef SHA1: e1e3e7d55496a85d3bac1ec48f92360b55daa22b SHA256: 501ef560a6a1cb3c7cc604d58f6418550c7dc1732127bf413cb759a1a74552c2 SHA512: ae7eaebbddd122e2c130813025ca1a13e0cb374bdea5abf32402bc6da2a267e44d8c0fef5bed2a5d81f16585fc7528f5e2206b1e340b7073bee1df8884446ce6 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: arm64 Version: 0.2.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 364 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_arm64.deb Size: 170132 MD5sum: 077cd28d422997eb404f55a940179d8b SHA1: 9feeff9574f11179ab29eb5316c06bf82836fef8 SHA256: 8c5110f80d1ab41573b19f947d9f211ef1379e2cfa26029ea07b20305226641f SHA512: 7285aa8ca562808646d4392b4ec31fd1201f011431fce8368622cd6c08467ae2b3001f1426439e632fc6ce30e5c13aa651fee497ca7362338206bedc500cd914 Homepage: https://cran.r-project.org/package=RcppCNPy Description: CRAN Package 'RcppCNPy' (Read-Write Support for 'NumPy' Files via 'Rcpp') The 'cnpy' library written by Carl Rogers provides read and write facilities for files created with (or for) the 'NumPy' extension for 'Python'. Vectors and matrices of numeric types can be read or written to and from files as well as compressed files. Support for integer files is available if the package has been built with as C++11 which should be the default on all platforms since the release of R 3.3.0. Package: r-cran-rcppcolmetric Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 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_arm64.deb Size: 90902 MD5sum: a31985929abdfb2885ff066098b0218f SHA1: 853be21d198b3d4fa0a9d3e79ae699b19369804d SHA256: 6d158fd301bcfe1028fa6f574a15a15d0613c8a0b21f39c32145fd2346d4f908 SHA512: 19068490d36c89576011bd607f03dc814cf67fc0707e79dcd33079d1776f3a61093c65e50b976cb2b403e000c434498c3cd049c08c8f5add751b1262731f6e1c Homepage: https://cran.r-project.org/package=RcppColMetric Description: CRAN Package 'RcppColMetric' (Efficient Column-Wise Metric Computation Against Common Vector) In data science, it is a common practice to compute a series of columns (e.g. features) against a common response vector. Various metrics are provided with efficient computation implemented with 'Rcpp'. Package: r-cran-rcppcolors Architecture: arm64 Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 589 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 400636 MD5sum: 0e88b8af3e7b2024a665c02628801035 SHA1: b974447a41b1dd02718758086c46a7558c0632ca SHA256: eaa489979e9ae30373002879292c2758b8c9141b6bfbfae7a21c583695b353fd SHA512: b203e1a87b6359201679abebbbecf6e7aa807092bda2a7ecad1f59bc960a5822468081ee5928b03d368e70c09bee0473dff471a78af53b51ff064c486f5088db Homepage: https://cran.r-project.org/package=RcppColors Description: CRAN Package 'RcppColors' (Color Mappings and 'C++' Header Files for Color Conversion) Provides 'C++' header files to deal with color conversion from some color spaces to hexadecimal with 'Rcpp', and exports some color mapping functions for usage in R. Also exports functions to convert colors from the 'HSLuv' color space for usage in R. 'HSLuv' is a human-friendly alternative to HSL. Package: r-cran-rcppcwb Architecture: arm64 Version: 0.6.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2163 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libglib2.0-0t64 (>= 2.14.0), libpcre2-8-0 (>= 10.22), libstdc++6 (>= 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_arm64.deb Size: 749824 MD5sum: d1764efd022debb803d8466e1d545eeb SHA1: a6794f3549b97c827c8056682712b272352746fc SHA256: c66dd47cbed221bcfaede8a08eb2a69a7166951d7ecadbc991d0d38b41d8e011 SHA512: b7961721cd84d9d990f4eee5f19dc711fb748051bff67bfb5261ed8a4773833dc30d0b7ef3b270a741aab4a28bf0375ea5671d60583432ffe78f9fd29341db5d Homepage: https://cran.r-project.org/package=RcppCWB Description: CRAN Package 'RcppCWB' ('Rcpp' Bindings for the 'Corpus Workbench' ('CWB')) 'Rcpp' Bindings for the C code of the 'Corpus Workbench' ('CWB'), an indexing and query engine to efficiently analyze large corpora (). 'RcppCWB' is licensed under the GNU GPL-3, in line with the GPL-3 license of the 'CWB' (). The 'CWB' relies on 'pcre2' (BSD license, see ) and 'GLib' (LGPL license, see ). See the file LICENSE.note for further information. The package includes modified code of the 'rcqp' package (GPL-2, see ). The original work of the authors of the 'rcqp' package is acknowledged with great respect, and they are listed as authors of this package. To achieve cross-platform portability (including Windows), using 'Rcpp' for wrapper code is the approach used by 'RcppCWB'. Package: r-cran-rcppde Architecture: arm64 Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 307870 MD5sum: 94a1c0d5cd9d87bcca7fdbabca2bb8ef SHA1: 046f8e39387ba01408860d3df51ab4a3b06abeac SHA256: 797c4a1e22fd24aac92b1e4ca92e466be64af9e4716e8eee6d0e4af6ca61331e SHA512: eee813ed9fdee9b572c7ec8b90225b758dac4652817faf3a9189086140d35c254e31633bfaaa2a6022f36e95247bd30ae97f26d2d6d7caa191fc58c5007f42fc Homepage: https://cran.r-project.org/package=RcppDE Description: CRAN Package 'RcppDE' (Global Optimization by Differential Evolution in C++) An efficient C++ based implementation of the 'DEoptim' function which performs global optimization by differential evolution. Its creation was motivated by trying to see if the old approximation "easier, shorter, faster: pick any two" could in fact be extended to achieving all three goals while moving the code from plain old C to modern C++. The initial version did in fact do so, but a good part of the gain was due to an implicit code review which eliminated a few inefficiencies which have since been eliminated in 'DEoptim'. Package: r-cran-rcppdist Architecture: arm64 Version: 0.1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 535 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 199906 MD5sum: a602e5d605d57cab8937e34b81b14ff1 SHA1: 0082ba610e8b130db6ad09cbc4fc4ed4adcdb5c8 SHA256: 72d1d956b979f7112bb15b623e10b0838f5a72c4af01b3e4ce5115626d8ac01d SHA512: 44b84ff6f98bc902fa6f42859a92cb1a6657759773fd5ea0158e1087650525cb922a5962a170295c11fdc89da06bd6e8ec4fad2a6bbea47e85f452b614163bfc Homepage: https://cran.r-project.org/package=RcppDist Description: CRAN Package 'RcppDist' ('Rcpp' Integration of Additional Probability Distributions) The 'Rcpp' package provides a C++ library to make it easier to use C++ with R. R and 'Rcpp' provide functions for a variety of statistical distributions. Several R packages make functions available to R for additional statistical distributions. However, to access these functions from C++ code, a costly call to the R functions must be made. 'RcppDist' provides a header-only C++ library with functions for additional statistical distributions that can be called from C++ when writing code using 'Rcpp' or 'RcppArmadillo'. Functions are available that return a 'NumericVector' as well as doubles, and for multivariate or matrix distributions, 'Armadillo' vectors and matrices. 'RcppDist' provides functions for the following distributions: the four parameter beta distribution; the location- scale t distribution; the truncated normal distribution; the truncated t distribution; a truncated location-scale t distribution; the triangle distribution; the multivariate normal distribution*; the multivariate t distribution*; the Wishart distribution*; and the inverse Wishart distribution*. Distributions marked with an asterisk rely on 'RcppArmadillo'. Package: r-cran-rcppdpr Architecture: arm64 Version: 0.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3001 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_arm64.deb Size: 2498606 MD5sum: f0bfbfdf31afd4c9028d10a86fc0b41a SHA1: cdf3e239a23c68703920b898f07c1e668b285679 SHA256: 3442d52ea7b58f3ccdf478835593d5ab14e2c0e18b40a4aabffe5d836f1fada2 SHA512: 4a17c03a5c083b0775b8f4f3a51631d83e0b050b5036997948529443894ae3b3383a015432d19320b2775da736eca5f9c70b434447f3c84df82d6c8f41ae332e 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: arm64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 949 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_arm64.deb Size: 521226 MD5sum: 8fa1a134fa310cb25e6863937b644e74 SHA1: 97e5f168f620645f8bde97d7e8809eaf91b84416 SHA256: 1bcb806b256ac42c7ad978d407979cefc6f35b13e3d319c24d55ced4ba3e4cca SHA512: 692a430b7da840e14742a3da73e4b9da90731df5672572eaf24e58d8048f165c34d6cb472353b16de54ce45be90ab0ca7a81dca36a68f24ccfffbecd3f9e0d49 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: arm64 Version: 0.3.4.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9661 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1413924 MD5sum: 0aacec86b8a859775421106388be3465 SHA1: 40425a9ca7a2d5feadad970903621ec7cb8e413e SHA256: db83d1e3806683b4f0a7fce06c1a5df27b737038a2a8b98fb6894aa29645b9dd SHA512: c0cc82e26c9d6ed653fbb50de7b4912e67661370245c16205d3149f2364fcb82198fb2f37a8f726452d92e93ece738a5e7180112d778a17f5c4722b12cb966dd 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: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4006 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 504368 MD5sum: 7899914432b8563f1f13efccc359fc4d SHA1: 4f46a71ed31ab8fb660753ff69148cf581a83b8a SHA256: 91913ac597c5b62bd2064e8c4795b518f70f57b20f236b03a8882eda45de1db3 SHA512: d5ede22127a452c06cd3276eb6da4c4679d1fe730ed25761dc8f6c2e1ab539e7a54ced097998cf447fae371d39c3594f439445877884947bb8acaba79bab9285 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: arm64 Version: 0.3.10.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2119 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_arm64.deb Size: 252084 MD5sum: 1745ad337d9338bdf536f10fa36eccf8 SHA1: dc738080ac4747b9e026dfbd6d20bbfebfabf937 SHA256: e10b405b589d4d2e769c818cf9056152606d914aeff51b86498874c45952d450 SHA512: c20b0a98950419f83bcebd6bf6180a34f09d235cc3e6a4aecbc1981420395076b681a81fc90a73600d0380b38809a02a868b89b148a3d0e3b529ca0dc1e0ddb0 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: arm64 Version: 0.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 266 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 93586 MD5sum: 602d55eccf95fd7fe4f9fe00f52b73cf SHA1: 402169c353a3af739a390d9c84219fd5336c5c28 SHA256: d360704fabc6351972182bfa4b91af600380054e2827c321a617b87932b8844c SHA512: a714a2e6bcad7d4a5281baa0f95cb89382602403c7fcc3b9fabbdb520cc6ff79bee3afdaff41192696c0b6d82a628c3fb3b703c94665eaa0931c54612a54aa54 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: arm64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 39856 MD5sum: daa7e150bea119ff30fdc8f456928772 SHA1: 9ed7ee9084f9de7d26ca1e09f0ec5da1ecb81813 SHA256: da17246ad4ba22cfc669d64db3d8c262b8caa741510300b49a8f796fd1acc7c5 SHA512: f1d92d2658151b8c01136a4113beeb1d74256e4fbf8660466c1d88516d8d86954c8ea95d308022755d90ca067cc71391cebfe3974e25a7370586a64432c02bc6 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: arm64 Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 501 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_arm64.deb Size: 108000 MD5sum: 076fc1fb6fa82b3541e4c71876e2a154 SHA1: b3a24661774409d290e50b2c52e57574f3703347 SHA256: 4b0c6a397bdc71ed6f04d66fe4614cda7643937bd00b7274df3c1b994201a2a5 SHA512: 2d066c3ad3b71b44be8b1a2fe3d5fd33cfcb2d80b3ad45eb9475c66e528082bd7bb3e66ea460244fe392ba7929d81fd364014678a2ae2d303e63f4dfa9138380 Homepage: https://cran.r-project.org/package=RcppFastAD Description: CRAN Package 'RcppFastAD' ('Rcpp' Bindings to 'FastAD' Auto-Differentiation) The header-only 'C++' template library 'FastAD' for automatic differentiation is provided by this package, along with a few illustrative examples that can all be called from R. Package: r-cran-rcppfastfloat Architecture: arm64 Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 103296 MD5sum: 400e7c3d2297274c4393f240643c205e SHA1: 15bcce6f81d39f6efddc4ea81b06a56cea81a455 SHA256: 70165976521dc1cdf6da10fe8ee00d716867f9cd913a66f212a7219089a802de SHA512: f9faebf39f11552962aaec986d5fe53d538deec98078fe65b9a780eac24d8b2ca90ca288a3444520f378e0969c46afd6bfdc032dc8ccd5e2d0bbaaa65d054aee 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: arm64 Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 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_arm64.deb Size: 48214 MD5sum: 3749971e5e76cfe5018253ef94716d66 SHA1: f6d441db5e59fceda9afe1484e23ef2205d2fbdd SHA256: c1e7c5aae84e351d9c1448362d3582a6aa3f4e8770f68838c8818743ca2b53b3 SHA512: 3cff10d38a27687e49800a725eb31490fde229f07c669388d4917893d12c8aed062054df684068f02f4685bafd35d2a60d0b756be1bb0da6062958726568ccec 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: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 47582 MD5sum: eb2fd5723d8d606d1394238a25ff7284 SHA1: 1d7e4f361e27455f7d6ae3b3ba4ac67ceddc83dc SHA256: 8fcde4293e19952b8d8990ea21d5a3802747485fbc34e5caf5ca3766d4d17aeb SHA512: 86cfe842cd8b9170ae15e11f844482814f0a0ae9100449af3052add41a8fded36e2452751f260b19c5fcb3784834cf26b81929f38d97561d1680a9a1633b050d Homepage: https://cran.r-project.org/package=RcppGreedySetCover Description: CRAN Package 'RcppGreedySetCover' (Greedy Set Cover) A fast implementation of the greedy algorithm for the set cover problem using 'Rcpp'. Package: r-cran-rcppgsl Architecture: arm64 Version: 0.3.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 694 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 371806 MD5sum: ea0d9424e11c656d18d69b35422e7cc1 SHA1: c7c41380adf797c8b3239652da024f98ae3d09a3 SHA256: 536d8a0f462e676f47a7a0cfc6fe250b272e0b4f3317c0004ff63a3d767a5ef1 SHA512: bb3a95816685a87d9c49963328b86a75182d549f06f9fcd4b8b37a1329ee4bc6fce8d4226e9c3e6d41ed8eee9c840333b972b57a12ca04c4bcc021c72a908b39 Homepage: https://cran.r-project.org/package=RcppGSL Description: CRAN Package 'RcppGSL' ('Rcpp' Integration for 'GNU GSL' Vectors and Matrices) 'Rcpp' integration for 'GNU GSL' vectors and matrices The 'GNU Scientific Library' (or 'GSL') is a collection of numerical routines for scientific computing. It is particularly useful for C and C++ programs as it provides a standard C interface to a wide range of mathematical routines. There are over 1000 functions in total with an extensive test suite. The 'RcppGSL' package provides an easy-to-use interface between 'GSL' data structures and R using concepts from 'Rcpp' which is itself a package that eases the interfaces between R and C++. This package also serves as a prime example of how to build a package that uses 'Rcpp' to connect to another third-party library. The 'autoconf' script, 'inline' plugin and example package can all be used as a stanza to write a similar package against another library. Package: r-cran-rcpphmm Architecture: arm64 Version: 1.2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 545 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 220284 MD5sum: 4e379d01f79b011bfcdca8f0a65622d1 SHA1: 96116c6f2bc400a1ad05b38117e901198d90d5a4 SHA256: 838db4872a594dcb067b60b0ab941012c99b76522062d7c2035d49d0fb9da8e8 SHA512: 5f386006ebd9f3bd337aa172a962fcee56567c20a4751999d7aa05bc0b5a0928038e8c02f235e9b2d577e73254c1567822cf28b2151eb4ad9a57b4f37e79da05 Homepage: https://cran.r-project.org/package=RcppHMM Description: CRAN Package 'RcppHMM' (Rcpp Hidden Markov Model) Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. The observed values can be modeled by a multinomial distribution for categorical/labeled emissions, a mixture of Gaussians for continuous data and also a mixture of Poissons for discrete values. It includes functions for random initialization, simulation, backward or forward sequence evaluation, Viterbi or forward-backward decoding and parameter estimation using an Expectation-Maximization approach. Package: r-cran-rcpphnsw Architecture: arm64 Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 763 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_arm64.deb Size: 181282 MD5sum: 0419f83b46877a08c5d26cf5c5be9311 SHA1: 919dfcc4b6fc17935f5351aefe25a27f6c4c475e SHA256: 6df1e9f13be0d76302760f0e058fe0f82075b6063bf632a4c9ce732a25964467 SHA512: 0beba4d31dcadc4d1748464f95a931e72a61f32087c9ba79b611beed24afe882e1aa5fceb0db0317787812311ceda4fb1711f41299fb1dd2c1a4ad1fc62e114e Homepage: https://cran.r-project.org/package=RcppHNSW Description: CRAN Package 'RcppHNSW' ('Rcpp' Bindings for 'hnswlib', a Library for Approximate NearestNeighbors) 'Hnswlib' is a C++ library for Approximate Nearest Neighbors. This package provides a minimal R interface by relying on the 'Rcpp' package. See for more on 'hnswlib'. 'hnswlib' is released under Version 2.0 of the Apache License. Package: r-cran-rcpphungarian Architecture: arm64 Version: 0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 360 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 140834 MD5sum: da34a8084be24d6c4f6afacb98e628c5 SHA1: 8852ee3b126be8f41781c49257d24aeddfeccffc SHA256: 649f7e373918a408196981fa37df8c1e47fd59bcd31910a9fe8fea0d027206d5 SHA512: 06681eb3fd2169ef5aa1ae43ce326a229f6960c9a4741ba78ef5ce33bda023f0372990f811ce46181ac4f29ebb17f03e71c73d57c403213e867aab5d880facf9 Homepage: https://cran.r-project.org/package=RcppHungarian Description: CRAN Package 'RcppHungarian' (Solves Minimum Cost Bipartite Matching Problems) Header library and R functions to solve minimum cost bipartite matching problem using Huhn-Munkres algorithm (Hungarian algorithm; ; Kuhn (1955) ). This is a repackaging of code written by Cong Ma in the GitHub repo . Package: r-cran-rcppint64 Architecture: arm64 Version: 0.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 217 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 46966 MD5sum: 02ea4fa0ae1f125e83816c3972ec0dd8 SHA1: 93438d5f7d0f1cbc1321591abf5bb084a6b42210 SHA256: 88b93e750600ce036e924dcfa0b43af54d2661cb18c1f6af5b0864413b9934ca SHA512: 6dec7e231e15923dead174bf26e5abab9f863df1582cec71ba9117726ccfad194f050f11f22c411c8f11684a4623826a4157838ab7372f23f98f1d1e9d837662 Homepage: https://cran.r-project.org/package=RcppInt64 Description: CRAN Package 'RcppInt64' ('Rcpp'-Based Helper Functions to Pass 'Int64' and 'nanotime'Values Between 'R' and 'C++') 'Int64' values can be created and accessed via the 'bit64' package and its 'integer64' class which package the 'int64' representation cleverly into a 'double'. 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Package: r-cran-rcppjagger Architecture: arm64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 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_arm64.deb Size: 101112 MD5sum: 207b9f2db4b5cdb103f7b0e0c650bd9c SHA1: b1e48dab5d474bc4ffcc8f0d6ecd680029d3a385 SHA256: f928a2b938146dcf157b3e3ab004222536d0a3ba230449c54e41f7f297a6dd2e SHA512: 96ffa33450e92edefc19b8b8960a4f93dcaa3f2f8a20c443c9e396de8cfecc91e46a2c2b33bc65bad54dc3f664b6fa5b900523c29f012d8a14c6a344305e272d 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: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 265 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 48592 MD5sum: 7e46582f722b52568e29ecf549d95d3a SHA1: d31f735d275ed6934689d499ee578edff05068d2 SHA256: 27885f64293c43a2abf08c430cbdaa010fcde0503aabcd64c13084432ec99594 SHA512: c81c104c671179ae1ad12b95a54e1cdb9b6fd8a66ab75fc5ec2144fe56302d2ff7bd4c7fc513950c7f84a0ec13dd197fd02c11f150e3c84a1f2a6c555eae2075 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. More information about the underlying library can be found at its repository at . Package: r-cran-rcppmecab Architecture: arm64 Version: 0.0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 131166 MD5sum: 6a679bc0e8085d8d0e4a2edd0ebfdbc4 SHA1: 10beb271f17c8672abaaccf756ad652960a09a1c SHA256: 7a4981fd4b65861eddba0a67888085d618f862ea4d935488a9052c95f96a10fa SHA512: af2e17e55f384e531cf607c806404f9c2305d744466dd71b63659434e6dd8f530965217a44e3ea2346d7702630d7799a2cf3d7fcc48d30cbad3d5bf828525d86 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: arm64 Version: 0.3.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 495 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 182410 MD5sum: 004aa7783159e27a14684dfafa0eb871 SHA1: f21bf4e36bce10959a1797bb545f8076d53d7732 SHA256: aec43bc19e835cae6b26daa288e31d05fa017394a44e77b115c122bdc9cd4108 SHA512: d4f2ed3af218ba700a0b50452794f125b2875c7668e92c435242f17f7e5d9357ecb5effa070f866cc5d99f7259fde54826f3242c5bb95923fb45ca947b0ac1ba 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. Package: r-cran-rcppmlpackexamples Architecture: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1884 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-rcppensmallen, r-cran-mlpack Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-rcppmlpackexamples_0.0.1-1.ca2604.1_arm64.deb Size: 1615380 MD5sum: 1860ff9d185d6c42bafbdb6ec882d366 SHA1: 1def456f984b2252eaed3f571117fbae6086e71d SHA256: 0542e1f6af7a613ba117b6cee1f6ae48ce4c20da3dbf3de5f3e646084693f3d8 SHA512: 2891b71683011eed0c7b3506308a0428115a045cfe281e6a2750cf18fb6c988a3df54a47e220f96dbc8c61932f1b426ed470cf05c43704ee051ff732c61bf757 Homepage: https://cran.r-project.org/package=rcppmlpackexamples Description: CRAN Package 'rcppmlpackexamples' (Example Use of 'mlpack' from C++ via R) A Minimal Example Package which demonstrates 'mlpack' use via C++ Code from R. Package: r-cran-rcppmsgpack Architecture: arm64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6182 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 548652 MD5sum: 2d746560798c48281e09c652c7d8a4ed SHA1: 3ddbb3e0efcc1c3bcfd9e06b782ce43695879fd1 SHA256: fc0b00f6baadc26d306e0e97e7f095209dc6898abde9e016146e495713a3b2f9 SHA512: fbc5914e1a0d3791ae470192e43e4c6f637c75a7ebab4644808b59dae9b531966a0db2f3db01e21c3e2dee733a1ee1511959d4acceed97c803a32c8d26c65646 Homepage: https://cran.r-project.org/package=RcppMsgPack Description: CRAN Package 'RcppMsgPack' ('MsgPack' C++ Header Files and Interface Functions for R) 'MsgPack' header files are provided for use by R packages, along with the ability to access, create and alter 'MsgPack' objects directly from R. 'MsgPack' is an efficient binary serialization format. It lets you exchange data among multiple languages like 'JSON' but it is faster and smaller. Small integers are encoded into a single byte, and typical short strings require only one extra byte in addition to the strings themselves. This package provides headers from the 'msgpack-c' implementation for C and C++(11) for use by R, particularly 'Rcpp'. The included 'msgpack-c' headers are licensed under the Boost Software License (Version 1.0); the code added by this package as well the R integration are licensed under the GPL (>= 2). See the files 'COPYRIGHTS' and 'AUTHORS' for a full list of copyright holders and contributors to 'msgpack-c'. Package: r-cran-rcppnloptexample Architecture: arm64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 35330 MD5sum: 417363265d9f9298367cc328671d2c87 SHA1: 17bf54e32646e553256d7441654b942300daf4d2 SHA256: a7c0ccd1d374be94220cca7bbf7e9400b45664b512fff76478e0370d1f53e48a SHA512: 71de0ad5fe23e76a43856b6a4ac6ec11c2ebb947a5b7fabfc5f77895ade9e97632e5c45b9329c7d0a226ea738d0b947e397de879a2ac54379ea03ed061326979 Homepage: https://cran.r-project.org/package=RcppNLoptExample Description: CRAN Package 'RcppNLoptExample' ('Rcpp' Package Illustrating Header-Only Access to 'NLopt') An example package which shows use of 'NLopt' functionality from C++ via 'Rcpp' without requiring linking, and relying just on 'nloptr' thanks to the exporting API added there by Jelmer Ypma. This package is a fully functioning, updated, and expanded version of the initial example by Julien Chiquet at also containing a large earlier pull request of mine. Package: r-cran-rcppnumerical Architecture: arm64 Version: 0.7-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 726 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-prettydoc, r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-rcppnumerical_0.7-0-1.ca2604.1_arm64.deb Size: 208760 MD5sum: b88a49e06c5c6948498414a987d3a787 SHA1: 43ace0320e92bcdc2e5b3754b8807c175c562bdd SHA256: 13dab43f81d43cdf0cd8cb119046e5c898dface006aa57af93986e5d69f29070 SHA512: e57171c0ce2194baeac7af32361cf042c5d11e0f89e35586b067e27aa778e7d40904423ef7dfe1991b12b72db1a9a9aeb8e00adba1f6e337db4c74b644996d9c Homepage: https://cran.r-project.org/package=RcppNumerical Description: CRAN Package 'RcppNumerical' ('Rcpp' Integration for Numerical Computing Libraries) A collection of open source libraries for numerical computing (numerical integration, optimization, etc.) and their integration with 'Rcpp'. Package: r-cran-rcppparallel Architecture: arm64 Version: 5.1.11-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2532 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), libtbbmalloc2 (>= 2022.3.0), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-rcpp, r-cran-runit, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rcppparallel_5.1.11-2-1.ca2604.1_arm64.deb Size: 485444 MD5sum: 305d9af5df0494c8a76a5218feaac23b SHA1: 8d344c17899c205bce6daa5873b10e774b00f310 SHA256: 2378aef425969db52d269e026535d13fca2b155e07bb684aef801c0e30d3f20a SHA512: 28450cb2b0514aa0270750213fa8c8e020c3cba8f651f3b770d3599b79a52a72a3dd401dbb9ecab662f478e1e6249b2bc719af171c070661d482e441ec67b3ac Homepage: https://cran.r-project.org/package=RcppParallel Description: CRAN Package 'RcppParallel' (Parallel Programming Tools for 'Rcpp') High level functions for parallel programming with 'Rcpp'. For example, the 'parallelFor()' function can be used to convert the work of a standard serial "for" loop into a parallel one and the 'parallelReduce()' function can be used for accumulating aggregate or other values. Package: r-cran-rcppplanc Architecture: arm64 Version: 2.0.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3751 Depends: libblas3 | libblas.so.3, libc6 (>= 2.40), libgcc-s1 (>= 3.0), libgomp1 (>= 6), libhdf5-310 (>= 1.14.3), 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-hdf5r.extra, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-highfive Suggests: r-cran-knitr, r-cran-withr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rcppplanc_2.0.15-1.ca2604.1_arm64.deb Size: 1980688 MD5sum: 26d57e1e6220a4664037d0b40fc076e8 SHA1: c8258b1072d1e2b4cbe3e88849f112b348e672cb SHA256: bdff90d7253f6edd0d4bc13ef20d00144f01bcafffa4581e36ba118d01c81d55 SHA512: 6628485c4358b41c5b738f0d81b42b9174d471e01100c5c8b4d2ac9f7debf08a307807a90662d13e6c6028832a97adf162f2146845a793369acd720ffe8feee5 Homepage: https://cran.r-project.org/package=RcppPlanc Description: CRAN Package 'RcppPlanc' (Parallel Low-Rank Approximation with Nonnegativity Constraints) 'Rcpp' bindings for 'PLANC', a highly parallel and extensible NMF/NTF (Non-negative Matrix/Tensor Factorization) library. Wraps algorithms described in Kannan et. al (2018) and Eswar et. al (2021) . Implements algorithms described in Welch et al. (2019) , Gao et al. (2021) , and Kriebel & Welch (2022) . Package: r-cran-rcppquantuccia Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1009 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_arm64.deb Size: 285856 MD5sum: 6c6fcd95a83dcad0be944c7eae5a9d2d SHA1: f96778395dfe34ddb9cffc420ff05ff6cedd3a02 SHA256: 7268e13b804015c654c1b82e7cd23a666ee63036be5af0bd6bb505c4bc9fc943 SHA512: 1c816f3ef1fde67b6106849ba29777658019435bd0bce711a5e4be73065571d38bf00f8ba99efd0bfaacaf5d6115c28467d8f79bce3fa99cc21f9e2191ff2330 Homepage: https://cran.r-project.org/package=RcppQuantuccia Description: CRAN Package 'RcppQuantuccia' (R Bindings to the Calendaring Functionality of 'QuantLib') 'QuantLib' bindings are provided for R using 'Rcpp' via an updated variant of the header-only 'Quantuccia' project (put together initially by Peter Caspers) offering an essential subset of 'QuantLib' (and now maintained separately for the calendaring subset). See the included file 'AUTHORS' for a full list of contributors to both 'QuantLib' and 'Quantuccia'. Note that this package provided an initial viability proof, current work is done (via approximately quarterly releases tracking 'QuantLib') in the smaller package 'qlcal' which is generally preferred. Package: r-cran-rcppredis Architecture: arm64 Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 860 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libhiredis1.1.0 (>= 1.2.0), libstdc++6 (>= 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_arm64.deb Size: 424204 MD5sum: ebcde92a79f19faa4e722c8de292aa16 SHA1: 9d7cc6a06a029ebb58abfc0df960ba63be661d48 SHA256: 5b4281b7b06677565d61d30eef862581455ef460c838bbbfb1976e85d079410f SHA512: 936bb3451ac6817b1f6c367043c35c279582b70bf3ab060eee86a35ef67cf32d7fe1a87d80b136614fe93e4dd123d84e0a2c7cd8552c1e6a1023ce7acded83bb Homepage: https://cran.r-project.org/package=RcppRedis Description: CRAN Package 'RcppRedis' ('Rcpp' Bindings for 'Redis' using the 'hiredis' Library) Connection to the 'Redis' (or 'Valkey') key/value store using the C-language client library 'hiredis' (included as a fallback) with 'MsgPack' encoding provided via 'RcppMsgPack' headers. It now also includes the pub/sub functions from the 'rredis' package. Package: r-cran-rcpproll Architecture: arm64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 333 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-zoo, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rcpproll_0.3.2-1.ca2604.1_arm64.deb Size: 88032 MD5sum: cb75acff8b759e6aa3641063de78d63d SHA1: b52d3e5e55a723f99408b36c3b9336b556ac4003 SHA256: a5ada1527931cc6a25629082a738d278be844a21fe2f6293a52b2595df77185e SHA512: 77186699c29fbc8d2dde85b9af4c2aa64be5a89efdb298e07d636830d7250e91bdd50992aa8f1f2158d15357364af48ab985bf6abe35dee091522eb7e7c9b34b Homepage: https://cran.r-project.org/package=RcppRoll Description: CRAN Package 'RcppRoll' (Efficient Rolling / Windowed Operations) Provides fast and efficient routines for common rolling / windowed operations. 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Package: r-cran-rcppsimdjson Architecture: arm64 Version: 0.1.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13831 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-bit64, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-rcppsimdjson_0.1.15-1.ca2604.1_arm64.deb Size: 1078474 MD5sum: f474ce8928f59f40fc35951469e30d83 SHA1: be7d823af3744ee4c71c935132abd4a865e11af3 SHA256: 926ed85a27afd378a14f112e99b100b136a975787660aec3d9eef58f4ba91a42 SHA512: bd66ec3ada5e7514d5575e73d91653614f7f209602c0ef25f47533ac2119cd565083ffa64ac583eb68bcf91b95ed05ea7cf64d597d3dd377e43bb9f536a08bac Homepage: https://cran.r-project.org/package=RcppSimdJson Description: CRAN Package 'RcppSimdJson' ('Rcpp' Bindings for the 'simdjson' Header-Only Library for'JSON' Parsing) The 'JSON' format is ubiquitous for data interchange, and the 'simdjson' library written by Daniel Lemire (and many contributors) provides a high-performance parser for these files which by relying on parallel 'SIMD' instruction manages to parse these files as faster than disk speed. See the paper for more details about 'simdjson'. This package parses 'JSON' from string, file, or remote URLs under a variety of settings. Package: r-cran-rcppsmc Architecture: arm64 Version: 0.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 803 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-fkf, r-cran-rcpparmadillo Suggests: r-cran-pkgkitten Filename: pool/dists/resolute/main/r-cran-rcppsmc_0.2.9-1.ca2604.1_arm64.deb Size: 271810 MD5sum: 9372b3dad73078b79808afe5e8fc45a7 SHA1: 46028213beac5c98e22a4e26acf24ce65fa9297b SHA256: ec2c4a53f2e5fabf481f932e49a8cc4f5db2b8d087504a743f1d0108ff1bdc0b SHA512: c60011fc964177415abc7afd7626af5a0e1d4d10b268d4c083ecdcf0da7e3df10f40033068ef8a0f0121fa6b98fd69fd96729f36038a939d7a8935c814c9020d Homepage: https://cran.r-project.org/package=RcppSMC Description: CRAN Package 'RcppSMC' (Rcpp Bindings for Sequential Monte Carlo) R access to the Sequential Monte Carlo Template Classes by Johansen is provided. 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Package: r-cran-rcppspdlog Architecture: arm64 Version: 0.0.29-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1777 Depends: libc6 (>= 2.33), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 377910 MD5sum: e8e8fac10afaaa806a2eeb509e3676f8 SHA1: ad45ffce965c7da52fce866b0d92d49e71e06955 SHA256: f31d742a255163b0ab1164872140503a8bda81077b51a021806e20278d0b221e SHA512: 32a49fb79f506ba0dd30a8c893bb83d0f2ccb47f855009886ea3793f1bec55c9fc113bb6ca2bced76d7cc61fc99fcd9ebe8a4edefb643e94fd10040476f94f1c Homepage: https://cran.r-project.org/package=RcppSpdlog Description: CRAN Package 'RcppSpdlog' (R and C++ Interfaces to 'spdlog' C++ Header Library for Logging) The mature and widely-used C++ logging library 'spdlog' by Gabi Melman provides many desirable features. This package bundles these header files for easy use by R packages from both their R and C or C++ code. Explicit use via 'LinkingTo:' is also supported. Also see the 'spdl' package which enhanced this package with a consistent R and C++ interface. 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Package: r-cran-rcpptimer Architecture: arm64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 654 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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-rcpptimer_1.2.1-1.ca2604.1_arm64.deb Size: 325258 MD5sum: 173dc82111d5920c40d6e9dac53469bb SHA1: 64e4e2d8c12d4c58230fd7430f63e2dc424e6230 SHA256: 3414734aa38bcc8c16242b54597c668afda8a91a84e6dfe57c8b825cad84cdd8 SHA512: 4a66dcd20aec302d81d55554f93be1cb47f74ce3faeb94a0b6abf89f8d78b56f2d978dec911e566ccfc5b6b6fb3747ae4b700ce8bdad409f9e8459133fd2030b Homepage: https://cran.r-project.org/package=rcpptimer Description: CRAN Package 'rcpptimer' ('Rcpp' Tic-Toc Timer with 'OpenMP' Support) Provides 'Rcpp' bindings for 'cpptimer', a simple tic-toc timer class for benchmarking 'C++' code . It's not just simple, it's blazing fast! This sleek tic-toc timer class supports overlapping timers as well as 'OpenMP' parallelism . It boasts a nanosecond-level time resolution. We did not find any overhead of the timer itself at this resolution. Results (with summary statistics) are automatically passed back to 'R' as a data frame. Package: r-cran-rcpptn Architecture: arm64 Version: 0.2-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 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_arm64.deb Size: 44468 MD5sum: 53a9c722d1bb08f72ab02a38a6dda2fa SHA1: 6646e73c3b9154b96c618cb5ef672db3b23b9845 SHA256: 8b92687634f1ec7c858bb26c5dc37ccd56be068c10a94ff5506e6cf8f1a89054 SHA512: 6646150640d0dcd29eecf0eb095c2aa7ec33b9877366e9130959bc03bd090e702751a12c2758aae14e757c0c1c139e963a53545a8c3b9b9d1614bc403c208105 Homepage: https://cran.r-project.org/package=RcppTN Description: CRAN Package 'RcppTN' (Rcpp-Based Truncated Normal Distribution RNG and Family) R-level and C++-level functionality to generate random deviates from and calculate moments of a Truncated Normal distribution using the algorithm of Robert (1995) . In addition to RNG, functions for calculating moments, densities, and entropies are provided at both levels. Package: r-cran-rcpptoml Architecture: arm64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1076 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_arm64.deb Size: 196822 MD5sum: f8eec1c7e382a0f9d93882d073f06aeb SHA1: adf46d83dae21df92c41841604e4f808a438f0a8 SHA256: 6ec10a97bf673cae131a6ecb0ba926d9a7c820b2bdc47dcf78d07165ddf3a8fc SHA512: 1f27c44beb3847cc4d359208c8137a1a28d31038d941b494e292d7b4810085aabe06237c3d1efc97950ce7410ba25aece58917ee1a360a2520387b58a2d3fa47 Homepage: https://cran.r-project.org/package=RcppTOML Description: CRAN Package 'RcppTOML' ('Rcpp' Bindings to Parser for "Tom's Obvious Markup Language") The configuration format defined by 'TOML' (which expands to "Tom's Obvious Markup Language") specifies an excellent format (described at ) suitable for both human editing as well as the common uses of a machine-readable format. This package uses 'Rcpp' to connect to the 'toml++' parser written by Mark Gillard to R. Package: r-cran-rcpptskit Architecture: arm64 Version: 0.2.0-1.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-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_arm64.deb Size: 400846 MD5sum: 51a8515df817e088857f56226e5ac696 SHA1: 05ea1442421bc7ac1c3c3e1023619a2b51ac4e12 SHA256: fa7c49a8608ff76582d415cd6191687d11ad85f993b54a9fe78b0bea6376678f SHA512: 964e353a2a417494083f63047aeaf7dd65f6e020058abf1599e85275efbacc2c48294c736bae0d5a731ed568d68a6e80267bf405d332ea23a7b4eca2d8a4c27e 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. 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The initial repository was at . 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Urs Ramer (1972), "An iterative procedure for the polygonal approximation of plane curves" . David H. Douglas and Thomas K. Peucker (1973), "Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature" . 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Package: r-cran-ream Architecture: arm64 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_arm64.deb Size: 326348 MD5sum: 0307c874b4e1f3791721ac8ad8dac6f0 SHA1: 23a2f94cfd86b22a98334bc22352928c6e496f62 SHA256: c475a44d2b00e7947c669ce57986900dfab8978c71febcd4ccccce6c0cc180bc SHA512: 29860f5d7e8126b011340489ae43e2d0c3367c9355308c40f469d8028221b21644055a39092e49b8b435d09082e7b9edb6ceb91a2ffbcbb79b098c9da90e88e0 Homepage: https://cran.r-project.org/package=ream Description: CRAN Package 'ream' (Density, Distribution, and Sampling Functions for EvidenceAccumulation Models) Calculate the probability density functions (PDFs) for two threshold evidence accumulation models (EAMs). These are defined using the following Stochastic Differential Equation (SDE), dx(t) = v(x(t),t)*dt+D(x(t),t)*dW, where x(t) is the accumulated evidence at time t, v(x(t),t) is the drift rate, D(x(t),t) is the noise scale, and W is the standard Wiener process. The boundary conditions of this process are the upper and lower decision thresholds, represented by b_u(t) and b_l(t), respectively. Upper threshold b_u(t) > 0, while lower threshold b_l(t) < 0. The initial condition of this process x(0) = z where b_l(t) < z < b_u(t). We represent this as the relative start point w = z/(b_u(0)-b_l(0)), defined as a ratio of the initial threshold location. This package generates the PDF using the same approach as the 'python' package it is based upon, 'PyBEAM' by Murrow and Holmes (2023) . First, it converts the SDE model into the forwards Fokker-Planck equation dp(x,t)/dt = d(v(x,t)*p(x,t))/dt-0.5*d^2(D(x,t)^2*p(x,t))/dx^2, then solves this equation using the Crank-Nicolson method to determine p(x,t). Finally, it calculates the flux at the decision thresholds, f_i(t) = 0.5*d(D(x,t)^2*p(x,t))/dx evaluated at x = b_i(t), where i is the relevant decision threshold, either upper (i = u) or lower (i = l). The flux at each thresholds f_i(t) is the PDF for each threshold, specifically its PDF. We discuss further details of this approach in this package and 'PyBEAM' publications. Additionally, one can calculate the cumulative distribution functions of and sampling from the EAMs. Package: r-cran-rebmix Architecture: arm64 Version: 2.17.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4336 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rebmix_2.17.1-1.ca2604.1_arm64.deb Size: 3167918 MD5sum: 57b2d34f145bdfe5ba1e27c8733b45af SHA1: 8bd32307e0e728cefc69a0c963c94f9f8586c9a6 SHA256: b65a152e998efce2e4b3aa04c92824f9f8b1b1966fb7d20961ceb055e80f7017 SHA512: 2226e527ae499de2b936095d837a5ec7acb5d3bf90517dba91bccdb3a4d9406c428c5a756a01204fd51d5ed7d859ad3b95da8a37382c991087d9a26d1f4a3f88 Homepage: https://cran.r-project.org/package=rebmix Description: CRAN Package 'rebmix' (Finite Mixture Modeling, Clustering & Classification) Random univariate and multivariate finite mixture model generation, estimation, clustering, latent class analysis and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac, uniform or circular von Mises parametric families. Package: r-cran-recassorules Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 245 Depends: libc6 (>= 2.17), 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-recassorules_1.0-1.ca2604.1_arm64.deb Size: 71442 MD5sum: 646d46c67e34f933b5706963a2462cef SHA1: ee7928f18e20a7520167e0b3aa2172eab4814284 SHA256: 6d176eea452d734761dd1abfb00718ed1435bc577a83fb8a14ea29d5f0171716 SHA512: bbcf6580b594917405d7f8f72db7a89cc5775a3cdf006adabc956612958b2abf9788fb95b8ad158c21b6fbaaf0d016e8c3cf71e379140cce550455d6262dd846 Homepage: https://cran.r-project.org/package=RecAssoRules Description: CRAN Package 'RecAssoRules' (Recursive Mining for Frequent Pattern and Confident AssociationRules) Provides functions allowing the user to recursively extract frequent patterns and confident rules according to indicators of minimal support and minimal confidence. These functions are described in "Recursive Association Rule Mining" Abdelkader Mokkadem, Mariane Pelletier, Louis Raimbault (2020) . Package: r-cran-recexcavaar Architecture: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2961 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-kriging, r-cran-rcpp Suggests: r-cran-devtools, r-cran-dplyr, r-cran-knitr, r-cran-magrittr, r-cran-rgl, r-cran-rmarkdown, r-cran-roxygen2, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-recexcavaar_0.3.0-1.ca2604.1_arm64.deb Size: 456952 MD5sum: 42beaa7906ff6aa4916c0fbd7d61483a SHA1: cdc053c17303efff0d58486fad386b442ae8ad31 SHA256: 2a459f6df73cc85ca2fbe32b1d45fbaf47e9a80bc5a7cb5f2207cc3348efed1f SHA512: 036209e3f0a126b8427b313d0869ac8b16932d909ef091ed8bae16548620ebbf949ca407c92c12b32fcb7f0dc10b9afce58078466251ebc6558c61c61f7fb534 Homepage: https://cran.r-project.org/package=recexcavAAR Description: CRAN Package 'recexcavAAR' (3D Reconstruction of Archaeological Excavations) A toolset for 3D reconstruction and analysis of excavations. It provides methods to reconstruct natural and artificial surfaces based on field measurements. This allows to spatially contextualize documented subunits and features. Intended to be part of a 3D visualization workflow. Package: r-cran-reclin2 Architecture: arm64 Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 733 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 273096 MD5sum: a3fa50feeb7ee2b4430efaab308b55bb SHA1: ea6498d666bf181d79b814d0fa6e3ff80e225bdd SHA256: 51342fdfb11e143d14c80898b4dd792398851f56d31f3c7626db68e3e4e18eb9 SHA512: 963b034eb5efe0b233761760dac7f1ae11e1ea26fd0529508b1c8da06f6938eeb8cfd17b99c84af5ce320a0aa26583065d8c656dfc3b125c27ca54254d61ea31 Homepage: https://cran.r-project.org/package=reclin2 Description: CRAN Package 'reclin2' (Record Linkage Toolkit) Functions to assist in performing probabilistic record linkage and deduplication: generating pairs, comparing records, em-algorithm for estimating m- and u-probabilities (I. Fellegi & A. Sunter (1969) , T.N. Herzog, F.J. Scheuren, & W.E. Winkler (2007), "Data Quality and Record Linkage Techniques", ISBN:978-0-387-69502-0), forcing one-to-one matching. Can also be used for pre- and post-processing for machine learning methods for record linkage. Focus is on memory, CPU performance and flexibility. Package: r-cran-recmap Architecture: arm64 Version: 1.0.20-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2029 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1465340 MD5sum: 4d151340ebd20c31d1b8e1e218032126 SHA1: cc2511af86266b7dadaa6a1d0d99e870c0c74e09 SHA256: 3c997da383f0e25ed3e98505831d177ce5c58973fe1c3979f27251fce979ec26 SHA512: aa9c6eb2302447249d8e138ae2e41e9ba7e8c6a9bd29a3247c02a8428d749064b4c0887f568a7e2ef9c5db653e3f1c31fc5375ca959b2244441899b162cce87c Homepage: https://cran.r-project.org/package=recmap Description: CRAN Package 'recmap' (Compute the Rectangular Statistical Cartogram) Implements the RecMap MP2 construction heuristic . This algorithm draws maps according to a given statistical value, e.g., election results, population, or epidemiological data. The basic idea of the RecMap algorithm is that each map region, e.g., different countries, is represented by a rectangle. The area of each rectangle represents the statistical value provided as input to maintain zero cartographic error. Computationally intensive tasks are implemented in C++. The included vignette documents recmap algorithm usage. Package: r-cran-recocrop Architecture: arm64 Version: 0.4-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 810 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 500372 MD5sum: c112bb8b04202178d53dd6100b4e6c2a SHA1: 0c70db263c1e3b6b3e6a3e56af5feb8a5df1a1d4 SHA256: b3817a9484565f2c349c46df2ff6780492deaff5c00e3efeee5e770a327af72d SHA512: f1096feeec0727a8df9d680d3c274474c5696d69195e57679e09ac5fde3f9db1e6d57a8a7cede04cf9a1f7eff0ac007cd19f21386ccb16201eb024fd7fba6ebf Homepage: https://cran.r-project.org/package=Recocrop Description: CRAN Package 'Recocrop' (Estimating Environmental Suitability for Plants) The ecocrop model estimates environmental suitability for plants using a limiting factor approach for plant growth following Hackett (1991) . The implementation in this package is fast and flexible: it allows for the use of any (environmental) predictor variable. Predictors can be either static (for example, soil pH) or dynamic (for example, monthly precipitation). Package: r-cran-recometrics Architecture: arm64 Version: 0.1.6-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.2), libgomp1 (>= 6), libstdc++6 (>= 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_arm64.deb Size: 143372 MD5sum: 536f8606b5d4be1e6ac6937f071852fc SHA1: f3e83908306888fef01ec18874f8084948c84e13 SHA256: be662541978b61a9686e0a732f724b4c54409b067fc37a1e81f241139b18d9c4 SHA512: a9d120bbab7915a652446f12e2e3cbcbb055d88851fa6406d22b3cbeff7f64d899b34a2da58093916fcc0b876d6c32a868e52396f217c7729320b03668fe8500 Homepage: https://cran.r-project.org/package=recometrics Description: CRAN Package 'recometrics' (Evaluation Metrics for Implicit-Feedback Recommender Systems) Calculates evaluation metrics for implicit-feedback recommender systems that are based on low-rank matrix factorization models, given the fitted model matrices and data, thus allowing to compare models from a variety of libraries. Metrics include P@K (precision-at-k, for top-K recommendations), R@K (recall at k), AP@K (average precision at k), NDCG@K (normalized discounted cumulative gain at k), Hit@K (from which the 'Hit Rate' is calculated), RR@K (reciprocal rank at k, from which the 'MRR' or 'mean reciprocal rank' is calculated), ROC-AUC (area under the receiver-operating characteristic curve), and PR-AUC (area under the precision-recall curve). These are calculated on a per-user basis according to the ranking of items induced by the model, using efficient multi-threaded routines. Also provides functions for creating train-test splits for model fitting and evaluation. Package: r-cran-reconstructr Architecture: arm64 Version: 2.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1713 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1066762 MD5sum: b0e51cc1dcfa9fc80d5b47e82cca85d3 SHA1: 9bb9426577d3bec098e7c0768fe9f1300c81fb57 SHA256: 72cad88211c9d98600466236dc3f27e471447e0e842803f5e5743fcab560417c SHA512: 9c5f2dbe55516325b86a980c068b1344889c8616ceb3201f62fd2472adb88b8ac4483226c82ddf0c17149a45facec8abc8c00d50b390d71e98a9a8b6cb2f51d3 Homepage: https://cran.r-project.org/package=reconstructr Description: CRAN Package 'reconstructr' (Session Reconstruction and Analysis) Functions to reconstruct sessions from web log or other user trace data and calculate various metrics around them, producing tabular, output that is compatible with 'dplyr' or 'data.table' centered processes. Package: r-cran-recor Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 229 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 55240 MD5sum: e47784a87049c80d054d87a33ffeabd3 SHA1: 92f4698ea982a67f6a385938c84fbfd8c27d0295 SHA256: 7db479b2329eed54c81a06fcb9b2a63ed22f684192f775d3dbc090fd6b3f0169 SHA512: bf584a05cb9e61e78cd006f16a8557eb8a0dcf20b964d3f4abf65b4c4032a9b3cd0c79d17506855910e4bf0ba0178c08ae3b3fcf119ca90dd5c61f1b33530ae5 Homepage: https://cran.r-project.org/package=recor Description: CRAN Package 'recor' (Rearrangement Correlation Coefficient) The Rearrangement Correlation Coefficient is an adjusted version of Pearson's correlation coefficient that accurately measures monotonic dependence relationships, including both linear and nonlinear associations. This method addresses the underestimation problem of classical correlation coefficients in nonlinear monotonic scenarios through improved statistical bounds derived from rearrangement inequalities. For more details, see Ai (2024) . Package: r-cran-recordlinkage Architecture: arm64 Version: 0.4-12.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3627 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi, r-cran-rsqlite, r-cran-ff, r-cran-e1071, r-cran-rpart, r-cran-ipred, r-cran-evd, r-cran-data.table, r-cran-nnet, r-cran-xtable Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-recordlinkage_0.4-12.6-1.ca2604.1_arm64.deb Size: 1026608 MD5sum: 216ed10ad2b9a78d76a2b8b0e0a4e4df SHA1: 1f17d0743dad911853394d9f0bdb89b60321b899 SHA256: 1cc413209c922e2c3f054828b93db681fdd6e7a9ef89b905657e71b7b76979d8 SHA512: 2faf433b2ecf9a2b0e6e1259e7764a3f6e14244c0eb7a5156cec1297d5bd106117df95d1f8be031e808efc435f8b6380bd16c5da3c8ee1065935cd289e0bc557 Homepage: https://cran.r-project.org/package=RecordLinkage Description: CRAN Package 'RecordLinkage' (Record Linkage Functions for Linking and Deduplicating Data Sets) Provides functions for linking and deduplicating data sets. Methods based on a stochastic approach are implemented as well as classification algorithms from the machine learning domain. For details, see our paper "The RecordLinkage Package: Detecting Errors in Data" Sariyar M / Borg A (2010) . Package: r-cran-recosystem Architecture: arm64 Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 809 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_arm64.deb Size: 396506 MD5sum: 761323a4237682ac75608f2c583988a6 SHA1: 5c2a7b8b98498fd4c4429e1f9b8158b41bf83702 SHA256: d053d24dfb77af80fee5ea131d9d51e275f8e375dcbfa5018580cb864c49a525 SHA512: 33b29e10bee47986502967d19daff9079127cdb62542ebbcf3280e26b7e467c47757870593f1a0351fe06ec7cdb10fc5c52b230eb62b7ac4afc4f8f0fb5cfd61 Homepage: https://cran.r-project.org/package=recosystem Description: CRAN Package 'recosystem' (Recommender System using Matrix Factorization) R wrapper of the 'libmf' library for recommender system using matrix factorization. It is typically used to approximate an incomplete matrix using the product of two matrices in a latent space. Other common names for this task include "collaborative filtering", "matrix completion", "matrix recovery", etc. High performance multi-core parallel computing is supported in this package. Package: r-cran-rectpacker Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 21486 MD5sum: daaf1ce6187c6c23395ace96de20207e SHA1: 560da9789cd1847bfe5b694fe2bfd059dfc0a38e SHA256: cc774abd054b9230f0702352745f8ee6369a46181a6a7bae6f9e4ec63ff0dde5 SHA512: d6df61552d0e40064b2c24a5f1676d10541d3c45675958adbde94657ba5eb1fbdfbe12661fb3403db6a5e5a14bd871d01f9fc53ca022fa46a65981df2008314f Homepage: https://cran.r-project.org/package=rectpacker Description: CRAN Package 'rectpacker' (Rectangle Packing) Rectangle packing is a packing problem where rectangles are placed into a larger rectangular region (without overlapping) in order to maximise the use space. Rectangles are packed using the skyline heuristic as discussed in Lijun et al (2011) 'A Skyline-Based Heuristic for the 2D Rectangular Strip Packing Problem' . A function is also included for determining a good small-sized box for containing a given set of rectangles. Package: r-cran-recurse Architecture: arm64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 340910 MD5sum: fbadc5b471c964aa7340bead9f72f84a SHA1: 67044e914353e59ae37d2bdfb683ac8413853d4b SHA256: f92f669e2fa9e0edd59aebfb86c27d328f54ddc2b01261c4aacfaa504087f27c SHA512: e53d025617221deb20f2d6eca14f358b0bbb0a8973e1593babd9f2719392670d24f25d3d63c935ec3ff68593b8660f07db45125c69a80aff107d484f10ce7ece Homepage: https://cran.r-project.org/package=recurse Description: CRAN Package 'recurse' (Computes Revisitation Metrics for Trajectory Data) Computes revisitation metrics for trajectory data, such as the number of revisitations for each location as well as the time spent for that visit and the time since the previous visit. Also includes functions to plot data. Package: r-cran-reda Architecture: arm64 Version: 0.5.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4344 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 1278202 MD5sum: e5c4d098ef13558e87bdbead4fd36a5d SHA1: c6a8960d978f07e49ced193a841a859a541cc49d SHA256: ed1a5a136466e6c537dfd2362496354a7ae3eb4ab342ae65d9a253991e4ff74e SHA512: c49a1ea638d1a912c431f121ef6d841832134fd29f893dc131f24bd8d8eeb774844bea368ce5408a5f616743b55423354d076b0fd1556c832a16ada1dc830032 Homepage: https://cran.r-project.org/package=reda Description: CRAN Package 'reda' (Recurrent Event Data Analysis) Contains implementations of recurrent event data analysis routines including (1) survival and recurrent event data simulation from stochastic process point of view by the thinning method proposed by Lewis and Shedler (1979) and the inversion method introduced in Cinlar (1975, ISBN:978-0486497976), (2) the mean cumulative function (MCF) estimation by the Nelson-Aalen estimator of the cumulative hazard rate function, (3) two-sample recurrent event responses comparison with the pseudo-score tests proposed by Lawless and Nadeau (1995) , (4) gamma frailty model with spline rate function following Fu, et al. (2016) . Package: r-cran-redatam Architecture: arm64 Version: 2.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1378 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_arm64.deb Size: 378188 MD5sum: 156d0c3d0da03d142a9b073627f282e4 SHA1: b557644422f170487ccf99c78de989ecbf2832fa SHA256: a267858b9084b17889c8bd4eee6013ffb7012e27ef5697f3c551ea96fc7fcad1 SHA512: f15bdf261cab0d8d3930fd1b571d803dd8d2303818e86eb7e666669384021e32eb6a44a4f36f7b006366461d19a051c9778e2a0f6c891471251ee5c308c3261c Homepage: https://cran.r-project.org/package=redatam Description: CRAN Package 'redatam' (Import 'REDATAM' Files) Import 'REDATAM' formats into R via the 'Open REDATAM' C++ library. The full context of this project and details about the implementation are available in (Open Access). Package: r-cran-redatamx Architecture: arm64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.6.0), r-api-4.0, r-cran-cpp11 Filename: pool/dists/resolute/main/r-cran-redatamx_1.3.0-1.ca2604.1_arm64.deb Size: 76218 MD5sum: dffab6695c2dab2882f395b73b7345bd SHA1: 6615acf2ba6ef2e585216800e59fdec15b901990 SHA256: 911070a3430cc7c7fc7e5c86e50774e64f223f797825e83e913f5fd7be459125 SHA512: 89878b5a986bece9dfe2589987d2680447f673aa6f6956ef17425f139382ef38e641a22f527f7de2fac0ab2834cb73050c6a4fe46cc1c57e5d3279ebd0f67c67 Homepage: https://cran.r-project.org/package=redatamx Description: CRAN Package 'redatamx' (R Interface to 'Redatam' Library) Provides an API to work with 'Redatam' (see ) databases in both formats: 'RXDB' (new format) and 'DICX' (old format) and running 'Redatam' programs written in 'SPC' language. It's a wrapper around 'Redatam' core and provides functions to open/close a database (redatam_open()/redatam_close()), list entities and variables from the database (redatam_entities(), redatam_variables()) and execute a 'SPC' program and gets the results as data frames (redatam_query(), redatam_run()). Package: r-cran-reddyproc Architecture: arm64 Version: 1.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2652 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_arm64.deb Size: 2098728 MD5sum: c92e584516de6bd72dd45b2cf53925a1 SHA1: 26a6ef9535d85dff2849c4bf5c8ecd94710d666d SHA256: 5c91d65d486541a9d39efd68d892298803eefbbfce140a49259c9984b9f08233 SHA512: 57945ce492a85c10e75fe964d9e2bc6db8f2028f8b823a743020f696ea94874d387f9403a7d160ed02a7211da536912f6f6c4064a31f3e7a50d8dbe7ee5c9d22 Homepage: https://cran.r-project.org/package=REddyProc Description: CRAN Package 'REddyProc' (Post Processing of (Half-)Hourly Eddy-Covariance Measurements) Standard and extensible Eddy-Covariance data post-processing (Wutzler et al. (2018) ) includes uStar-filtering, gap-filling, and flux-partitioning. The Eddy-Covariance (EC) micrometeorological technique quantifies continuous exchange fluxes of gases, energy, and momentum between an ecosystem and the atmosphere. It is important for understanding ecosystem dynamics and upscaling exchange fluxes. (Aubinet et al. (2012) ). This package inputs pre-processed (half-)hourly data and supports further processing. First, a quality-check and filtering is performed based on the relationship between measured flux and friction velocity (uStar) to discard biased data (Papale et al. (2006) ). Second, gaps in the data are filled based on information from environmental conditions (Reichstein et al. (2005) ). Third, the net flux of carbon dioxide is partitioned into its gross fluxes in and out of the ecosystem by night-time based and day-time based approaches (Lasslop et al. (2010) ). Package: r-cran-redist Architecture: arm64 Version: 4.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4956 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_arm64.deb Size: 3197462 MD5sum: 5eacdc21fa8205f218b993daa2aeea97 SHA1: 41020d5e7873c05c84ed853556784572e205d506 SHA256: b6ef3b8b148cd942ceaf0c3329e4a33350c1c10177b9b73c1784067b07f74284 SHA512: 020f2796939ca484aee218abc723e76a76ed1a18cf6e89631c81cb01ef184495ab95d7d7c987111ff1bfb9b3702d1a93bcf08cbb623cd38409959924bd409c2a Homepage: https://cran.r-project.org/package=redist Description: CRAN Package 'redist' (Simulation Methods for Legislative Redistricting) Enables researchers to sample redistricting plans from a pre-specified target distribution using Sequential Monte Carlo and Markov Chain Monte Carlo algorithms. The package allows for the implementation of various constraints in the redistricting process such as geographic compactness and population parity requirements. Tools for analysis such as computation of various summary statistics and plotting functionality are also included. The package implements the SMC algorithm of McCartan and Imai (2023) , the enumeration algorithm of Fifield, Imai, Kawahara, and Kenny (2020) , the Flip MCMC algorithm of Fifield, Higgins, Imai and Tarr (2020) , the Merge-split/Recombination algorithms of Carter et al. (2019) and DeFord et al. (2021) , and the Short-burst optimization algorithm of Cannon et al. (2020) . Package: r-cran-redistmetrics Architecture: arm64 Version: 1.0.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1121 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-rcpp, r-cran-vctrs, r-cran-cli, r-cran-foreach, r-cran-doparallel, r-cran-magrittr, r-cran-dplyr, r-cran-rlang, r-cran-geos, r-cran-wk, r-cran-libgeos, r-cran-rcpparmadillo, r-cran-rcppthread Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-redistmetrics_1.0.11-1.ca2604.1_arm64.deb Size: 527876 MD5sum: 43e2104f90ed025f2cb7da67addc0c20 SHA1: 5d9ff2fc2a43f2a77fd553184d5e48c37bdca060 SHA256: dbd356826fab98447eeb93104aadc416a16794565068c899c0f8fc094683edee SHA512: 3fa9910b41216df5b1ac8d5a13485dbf52c662c8dea071dfdb3df9a45875def8ea132061670336e9b25abb4734c14c0d512becd91d17205dbd7ea702ab84f1be Homepage: https://cran.r-project.org/package=redistmetrics Description: CRAN Package 'redistmetrics' (Redistricting Metrics) Reliable and flexible tools for scoring redistricting plans using common measures and metrics. These functions provide key direct access to tools useful for non-simulation analyses of redistricting plans, such as for measuring compactness or partisan fairness. Tools are designed to work with the 'redist' package seamlessly. Package: r-cran-redland Architecture: arm64 Version: 1.0.17-19-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1200 Depends: libc6 (>= 2.17), librdf0t64 (>= 1.0.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-roxygen2 Suggests: r-cran-spelling, r-cran-knitr, r-cran-testthat, r-cran-rmarkdown, r-cran-stringi Filename: pool/dists/resolute/main/r-cran-redland_1.0.17-19-1.ca2604.1_arm64.deb Size: 739604 MD5sum: 22eb6929a488e1b404dcee4ed77fbeb5 SHA1: 120389b021983c4b0da777f310114b5cd242f34e SHA256: 35247640be675b1ee9face748b9817ac41f9884392efcc831db94058d9f84e47 SHA512: 5e2bc979975125168d9e8edf8ac95afb85bc68e97c4932567dcfdb694b3c2031db7665dd38c41f9ec52a5c4e33d871cca09a09ec9f237ba81c5fe24b480f19fd Homepage: https://cran.r-project.org/package=redland Description: CRAN Package 'redland' (RDF Library Bindings in R) Provides methods to parse, query and serialize information stored in the Resource Description Framework (RDF). RDF is described at . This package supports RDF by implementing an R interface to the Redland RDF C library, described at . In brief, RDF provides a structured graph consisting of Statements composed of Subject, Predicate, and Object Nodes. Package: r-cran-redm Architecture: arm64 Version: 1.15.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1645 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 938200 MD5sum: 35df2d6335f1080e2b1e56a409697281 SHA1: 70b5ca72f37e4d2548376338d47ce2176d677a36 SHA256: c64ea74cb0b974c8558615783e4b3e879009f3cd759450fb32e53752b0fab371 SHA512: 985afb882f1c12e40add2bf0c8ea3ea128f258c428dbd98f9530399e5e9fa29237afc3f99ef3656195bead1266246fbe9642df5bb325d1f73e50a30611697712 Homepage: https://cran.r-project.org/package=rEDM Description: CRAN Package 'rEDM' (Empirical Dynamic Modeling ('EDM')) An implementation of 'EDM' algorithms based on research software developed for internal use at the Sugihara Lab ('UCSD/SIO'). The package is implemented with 'Rcpp' wrappers around the 'cppEDM' library. It implements the 'simplex' projection method from Sugihara & May (1990) , the 'S-map' algorithm from Sugihara (1994) , convergent cross mapping described in Sugihara et al. (2012) , and, 'multiview embedding' described in Ye & Sugihara (2016) . Package: r-cran-redux Architecture: arm64 Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 378 Depends: libc6 (>= 2.17), libhiredis1.1.0 (>= 1.2.0), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-storr Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-sys, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-redux_1.1.5-1.ca2604.1_arm64.deb Size: 226046 MD5sum: d16ad07a5a725dd79431ff0039bd0bcb SHA1: 55afef535862441533047dae8b536488a9188af8 SHA256: 791e34c897f3633e5232f0a3941b086842420eceaee424cc1c40405ccbccec1f SHA512: 5cb1992f6b63092af03d29a2ff57196b85d47927d6545c7bb0aefaee7d342500ddf6dd18581ad455cbc88f28058cc2d5a7bcad664461d192ca8e30549f1f3a6d Homepage: https://cran.r-project.org/package=redux Description: CRAN Package 'redux' (R Bindings to 'hiredis') A 'hiredis' wrapper that includes support for transactions, pipelining, blocking subscription, serialisation of all keys and values, 'Redis' error handling with R errors. Includes an automatically generated 'R6' interface to the full 'hiredis' API. Generated functions are faithful to the 'hiredis' documentation while attempting to match R's argument semantics. Serialisation must be explicitly done by the user, but both binary and text-mode serialisation is supported. Package: r-cran-reems Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2951 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 842646 MD5sum: b13ae21a15925fefd7229ad5e8d1876f SHA1: d91b24f01b82c2584dd46bb0cb5e2c45c41612cb SHA256: fa1d207e22c0844389c973d372ce654a81354b0eeca5d6e01a3098723b6e60ba SHA512: 3b80e6247f2bcae3180c3a52b82bc250e8b3eaed68e5c215a6de9c7e24f0f1b0e1a3c59b1508bb04bc7208a9e111b1a883bdad0381093e9654a685cc0cf1b113 Homepage: https://cran.r-project.org/package=reems Description: CRAN Package 'reems' (Estimating Effective Migration Surfaces from Single NucleotidePolymorphism Data) Wrapper and plotting utilities for the spatial population genetics tool 'EEMS' (Estimated Effective Migration Surfaces) for SNP (Single Nucleotide Polymorphism) data, originally provided as a command-line tool written in 'C++' together with an accompanying 'R' package for plotting the output of the 'EEMS' tool itself (). There are four main motivations for offering this to 'R' users as a package. Firstly, to remove the installation and configuration burden for the 'EEMS' command-line tool, which relies on manually installed 'Boost' and 'Eigen' system libraries and configuring their location; secondly, to streamline the workflow by having a singe environment (the 'R' system) for the entire analysis rather than a file-based command-line executable whose output files are then to be imported and analysed by a separate 'R' script; thirdly, to make the input formats compatible with other, 'R'-based spatial population genetics tools such as the 'ConStruct' package; and lastly, to allow for easily running several chains in parallel and combining them for plotting and further analysis. The package also adds more intuitive, streamlined tooling around creating more complex habitats. The method of estimating effective migration surfaces was first described by Petkova, D., Novembre, J. & Stephens, M. (2016) . Package: r-cran-refbasedmi Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 859 Depends: libc6 (>= 2.38), 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_arm64.deb Size: 435966 MD5sum: 3c05ed44cb37b57753a42a5eaa4aa8f1 SHA1: c6614b5b57b9492d28435a6fdb446f2f058326fd SHA256: 5cf67eff4bcdf55f74d97be9e990e4c24f56ddfbbddf48d596d124fb3abb163f SHA512: 74de5ce95a83591b62e88fca828693d02bbb9afdb928c6175ca54389fa7ee3dad1b8929c26ae1dfd33dcc4a79252c603b9951c2ad47b2465bd72e77ebc4407c7 Homepage: https://cran.r-project.org/package=RefBasedMI Description: CRAN Package 'RefBasedMI' (Reference-Based Imputation for Longitudinal Clinical Trials withProtocol Deviation) Imputation of missing numerical outcomes for a longitudinal trial with protocol deviations. The package uses distinct treatment arm-based assumptions for the unobserved data, following the general algorithm of Carpenter, Roger, and Kenward (2013) , and the causal model of White, Royes and Best (2020) . Sensitivity analyses to departures from these assumptions can be done by the Delta method of Roger. The program uses the same algorithm as the 'mimix' 'Stata' package written by Suzie Cro, with additional coding for the causal model and delta method. The reference-based methods are jump to reference (J2R), copy increments in reference (CIR), copy reference (CR), and the causal model, all of which must specify the reference treatment arm. Other methods are missing at random (MAR) and the last mean carried forward (LMCF). Individual-specific imputation methods (and their reference groups) can be specified. Package: r-cran-refinr Architecture: arm64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 364 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 127184 MD5sum: a9c578846408d8f1ead238b53336d244 SHA1: ab0c3ec308649bb647eee2cae6d62de0877b61f3 SHA256: ae9b914c6c45e4c2317d17aa50565ce03911a55bb43ac4a85fb71ffa14aa2c4f SHA512: c0172fa75297f7c98094021be6a87838b4ba0237eadb796e0eab6460940ef09512a147050655aa75c421f854b3edce3176fd76282e6c534bdab6bf6d45b64129 Homepage: https://cran.r-project.org/package=refinr Description: CRAN Package 'refinr' (Cluster and Merge Similar Values Within a Character Vector) These functions take a character vector as input, identify and cluster similar values, and then merge clusters together so their values become identical. The functions are an implementation of the key collision and ngram fingerprint algorithms from the open source tool Open Refine . More info on key collision and ngram fingerprint can be found here . Package: r-cran-registr Architecture: arm64 Version: 2.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2417 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1666950 MD5sum: a3b4240a184e4cf9b9c62e4bc7e68eba SHA1: a90be119f8660d350f411330c81fbdcf9db8c5d1 SHA256: 34fa6ec826d7a27942b8a06c33f105e4fb7e1e992f40f040f189815bd3d3901b SHA512: e33839fef0e446f9e5c82fc78c8b36380205d193dd1fd92253462faf0846baedfad79d48131e4ddc92616956a3a9cf8b559bb73545d2db9a15674a047641bbed Homepage: https://cran.r-project.org/package=registr Description: CRAN Package 'registr' (Curve Registration for Exponential Family Functional Data) A method for performing joint registration and functional principal component analysis for curves (functional data) that are generated from exponential family distributions. This mainly implements the algorithms described in 'Wrobel et al. (2019)' and further adapts them to potentially incomplete curves where (some) curves are not observed from the beginning and/or until the end of the common domain. Curve registration can be used to better understand patterns in functional data by separating curves into phase and amplitude variability. This software handles both binary and continuous functional data, and is especially applicable in accelerometry and wearable technology. Package: r-cran-reglogit Architecture: arm64 Version: 1.2-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: libc6 (>= 2.29), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-boot, r-cran-matrix Suggests: r-cran-plgp Filename: pool/dists/resolute/main/r-cran-reglogit_1.2-8-1.ca2604.1_arm64.deb Size: 98406 MD5sum: f00fe9cb764e008cb5a48ee7057ed75d SHA1: b203fbc45324e13cd0833bbe5e184108f2f6b250 SHA256: d3490ad424a9c623b0fb30adecdac9fcfd631fe7d1ca4cbb2f6ea539d23d7862 SHA512: 4be6fed2386c698c005c0de526c06c45705f9708a05dc523ab4ac864ba134ae84bce8ecf9e6febce8a423a80ed83b8b40c0f53c194a06bb610cf280967a6637b Homepage: https://cran.r-project.org/package=reglogit Description: CRAN Package 'reglogit' (Simulation-Based Regularized Logistic Regression) Regularized (polychotomous) logistic regression by Gibbs sampling. The package implements subtly different MCMC schemes with varying efficiency depending on the data type (binary v. binomial, say) and the desired estimator (regularized maximum likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a unified interface. For details, see Gramacy & Polson (2012 ). Package: r-cran-regmed Architecture: arm64 Version: 2.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 972 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 607820 MD5sum: 986d2bb472e2439a6ba8e086e43b023f SHA1: e8f6615295abaa32202daf7ff50c582a7fefb4f2 SHA256: d8663dda5d8c7597abea5a341455018cf5b6b9047fda2d9e1b2e9e44ba29eb64 SHA512: 25aa5abfd76c70f9afc31d083869421ba91accd05a8e2b264b24952473eb046801cf3ad2e254d44c7e7f9bbdf61c54a2615e4600a586ea4eb250990036c1a32c Homepage: https://cran.r-project.org/package=regmed Description: CRAN Package 'regmed' (Regularized Mediation Analysis) Mediation analysis for multiple mediators by penalized structural equation models with different types of penalties depending on whether there are multiple mediators and only one exposure and one outcome variable (using sparse group lasso) or multiple exposures, multiple mediators, and multiple outcome variables (using lasso, L1, penalties). Package: r-cran-regmhmm Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 162836 MD5sum: f862ec8befb98b1aad42cc6a88a5b2d0 SHA1: 6ad48787a77ea4adfded864da014e972289e2d5a SHA256: 269d62b8c2a40102c9cd2a7b7fbb4eaaf37bdacf4144817be2ea9b8df73fcbb3 SHA512: 4c64c103c12bb3ceb77c94906e2895050b84895a604cc1b5f52b3076777bdff0124ee99d15efe6ebfadbb9c19f84b6940e6b637135444eb79094564ed4a7b584 Homepage: https://cran.r-project.org/package=regmhmm Description: CRAN Package 'regmhmm' ('regmhmm' Fits Hidden Markov Models with Regularization) Designed for longitudinal data analysis using Hidden Markov Models (HMMs). Tailored for applications in healthcare, social sciences, and economics, the main emphasis of this package is on regularization techniques for fitting HMMs. Additionally, it provides an implementation for fitting HMMs without regularization, referencing Zucchini et al. (2017, ISBN:9781315372488). Package: r-cran-regnet Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2856 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 2657792 MD5sum: 229b63f04cef5b04b94340bad1511cf2 SHA1: 6b761683de56466b4f541ae86554d5c3a7b36400 SHA256: ee1864ab4d8dac28c8c0153bb0a3b7c65e2592ea09e5ddbc1aa6d05a646628da SHA512: bf1c4ccc6b222689ca892774d60bb39f4e7bab18f01ebdd0614c59d30010a522d78634cbf9be9adf091595550ed134c5a2654f371a028feddae0d41df3db7fae Homepage: https://cran.r-project.org/package=regnet Description: CRAN Package 'regnet' (Network-Based Regularization for Generalized Linear Models) Network-based regularization has achieved success in variable selection for high-dimensional biological data due to its ability to incorporate correlations among genomic features. This package provides procedures of network-based variable selection for generalized linear models (Ren et al. (2017) and Ren et al.(2019) ). Continuous, binary, and survival response are supported. Robust network-based methods are available for continuous and survival responses. Package: r-cran-regsem Architecture: arm64 Version: 1.9.5-1.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), 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_arm64.deb Size: 369668 MD5sum: cd10b5962b023b3bc7eacade1ba6d6e2 SHA1: e800b368645fe136e1b004703971d57fe393617e SHA256: da8fd1c7cb54eecf67296f25da86b6161154b7c8e7b0e9622bee7ca5bcf96f7e SHA512: 6387afc96dc57849aa62085f50441cbbee039f641aa1be87a2b9a0b76c6f33e5fa77dd0812b419e38e24b7431f695e82d38f1f50750f7d3cc0138b91917b0a25 Homepage: https://cran.r-project.org/package=regsem Description: CRAN Package 'regsem' (Regularized Structural Equation Modeling) Uses both ridge and lasso penalties (and extensions) to penalize specific parameters in structural equation models. The package offers additional cost functions, cross validation, and other extensions beyond traditional structural equation models. Also contains a function to perform exploratory mediation (XMed). Package: r-cran-rehh Architecture: arm64 Version: 3.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2383 Depends: libc6 (>= 2.17), libgomp1 (>= 6), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rehh.data Suggests: r-cran-ape, r-cran-bookdown, r-cran-data.table, r-cran-gap, r-cran-knitr, r-cran-qqman, r-cran-rmarkdown, r-cran-r.utils, r-cran-testthat, r-cran-vcfr Filename: pool/dists/resolute/main/r-cran-rehh_3.2.3-1.ca2604.1_arm64.deb Size: 1582844 MD5sum: 13b7f1072905f0d720d87c2a2edd7a03 SHA1: cb0c30f71b426a2bc0a06d74c21a7e03bc306148 SHA256: cb01c3379a537e4dd94d0b60a70b83921713c4a4cf1783ca4cf7d1f3f86e01de SHA512: ddf88c603094cdbf2af08522c5a27d1257ba3ce5784d9ef3cca1b7c22001e637ccfc35f8680cbc1fcc32c72b273fecb4ccc62e6c2b53537e025dd5387a213dc7 Homepage: https://cran.r-project.org/package=rehh Description: CRAN Package 'rehh' (Searching for Footprints of Selection using 'Extended HaplotypeHomozygosity' Based Tests) Population genetic data such as 'Single Nucleotide Polymorphisms' (SNPs) is often used to identify genomic regions that have been under recent natural or artificial selection and might provide clues about the molecular mechanisms of adaptation. One approach, the concept of an 'Extended Haplotype Homozygosity' (EHH), introduced by (Sabeti 2002) , has given rise to several statistics designed for whole genome scans. The package provides functions to compute three of these, namely: 'iHS' (Voight 2006) for detecting positive or 'Darwinian' selection within a single population as well as 'Rsb' (Tang 2007) and 'XP-EHH' (Sabeti 2007) , targeted at differential selection between two populations. Various plotting functions are included to facilitate visualization and interpretation of these statistics. Package: r-cran-reins Architecture: arm64 Version: 1.0.16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1785 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_arm64.deb Size: 1364168 MD5sum: 17c394ea0f11a95fac0743dd59888571 SHA1: e685e4e2ca52b4ff15eedcc1a2db862fb5daa30f SHA256: a9132bc567bd9d83ebbe602b5cd8e5bbb43742cfa951666798344c7ef51d6702 SHA512: 641dae15fd02960be838be1a031439ecbc4aa79222426649ae145860086e8653718a5ac784ab2355213c34b1cc5adc53873fd7f99e4b5d88b2a743649fc2780b Homepage: https://cran.r-project.org/package=ReIns Description: CRAN Package 'ReIns' (Functions from "Reinsurance: Actuarial and Statistical Aspects") Functions from the book "Reinsurance: Actuarial and Statistical Aspects" (2017) by Hansjoerg Albrecher, Jan Beirlant and Jef Teugels . Package: r-cran-relatedness Architecture: arm64 Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 206 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-relatedness_2.0-1.ca2604.1_arm64.deb Size: 108688 MD5sum: c9f3d82a5794834ecaa098ce6a96581a SHA1: c481e260b939768ed36352e1e333a835f8541a77 SHA256: 4461ed2efbf473763377232e1d67b75137015fb06420187ed4a237b4899a41d1 SHA512: cfdbba91ea65dbfe378e318276a60afcb434c1f7c18a055a1b77b720a531c7c5a45590497d94448ee83081cb7be0f09f05953d05e153f492b9d837fcd8f4e064 Homepage: https://cran.r-project.org/package=Relatedness Description: CRAN Package 'Relatedness' (Maximum Likelihood Estimation of Relatedness using EM Algorithm) Inference of relatedness coefficients from a bi-allelic genotype matrix using a Maximum Likelihood estimation, Laporte, F., Charcosset, A. and Mary-Huard, T. (2017) . Package: r-cran-relevent Architecture: arm64 Version: 1.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 158538 MD5sum: d50f10ee4d864131c4fb3e7df6dbabf4 SHA1: 986bfa097e39df895dac8dab2b1b90ba3cc3f930 SHA256: bd8aba2e97510d31dfb8611177ef2e05e7f268901e909d0ba5f0ba24eec7beac SHA512: 5a67209f13a36ca58139408851bab40229690f6c263f39836ae02f70be8e53aad8612b4bdb506826cc1b79a1df1364b80f04bf49de376975a6c76ad46c55c7ef Homepage: https://cran.r-project.org/package=relevent Description: CRAN Package 'relevent' (Relational Event Models) Tools to fit and simulate realizations from relational event models. Package: r-cran-relliptical Architecture: arm64 Version: 1.4.0-1.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), 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_arm64.deb Size: 238804 MD5sum: 685916609663802304898475b10cd036 SHA1: 38f9ba95c4db86574ed79fbabe3f024759a790b8 SHA256: a099f4f9c31334fd48a8248ddb76d5b7a9a44f8236b385f4a5719af38d30ce67 SHA512: 4d32a9613ace4052228432871de5f8b349148dfb596ed5b6fec957423b8f27b8247440476828c0f63e451faf0ee3a92cb7d8acbb1783523032a010912e564505 Homepage: https://cran.r-project.org/package=relliptical Description: CRAN Package 'relliptical' (The Truncated Elliptical Family of Distributions) It provides a function for random number generation from members of the truncated multivariate elliptical family of distributions, including truncated versions of the Normal, Student-t, Pearson type VII, Slash, Logistic, and related distributions. Additional distributions can be specified by supplying the density generating function. The package also computes first- and second-order moments, including the covariance matrix, for selected distributions. References used for this package: Galarza, C. E., Matos, L. A., Castro, L. M., & Lachos, V. H. (2022). Moments of the doubly truncated selection elliptical distributions with emphasis on the unified multivariate skew-t distribution. Journal of Multivariate Analysis, 189, 104944 ; Ho, H. J., Lin, T. I., Chen, H. Y., & Wang, W. L. (2012). Some results on the truncated multivariate t distribution. Journal of Statistical Planning and Inference, 142(1), 25-40 ; Valeriano, K. A., Galarza, C. E., & Matos, L. A. (2023). Moments and random number generation for the truncated elliptical family of distributions. Statistics and Computing, 33(1), 32 . Package: r-cran-relsim Architecture: arm64 Version: 1.0.0-1.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_arm64.deb Size: 305450 MD5sum: 260c1a0aa83afbd8d74e1459094e567a SHA1: 82205cb6ac488395675ec7842f18527e089a5705 SHA256: 519080ec9ce5dd7d36875389ecad91568d0f846e0ff312d2e06397787d545f51 SHA512: e946ebd77a45f12230408564bc0354d7dc0ee52c3152ed091dc3a6d318a83087a64cb910916512f4d8f4bd83cf8e0a370add2a18eccb2a2f2da74fdab6d64fcc Homepage: https://cran.r-project.org/package=relSim Description: CRAN Package 'relSim' (Relative Simulator) A set of tools to explore the behaviour statistics used for forensic DNA interpretation when close relatives are involved. The package also offers some useful tools for exploring other forensic DNA situations. Package: r-cran-relsurv Architecture: arm64 Version: 2.3-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1022 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-ggplot2, r-cran-pammtools, r-cran-scales, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-relsurv_2.3-3-1.ca2604.1_arm64.deb Size: 853572 MD5sum: 5e38dda7a7e72dbdf433d0da1c05d141 SHA1: ed209038576031089b1b80dd019cc975d88a9327 SHA256: fe2cb2d6b617858c9b4e28d1398237aea85448f436e6b1161ba24eac3d7c6041 SHA512: 2f3d2096a1c2a5aa386c0be395b4b67b4c88d31b3416b942cb029ca91b61c2ca91d78356367ce17fa2513a9f22a6ae6473356928b3f9cefc577f4d154a0d67e5 Homepage: https://cran.r-project.org/package=relsurv Description: CRAN Package 'relsurv' (Relative Survival) Contains functions for analysing relative survival data, including nonparametric estimators of net (marginal relative) survival, relative survival ratio, crude mortality, methods for fitting and checking additive and multiplicative regression models, transformation approach, methods for dealing with population mortality tables. Work has been described in Pohar Perme, Pavlic (2018) . Package: r-cran-rem Architecture: arm64 Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 423 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_arm64.deb Size: 261630 MD5sum: 31b6f86dd409c5cddcf49d44ad1a246c SHA1: 6c756354e0080ed5f2661a523c81deaff0dbfb3f SHA256: 6f8c8f4d6135d1b9a1ad840f54cb5fc7740c128b8ef9d36215f958b840b5b305 SHA512: f1f320155a1d5da55d2e48d1608c34743b807451940f2d5d885ab021cccd5fa59fc8bd00399e61394fb06c4a0a72a6b8ee29dfcbe9ef9ab5d138a7f1f10cafa0 Homepage: https://cran.r-project.org/package=rem Description: CRAN Package 'rem' (Relational Event Models (REM)) Calculate endogenous network effects in event sequences and fit relational event models (REM): Using network event sequences (where each tie between a sender and a target in a network is time-stamped), REMs can measure how networks form and evolve over time. Endogenous patterns such as popularity effects, inertia, similarities, cycles or triads can be calculated and analyzed over time. Package: r-cran-rema Architecture: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 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_arm64.deb Size: 105376 MD5sum: 6c12912c369741e1f6fad2c852ea5f53 SHA1: e1cc01cda1da54f6f2e8535b013169608192bde5 SHA256: 3333938ababaa8fdca33a8a2b90f590474cfe6df88a2157df57a9669941a713f SHA512: 5f76d00326c5398d7de0b1d32c2b6549a01c22951905231c36e7ab8868d0fff699f233ab01b2aba6d78b62604b4655d34899584886d74df3f24e5738799ac47d Homepage: https://cran.r-project.org/package=rema Description: CRAN Package 'rema' (Rare Event Meta Analysis) The rema package implements a permutation-based approach for binary meta-analyses of 2x2 tables, founded on conditional logistic regression, that provides more reliable statistical tests when heterogeneity is observed in rare event data (Zabriskie et al. 2021 ). To adjust for the effect of heterogeneity, this method conditions on the sufficient statistic of a proxy for the heterogeneity effect as opposed to estimating the heterogeneity variance. While this results in the model not strictly falling under the random-effects framework, it is akin to a random-effects approach in that it assumes differences in variability due to treatment. Further, this method does not rely on large-sample approximations or continuity corrections for rare event data. This method uses the permutational distribution of the test statistic instead of asymptotic approximations for inference. The number of observed events drives the computation complexity for creating this permutational distribution. Accordingly, for this method to be computationally feasible, it should only be applied to meta-analyses with a relatively low number of observed events. To create this permutational distribution, a network algorithm, based on the work of Mehta et al. (1992) and Corcoran et al. (2001) , is employed using C++ and integrated into the package. Package: r-cran-remacor Architecture: arm64 Version: 0.0.20-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1285 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-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_arm64.deb Size: 717798 MD5sum: cbd9dba1f80f51ba70449b53c8b06eff SHA1: b97dbd8221a529ed0647b9e217a462c8e774e540 SHA256: b9ff93349b10e29076e018cb4e6cbd0f20fabe55bcd8a3efcb5d404c554c9abe SHA512: 9c4cfe894ff1fa8bac57a2449d6f2d1074a42a1526a0bcad1ce54aa1ff6d7e08b788afb21830ffcd14338f18785c8bb4cf3127a928f0607e19652cb6c431e40b Homepage: https://cran.r-project.org/package=remaCor Description: CRAN Package 'remaCor' (Random Effects Meta-Analysis for Correlated Test Statistics) Meta-analysis is widely used to summarize estimated effects sizes across multiple statistical tests. Standard fixed and random effect meta-analysis methods assume that the estimated of the effect sizes are statistically independent. Here we relax this assumption and enable meta-analysis when the correlation matrix between effect size estimates is known. Fixed effect meta-analysis uses the method of Lin and Sullivan (2009) , and random effects meta-analysis uses the method of Han, et al. . Package: r-cran-remify Architecture: arm64 Version: 4.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4487 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 1531588 MD5sum: fd4aa4dcd8f29b8152959b5c37090834 SHA1: 8ea46a57f0ae3779c7735a9c3feb0a9960a0e1c2 SHA256: b07f0c0f40d60110b74d5464fb3da3728eadc62d93d652ff236e7c113d3aaa5e SHA512: 481494454f28c3116beb8662f7f1f618b08e040c57ed24cc390dd381ed99411612ae7abbfdac73dfdd2e5f878acf70c2f732b8612a0c99c2c9c69388e39bb787 Homepage: https://cran.r-project.org/package=remify Description: CRAN Package 'remify' (Processing and Transforming Relational Event History Data) Efficiently processes relational event history data and transforms them into formats suitable for other packages. The primary objective of this package is to convert event history data into a format that integrates with the packages in 'remverse' and is compatible with various analytical tools (e.g., computing network statistics, estimating tie-oriented or actor-oriented social network models). Second, it can also transform the data into formats compatible with other packages out of 'remverse'. The package processes the data for two types of temporal social network models: tie-oriented modeling framework (Butts, C., 2008, ) and actor-oriented modeling framework (Stadtfeld, C., & Block, P., 2017, ). Package: r-cran-remote Architecture: arm64 Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2372 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2050188 MD5sum: 89ae84da6c94db7faa801fb70cb1a5c9 SHA1: 29817ebd0a8427031f24327a790ed4cd4a387cd8 SHA256: cb7e7d1eea235bd4416db86530e2654b83b92ffac17619c5c7f4b7ee9af205cc SHA512: 541eb4d9def63cb2b9afef290c0812c26e20f5a4d30fff95ca460b9a93bd0ffa57784246937c23c6f386954d10cdbfe0df375dbfdc1ec1e8b6c4e0700bba5d85 Homepage: https://cran.r-project.org/package=remote Description: CRAN Package 'remote' (Empirical Orthogonal Teleconnections in R) Empirical orthogonal teleconnections in R. 'remote' is short for 'R(-based) EMpirical Orthogonal TEleconnections'. It implements a collection of functions to facilitate empirical orthogonal teleconnection analysis. Empirical Orthogonal Teleconnections (EOTs) denote a regression based approach to decompose spatio-temporal fields into a set of independent orthogonal patterns. They are quite similar to Empirical Orthogonal Functions (EOFs) with EOTs producing less abstract results. In contrast to EOFs, which are orthogonal in both space and time, EOT analysis produces patterns that are orthogonal in either space or time. Package: r-cran-remoteparts Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1796 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_arm64.deb Size: 1396372 MD5sum: 17551dcd031ae732eed4dac38b6c5a50 SHA1: 5c85d1076b063ad3d5f13cb5cd72a1797da59595 SHA256: dc7f497fe3464da06ec4e39deba359eef3851afac30c46fae8ab39629bb129ae SHA512: 1a138a6ac1d5369fe35caeb0f11cf1bb9c88b8a87f2b70ad41467e8c80bea8cfe4d34df43c5a0c6502fbf487093a3b11940811081d2c0e08ec7536f2fe05992f Homepage: https://cran.r-project.org/package=remotePARTS Description: CRAN Package 'remotePARTS' (Spatiotemporal Autoregression Analyses for Large Data Sets) These tools were created to test map-scale hypotheses about trends in large remotely sensed data sets but any data with spatial and temporal variation can be analyzed. Tests are conducted using the PARTS method for analyzing spatially autocorrelated time series (Ives et al., 2021: ). The method's unique approach can handle extremely large data sets that other spatiotemporal models cannot, while still appropriately accounting for spatial and temporal autocorrelation. This is done by partitioning the data into smaller chunks, analyzing chunks separately and then combining the separate analyses into a single, correlated test of the map-scale hypotheses. Package: r-cran-remstats Architecture: arm64 Version: 4.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2328 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_arm64.deb Size: 960692 MD5sum: e11298d114b91ce28026bd04e720ce28 SHA1: 07c0bdf43584ad6878a84584eed2b4598f8091c6 SHA256: bb4d0b3e894edbc482f87379fcd759b8462aed18c99e05aca89d943dfd27e7e1 SHA512: 1844039bc0ce842ed8efbb69093a9b20bbac6c40b3af13e05beba04b2aa114d9faf66028ebf91dc64452ee0ca3cd31187e54a5b521c5efd802fb7f02be17b1ee Homepage: https://cran.r-project.org/package=remstats Description: CRAN Package 'remstats' (Computes Statistics for Relational Event History Data) Computes a variety of statistics for relational event models (Meijerink et al., 2023, ). Relational event models enable researchers to investigate exogenous and endogenous factors, and interactions, influencing the evolution of a time-ordered sequence of events. These models are categorized into tie-oriented models (Butts, C., 2008, ), where the probability of a dyad interacting next is modeled in a single step, and actor-oriented models (Stadtfeld, C., & Block, P., 2017, ), which first model the probability of a sender initiating an interaction and subsequently the probability of the sender's choice of receiver. The package is designed to compute a variety of statistics that summarize exogenous and endogenous influences on the event stream for both types of models. Package: r-cran-remstimate Architecture: arm64 Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3813 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-rcpp, r-cran-remify, r-cran-remstats, r-cran-trust, r-cran-mvnfast, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-survival Filename: pool/dists/resolute/main/r-cran-remstimate_3.0.0-1.ca2604.1_arm64.deb Size: 1567740 MD5sum: 67b4799635d45088a27e69f67a890f73 SHA1: 79fa2cf9f185bb075bcbff04282d6bf99be099dd SHA256: 7afc38a44e2f745f8169ce97870986685f4faf870d4706cf7c20a794e7d916b2 SHA512: 314970408886ea1d96720cb978fa4150cd3a834eb2d00c082eb19ff5494b0e03d8898d634f1654c6a7acdc23e5e893351e95482b4eb5fb081f4e335945c0a4db Homepage: https://cran.r-project.org/package=remstimate Description: CRAN Package 'remstimate' (Optimization Frameworks for Tie-Oriented and Actor-OrientedRelational Event Models) A comprehensive set of tools designed for optimizing likelihood within a tie-oriented (Butts, C., 2008, ) or an actor-oriented modelling framework (Stadtfeld, C., & Block, P., 2017, ) in relational event networks. The package accommodates both frequentist and Bayesian approaches. Maximum Likelihood Optimization (MLE) is supported. Bayesian estimation is done via Hamiltonian Monte Carlo (HMC). Package: r-cran-remulate Architecture: arm64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 505 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_arm64.deb Size: 257376 MD5sum: 90b7acb0b94a439b26b5753d1db5721f SHA1: d099c571a5c33d12f804e88d4d8ecfdb7a893b71 SHA256: afe77db3a54b70ee39310c02a0e85586406698eb01993f15fec1178ec64127ea SHA512: a69f716f5aa099e19dd64f7c377da151891e7cd6c5d5076d177eff7154fb7e65c177c18c35ea4d4082a0119cfd1eddb88903b5c8546fb6a9bbad23f7116a7286 Homepage: https://cran.r-project.org/package=remulate Description: CRAN Package 'remulate' (Simulate Dynamic Networks from Relational Event Models) Model based simulation of dynamic networks under tie-oriented (Butts, C., 2008, ) and actor-oriented (Stadtfeld, C., & Block, P., 2017, ) relational event models. Supports simulation from a variety of relational event model extensions, including temporal variability in effects, heterogeneity through dyadic latent class relational event models (DLC-REM), random effects, blockmodels, and memory decay in relational event models (Lakdawala, R., 2024 ). The development of this package was supported by a Vidi Grant (452-17-006) awarded by the Netherlands Organization for Scientific Research (NWO) Grant and an ERC Starting Grant (758791). Package: r-cran-rena Architecture: arm64 Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1109 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 801580 MD5sum: c09fb37be4f4c9505085603622f0df44 SHA1: cd529ca3ce2b3bd579a253879a24f66101a2f397 SHA256: 7efdb9069abd2cebd43bb0c59e4dee73876fc00698f25d729e205d78804219a6 SHA512: 311bd88ca6fb1c7f1cb6041809a33b86cae3114c9bbff76702c2bab90012c2ea07f17541b45db582f0912b4f91963175b24203480355313f42fabbef7e174fb7 Homepage: https://cran.r-project.org/package=rENA Description: CRAN Package 'rENA' (Epistemic Network Analysis) ENA (Shaffer, D. 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Package: r-cran-rendo Architecture: arm64 Version: 2.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1564 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-formula, r-cran-optimx, r-cran-mvtnorm, r-cran-aer, r-cran-matrix, r-cran-lme4, r-cran-reformulas, r-cran-data.table, r-cran-corpcor, r-cran-rcpp, r-cran-lmtest, r-cran-copula, r-cran-ks, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-covr, r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-rendo_2.5.0-1.ca2604.1_arm64.deb Size: 1433614 MD5sum: 03ebc4f1d4f71d4f497370ec846ad621 SHA1: fa53da46352708fb73c87b616bcd0d22d55d84a1 SHA256: b43679412b1a31dbd9fe1cb361491a38acd7b17a37db9e899fca0e4c12c9af5c SHA512: 100c68ce4304ab6045f72f448c34b07e1cc97a39a3154eddb37e7a192634cdfc6a7fb9dc7419754c6624c52f441b8f7b2b52bab9f4857e349b6076d5b6df7de9 Homepage: https://cran.r-project.org/package=REndo Description: CRAN Package 'REndo' (Fitting Linear Models with Endogenous Regressors using LatentInstrumental Variables) Fits linear models with endogenous regressor using latent instrumental variable approaches. The methods included in the package are Lewbel's (1997) higher moments approach as well as Lewbel's (2012) heteroscedasticity approach, Park and Gupta's (2012) joint estimation method that uses Gaussian copula and Kim and Frees's (2007) multilevel generalized method of moment approach that deals with endogeneity in a multilevel setting. These are statistical techniques to address the endogeneity problem where no external instrumental variables are needed. See the publication related to this package in the Journal of Statistical Software for more details: . Note that with version 2.0.0 sweeping changes were introduced which greatly improve functionality and usability but break backwards compatibility. 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References include Lindsey's text books, JK Lindsey (2001) and JK Lindsey (1999) . 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Package: r-cran-representr Architecture: arm64 Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1434 Depends: libc6 (>= 2.17), 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-dplyr, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2 Filename: pool/dists/resolute/main/r-cran-representr_0.1.6-1.ca2604.1_arm64.deb Size: 1095500 MD5sum: c80a013a9e223b6fe58beb32d4bf1271 SHA1: 90bc399a474133ae1b2b317c2bcc6f47a5f0e969 SHA256: 1e3e8d193a2d073d4c5f6b59d4d5b24e2059acac7cc7a9d40d8e2d388917572b SHA512: 7fb361712ba3e5b72d3ec0c05fe56f4fc4cbac1853b277d3b365ea38a1ccaeba7da7533849f2591f8811592c79e6ce314ba30b4658fb9af330c4f8386d64fbaf Homepage: https://cran.r-project.org/package=representr Description: CRAN Package 'representr' (Create Representative Records After Entity Resolution) An implementation of Kaplan, Betancourt, Steorts (2022) that creates representative records for use in downstream tasks after entity resolution is performed. 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Package: r-cran-repsim Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 369 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-repsim_0.1.0-1.ca2604.1_arm64.deb Size: 151172 MD5sum: ab0f42a61840d1661e5108b2366eeb66 SHA1: f501f94db8e9f9c771fbd8ee5b37dfe5608698f4 SHA256: 61ae367358f4efb56a1b27e3cb72069197ed469a575e72a2e9370975db1e907f SHA512: 92d812c586d7aed9a72d6d682d5a98cc4484805f81fab1d27e252800cc8fe164a9a0f1f19b74b7308ae6c4efcf13a20ea9735074d5abb276463b5e21f3c1bc27 Homepage: https://cran.r-project.org/package=repsim Description: CRAN Package 'repsim' (Measures of Representational Similarity Across Models) Provides a collection of methods for quantifying representational similarity between learned features or multivariate data. The package offers an efficient 'C++' backend, designed for applications in machine learning, computational neuroscience, and multivariate statistics. See Klabunde et al. (2025) for a comprehensive overview of the topic. Package: r-cran-reqres Architecture: arm64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 571 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-stringi, r-cran-urltools, r-cran-brotli, r-cran-jsonlite, r-cran-xml2, r-cran-webutils, r-cran-cli, r-cran-rlang, r-cran-lifecycle, r-cran-base64enc, r-cran-sodium, r-cran-promises, r-cran-mirai, r-cran-otel Suggests: r-cran-fiery, r-cran-testthat, r-cran-covr, r-cran-keyring, r-cran-shiny Filename: pool/dists/resolute/main/r-cran-reqres_1.2.0-1.ca2604.1_arm64.deb Size: 455574 MD5sum: 73f1aaa7ff590b77beb870ed0440306a SHA1: 167bf59ed90ded40de4232ba980a1ee03393520b SHA256: c41d361b3dfb91dc995aa9fd14620af8202a2182c65e6f28105066a97b8d3c6d SHA512: 90f51e08784bc4afffad99bcb0f531549c8475be746a3dde727070cf08109fe677fafcfea2541066744055691199d0fa1a41e25227245b8682db231357e3841e Homepage: https://cran.r-project.org/package=reqres Description: CRAN Package 'reqres' (Powerful Classes for HTTP Requests and Responses) In order to facilitate parsing of http requests and creating appropriate responses this package provides two classes to handle a lot of the housekeeping involved in working with http exchanges. 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Package: r-cran-rereg Architecture: arm64 Version: 1.4.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 750 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bb, r-cran-nleqslv, r-cran-dfoptim, r-cran-optimx, r-cran-squarem, r-cran-survival, r-cran-directlabels, r-cran-ggplot2, r-cran-mass, r-cran-reda, r-cran-scam, r-cran-rcpp, r-cran-rootsolve, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-rereg_1.4.7-1.ca2604.1_arm64.deb Size: 450946 MD5sum: b7e644c5ffbb5cd15faf5cf47bfb7310 SHA1: c1535baa51e98bb163a9012c34e635908932e32b SHA256: 4fc0f3a64e2ee118adca7abbc1e77f19053250a15a91469850dfa3e581c7299f SHA512: e1ed51789b7c48335a0c33e1a1e829c755bcd6f00e579314e346b7b64886314ed760b93a955ced9983bc9b45508a766fdb74cd3565b1d7c250d4b4176f251844 Homepage: https://cran.r-project.org/package=reReg Description: CRAN Package 'reReg' (Recurrent Event Regression) A comprehensive collection of practical and easy-to-use tools for regression analysis of recurrent events, with or without the presence of a (possibly) informative terminal event described in Chiou et al. (2023) . The modeling framework is based on a joint frailty scale-change model, that includes models described in Wang et al. (2001) , Huang and Wang (2004) , Xu et al. (2017) , and Xu et al. (2019) as special cases. The implemented estimating procedure does not require any parametric assumption on the frailty distribution. The package also allows the users to specify different model forms for both the recurrent event process and the terminal event. Package: r-cran-resemble Architecture: arm64 Version: 3.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7103 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_arm64.deb Size: 3733260 MD5sum: a87898df7a65d669a86af9103457ff3a SHA1: eae3153383529528ed2da0b97186ec86d558cadf SHA256: 5de70ed39cf1aedba4f23080f80435dd59b1bd85055f47ee3beaeada9d3b0186 SHA512: 77f48393506d01349862e4cb63c36f2588ff67c937d82f755f6ec5647b189f3549d75c0a3eb4c0518a5a9d9d9824a744674ba2cad1260ba72e5915fda0c0c66a Homepage: https://cran.r-project.org/package=resemble Description: CRAN Package 'resemble' (Similarity Retrieval and Local Learning for SpectralChemometrics) Functions for dissimilarity analysis and machine learning in complex spectral data sets, including memory-based learning (MBL), optimal subset search and selection, and retrieval-based modelling with model libraries. Supports local learning, optimisation of spectral libraries, and ensemble prediction from precomputed models. Most of these functions are based on the methods presented in Ramirez-Lopez et al. (2013) . Package: r-cran-reservr Architecture: arm64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3844 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 2274494 MD5sum: 3267e6d64d9e08c2650af6b30f37d5f6 SHA1: a3319a652ff01d64985e801d2aec6f4029ab75b4 SHA256: e2b3f61f07a7b87f038a3e342ba56a077b4544275a41141c3a6f927271664dd9 SHA512: e303a74ee7edbf1d7c69e6cf1690920890ce8a853ca1487e9ec23c6b0d434cfd0e2eb8b440de2a360d54675fbe0adab9253e85d285ee0890399249636a0f79e2 Homepage: https://cran.r-project.org/package=reservr Description: CRAN Package 'reservr' (Fit Distributions and Neural Networks to Censored and TruncatedData) Define distribution families and fit them to interval-censored and interval-truncated data, where the truncation bounds may depend on the individual observation. The defined distributions feature density, probability, sampling and fitting methods as well as efficient implementations of the log-density log f(x) and log-probability log P(x0 <= X <= x1) for use in 'TensorFlow' neural networks via the 'tensorflow' package. Allows training parametric neural networks on interval-censored and interval-truncated data with flexible parameterization. Applications include Claims Development in Non-Life Insurance, e.g. modelling reporting delay distributions from incomplete data, see Bücher, Rosenstock (2022) . Package: r-cran-resevol Architecture: arm64 Version: 0.4.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2186 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-resevol_0.4.0.2-1.ca2604.1_arm64.deb Size: 1102502 MD5sum: 75b2871f9d34ff31c9f309b1e75bd4b2 SHA1: 55901b746de09b8c52da1ff62184a2f1647b9a88 SHA256: 4e18f3ac40480399d0a9dc38aa5658dae1cb35b7c6f1e6218d87fa97330e168a SHA512: 38dbde362f9d28c967eda549a52d477154cbef63393c792d92591d573c04301dc86434f3a76603dd8fc7c1455d4dfeaecb58eff80bd470e146c5a38b9738d603 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. Package: r-cran-reshape2 Architecture: arm64 Version: 1.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 Depends: libc6 (>= 2.17), 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-stringr Suggests: r-cran-covr, r-cran-lattice, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-reshape2_1.4.5-1.ca2604.1_arm64.deb Size: 112278 MD5sum: 365983a7b128259e718b35be54032ff7 SHA1: 66ab1bfdfb39278eaeb429db47d95556943840d2 SHA256: 1eed5fbd52a4cfabdc791e663938b3ff77ff27ad5d511253257db5e6e0c2b68e SHA512: 9cf5f360e968d2c0e6be7c7ec08b7e8623ed3a352608702c23690476c6b3e1726fc28920c098f5918ce4182f614f7467f43ff07393b6bc4781b0ee4441a91c28 Homepage: https://cran.r-project.org/package=reshape2 Description: CRAN Package 'reshape2' (Flexibly Reshape Data: A Reboot of the Reshape Package) Flexibly restructure and aggregate data using just two functions: melt and 'dcast' (or 'acast'). 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See for more details about the available data. Also provides facilities to plot and analyse downloaded data, such as computing the sea-state parameters from both the 1D and 2D surface elevation variance spectral density. Package: r-cran-respiranalyzer Architecture: arm64 Version: 1.0.2-1.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-signal, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-respiranalyzer_1.0.2-1.ca2604.1_arm64.deb Size: 469966 MD5sum: 58c87d1ced48ffa12600e7f8e1bb30a6 SHA1: 2542a6cdf41e786e1960590f7fbecb7768630400 SHA256: 29ab07019ecc3d89ff245b9141869ecbe43696ef7c22b0b3a6922a9ecf7e76cb SHA512: 4f88bf9b244a39a5b2353548f20ef7416bdbb295f0442ff095f4e1a1740dc786f6dc372bea6b3889b37eb9fd10a7615763c6989b6f32b4a465e71d70b66994e8 Homepage: https://cran.r-project.org/package=RespirAnalyzer Description: CRAN Package 'RespirAnalyzer' (Analysis Functions of Respiratory Data) Provides functions for the complete analysis of respiratory data. Consists of a set of functions that allow to preprocessing respiratory data, calculate both regular statistics and nonlinear statistics, conduct group comparison and visualize the results. Especially, Power Spectral Density ('PSD') (A. Eke (2000) ), 'MultiScale Entropy(MSE)' ('Madalena Costa(2002)' ) and 'MultiFractal Detrended Fluctuation Analysis(MFDFA)' ('Jan W.Kantelhardt' (2002) ) were applied for the analysis of respiratory data. Package: r-cran-restfulr Architecture: arm64 Version: 0.0.16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 593 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xml, r-cran-rcurl, r-cran-rjson, r-bioc-s4vectors, r-cran-yaml Suggests: r-cran-getpass, r-cran-rsolr, r-cran-runit Filename: pool/dists/resolute/main/r-cran-restfulr_0.0.16-1.ca2604.1_arm64.deb Size: 392864 MD5sum: 117a8515c121a9157012cd25c31279bb SHA1: 1067090bd2e4c3edd54762d8ac5c6a65ab0fa51f SHA256: ff62db4278d8d36f7b4715846e10cbdb2d4242777d93a97f76019089d6a3fab1 SHA512: 2d5113f35f7a0b17e9075891ed9e565e29f661b7ba5e57c6eac3b37b49aae3632ff6551d95b47ba0223c29eddd7ad5ef271c0fb2b223554a26644b7167390990 Homepage: https://cran.r-project.org/package=restfulr Description: CRAN Package 'restfulr' (R Interface to RESTful Web Services) Models a RESTful service as if it were a nested R list. 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Package: r-cran-rethnicity Architecture: arm64 Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4922 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-cli, r-cran-rlang, r-cran-rcppeigen, r-cran-rcppthread Suggests: r-cran-pak, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-rethnicity_0.2.7-1.ca2604.1_arm64.deb Size: 1728126 MD5sum: 51d629312042974c127278fb03f10b2c SHA1: ec25ab7589d111fa600477957e26aab5db05258a SHA256: 11e3668937f299800625eadca6f5833e274ca5675f158c20869d1312dd9de662 SHA512: 05c690598f894654865202659d4788d9f2e98b3b603ef624b41f5bb1ca76911cabeda8453917a21b01d102575f039de7871df5f27ad9c31a104ad723228924a7 Homepage: https://cran.r-project.org/package=rethnicity Description: CRAN Package 'rethnicity' (Predicting Ethnic Group from Names) Implementation of the race/ethnicity prediction method, described in "rethnicity: An R package for predicting ethnicity from names" by Fangzhou Xie (2022) and "Rethnicity: Predicting Ethnicity from Names" by Fangzhou Xie (2021) . Package: r-cran-reticulate Architecture: arm64 Version: 1.46.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2984 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-rcpptoml, r-cran-here, r-cran-jsonlite, r-cran-png, r-cran-rappdirs, r-cran-rlang, r-cran-withr Suggests: r-cran-callr, r-cran-knitr, r-cran-glue, r-cran-cli, r-cran-rmarkdown, r-cran-pillar, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-reticulate_1.46.0-1.ca2604.1_arm64.deb Size: 1858814 MD5sum: 879960f294613423b048f0c95cf6f8a6 SHA1: 46685204a1512717adcfc17932003fd92f15d547 SHA256: d6c4287ee14399bb8125e7065769ffbb971250c222883afd386b1a40ee545715 SHA512: 67524b55749168022bc3284e5d988e48e62bd248e3751e69ed96424dd1eba05e583735af23216bc9331e14db37724abd9f024e3e49152c84a47ac2839d0676b6 Homepage: https://cran.r-project.org/package=reticulate Description: CRAN Package 'reticulate' (Interface to 'Python') Interface to 'Python' modules, classes, and functions. When calling into 'Python', R data types are automatically converted to their equivalent 'Python' types. When values are returned from 'Python' to R they are converted back to R types. Compatible with all versions of 'Python' >= 2.7. 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It can estimate the position of a point on the intact adult retina to within 8 degrees of arc (3.6% of nasotemporal axis). The coordinates in reconstructed retinae can be transformed to visuotopic coordinates. For more details see Sterratt, D. C., Lyngholm, D., Willshaw, D. J. and Thompson, I. D. (2013) . Package: r-cran-revamp Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2528 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_arm64.deb Size: 409248 MD5sum: f3e10212febb5beb1127a63e992dd49a SHA1: 6d2d0e8883a2ee66a1386d8c48112195a0a83ac3 SHA256: ad016e48ba00c98e50724d1a2d8cceccb2509c6a0c3fd6523113a66754d43ed1 SHA512: a2f89604d0be6b173be73c5f590fc78bd2040f61cb05980f362d70494aa7886257b7b434b9d6ddd1f7c448cebcbdf6cae51bf8dbbec5b487664408654d720455 Homepage: https://cran.r-project.org/package=ReVAMP Description: CRAN Package 'ReVAMP' (Interface to 'Vamp' Audio Analysis Plugins) Provides an interface to the 'Vamp' audio analysis plugin system developed by Queen Mary University of London's Centre for Digital Music. Enables loading and running 'Vamp' plugins for various audio analysis tasks including tempo detection, onset detection, spectral analysis, and audio feature extraction. Supports mono and stereo audio with automatic channel adaptation and domain conversion. Package: r-cran-revdbayes Architecture: arm64 Version: 1.5.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1590 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 800338 MD5sum: 4c6fe59d0d03d1c96f1f686dd965068f SHA1: 346031b2eac62d2ec6d863cde7df4c72f16043d1 SHA256: 1fa626efb9133a9eef2cf3af2e41c206aed6c042d4adb5bec954fac6eb2158f2 SHA512: 7278bbbe9f5fb7e6cea4bed5a8bc6b84ebc485edd4016d837925e414d8c37dad704dc9ae7f23a9080a285c9b43efeb9f7c0a3aaf16e2c4c95f8ab5d00741abd2 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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Package: r-cran-revealedprefs Architecture: arm64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 361 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-pso, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-revealedprefs_0.4.2-1.ca2604.1_arm64.deb Size: 138750 MD5sum: c906f2a7d165216b2278aaa7f4c031e9 SHA1: 4eb0053bb7aef6ab9cbb9779e57d15260cf4e495 SHA256: e9db7ce3b7b44e0089d5d456a3e0bc6f9243f7129427b7a0dfccc1262379af3b SHA512: 5a978f045af5a4d9b941a1189f7e7f8048b313c0f3cb36b449bd178e511484763cb11af74db7eeb3bc42bbc47a0e953ba6c0e4cd285fba7a31f18c469393ff05 Homepage: https://cran.r-project.org/package=revealedPrefs Description: CRAN Package 'revealedPrefs' (Revealed Preferences and Microeconomic Rationality) Computation of (direct and indirect) revealed preferences, fast non-parametric tests of rationality axioms (WARP, SARP, GARP), simulation of axiom-consistent data, and detection of axiom-consistent subpopulations. Rationality tests follow Varian (1982) , axiom-consistent subpopulations follow Crawford and Pendakur (2012) . Package: r-cran-revss Architecture: arm64 Version: 3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-revss_3.1.0-1.ca2604.1_arm64.deb Size: 64912 MD5sum: c20e5bd8044ab8f324456ab38e75e419 SHA1: da02bb5f959d8732c20c14432031d8760b06659e SHA256: 042e7bf93a69a04c70ea81678814096ad6b448c8fcfdb593732247e8d2d22402 SHA512: 07ee221227a5e4a2f5d104787d1b511c4ba00413d50a5bb754453c947b25d078a36f9cc30d87b6360c86379eefcf6694b322f53de06137b0815a02b1b622f36f Homepage: https://cran.r-project.org/package=revss Description: CRAN Package 'revss' (Robust Estimation in Very Small Samples) Implements and enhances the estimation techniques described in Rousseeuw & Verboven (2002) for the location and scale of very small samples. Package: r-cran-rexpokit Architecture: arm64 Version: 0.26.6.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1350 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_arm64.deb Size: 377800 MD5sum: 8793007c87688687f6936c647f83a239 SHA1: 0863c2d7b41d150278652610284b17827b3db292 SHA256: 147af2043da7b6ecf2a5bdbe204b6241d0b6cabf97b452e4ccaed4ecd28584f0 SHA512: 03fd8cf608712e492da7460c48a88fd60b233a311bfe188c689bc53170bd5639edc57108468f3b7a645cbee1f220f75dec735dc40da879f6e14e03497c9870de Homepage: https://cran.r-project.org/package=rexpokit Description: CRAN Package 'rexpokit' (R Wrappers for EXPOKIT; Other Matrix Functions) Wraps some of the matrix exponentiation utilities from EXPOKIT (), a FORTRAN library that is widely recommended for matrix exponentiation (Sidje RB, 1998. "Expokit: A Software Package for Computing Matrix Exponentials." ACM Trans. Math. Softw. 24(1): 130-156). EXPOKIT includes functions for exponentiating both small, dense matrices, and large, sparse matrices (in sparse matrices, most of the cells have value 0). Rapid matrix exponentiation is useful in phylogenetics when we have a large number of states (as we do when we are inferring the history of transitions between the possible geographic ranges of a species), but is probably useful in other ways as well. NOTE: In case FORTRAN checks temporarily get rexpokit archived on CRAN, see archived binaries at GitHub in: nmatzke/Matzke_R_binaries (binaries install without compilation of source code). 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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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Also, C++ header files are contained that implement elementary operations on manifolds such as Sphere, Grassmann, and others. See Bhattacharya and Bhattacharya (2012) if you are interested in statistics on manifolds, and Absil et al (2007, ISBN:9780691132983) on computational aspects of optimization on matrix manifolds. Package: r-cran-riemtan Architecture: arm64 Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1050 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-arrow, r-cran-future, r-cran-furrr, r-cran-jsonlite, r-cran-matrix, r-cran-r6, r-cran-purrr, r-cran-mass, r-cran-matrixstats, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-progressr, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-riemtan_0.2.5-1.ca2604.1_arm64.deb Size: 546966 MD5sum: c76f956ae60fb1f4612a00323cee094e SHA1: b5bd6d682a52e4266e84aa9f447c118a11c2c0c2 SHA256: fa8385c365d6e87526b3abc6b3ef7ab02fb815d29ac8cd5497831624d9ac0b40 SHA512: 2d7fca2d3044aedad6e09456e6b4fc91a4ef5f6a73de1f9e550c79e83961fe7460480210b81d97c2ab368dd35284373a11b188960e818be666b1b179c52fddab Homepage: https://cran.r-project.org/package=riemtan Description: CRAN Package 'riemtan' (Riemannian Metrics for Symmetric Positive Definite Matrices) Implements various Riemannian metrics for symmetric positive definite matrices, including AIRM (Affine Invariant Riemannian Metric, ), Log-Euclidean (), Euclidean, Log-Cholesky (), and Bures-Wasserstein metrics (). 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Package: r-cran-rim Architecture: arm64 Version: 0.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1643 Depends: libc6 (>= 2.17), 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-knitr, r-cran-globaloptions Suggests: r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rim_0.8.1-1.ca2604.1_arm64.deb Size: 1028088 MD5sum: 717228734490ebb6809ccc2c49a921a2 SHA1: b3f89fb5cebc493f68564e2d286660113e419b6f SHA256: 19e3d47230294fbe3660b2b522d29273f58b2d8d9fee5be4e5aac96b4efecd91 SHA512: c9faac5f9b395d6ba705380e548e130f8a950573ca026bc98a85a2d9a88c1d26e027512081ee5e586e3b403b8e9842a20adc7bf56d00b78b7551518b4a5698f9 Homepage: https://cran.r-project.org/package=rim Description: CRAN Package 'rim' (Interface to 'Maxima', Enabling Symbolic Computation) An interface to the powerful and fairly complete computer algebra system 'Maxima'. It can be used to start and control 'Maxima' from within R by entering 'Maxima' commands. Results from 'Maxima' can be parsed and evaluated in R. It facilitates outputting results from 'Maxima' in 'LaTeX' and 'MathML'. 2D and 3D plots can be displayed directly. This package also registers a 'knitr'-engine enabling 'Maxima' code chunks to be written in 'RMarkdown' documents. Package: r-cran-ring Architecture: arm64 Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 789 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ring_1.0.8-1.ca2604.1_arm64.deb Size: 376182 MD5sum: b1dfc3abdb1f889091543aa8a4802324 SHA1: 991b54ce235e19d52af362a159a33b82d8c7d20f SHA256: 3a0fecdd882680bec12df53d6fb72fab3ddd0a67f721a00a74e2f0b00a7a2c9a SHA512: 88e363451c0365262302f5d684a258c0edabfc1a9c91e93652579a3408f36f8904e96e5acbe5a61caa4d72eaad45c0765ccc523ee815a4da51566c5494b63702 Homepage: https://cran.r-project.org/package=ring Description: CRAN Package 'ring' (Circular / Ring Buffers) Circular / ring buffers in R and C. There are a couple of different buffers here with different implementations that represent different trade-offs. Package: r-cran-rinside Architecture: arm64 Version: 0.2.19-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1103 Depends: libc6 (>= 2.17), 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-rinside_0.2.19-1.ca2604.1_arm64.deb Size: 135030 MD5sum: 432df33d5f74158a51b69a4180d75ffa SHA1: f0e91a8b7a703d75ba7c66b8d0e4e67f1d9ed801 SHA256: 2f6a8b36ec890a573579a4011b1e9cfe151f060598395accc6a65695faba7a54 SHA512: 914b1eec5ece4ff356827dd07e15e94187ec16c1f82aed9e94aebd20ba4864e4bbb680977f4189cb1d90dd8fed32e56a8af7a5d46d3fc81429191988fd421302 Homepage: https://cran.r-project.org/package=RInside Description: CRAN Package 'RInside' (C++ Classes to Embed R in C++ (and C) Applications) C++ classes to embed R in C++ (and C) applications A C++ class providing the R interpreter is offered by this package making it easier to have "R inside" your C++ application. As R itself is embedded into your application, a shared library build of R is required. This works on Linux, OS X and even on Windows provided you use the same tools used to build R itself. Numerous examples are provided in the nine subdirectories of the examples/ directory of the installed package: standard, 'mpi' (for parallel computing), 'qt' (showing how to embed 'RInside' inside a Qt GUI application), 'wt' (showing how to build a "web-application" using the Wt toolkit), 'armadillo' (for 'RInside' use with 'RcppArmadillo'), 'eigen' (for 'RInside' use with 'RcppEigen'), and 'c_interface' for a basic C interface and 'Ruby' illustration. The examples use 'GNUmakefile(s)' with GNU extensions, so a GNU make is required (and will use the 'GNUmakefile' automatically). 'Doxygen'-generated documentation of the C++ classes is available at the 'RInside' website as well. Package: r-cran-rinsp Architecture: arm64 Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 255 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rinsp_1.2.5-1.ca2604.1_arm64.deb Size: 166418 MD5sum: 02b63c667005f5234deabe532cab79a5 SHA1: 8faf3f3666b8d3c390df72d20ed1e5e01d6fe71c SHA256: d42f61303effc005d444f13773977e05251599d29dce4acb4f45c561a00a15c4 SHA512: 28c3d33493bb9fbf9d133870b6bda4b68eebf78c9a0f7069adf20197de317fb12cdf077380fadbe14885a92db8b7cd876ef9a2408ea1368cd04bfaba242ce9b3 Homepage: https://cran.r-project.org/package=RInSp Description: CRAN Package 'RInSp' (R Individual Specialization) Functions to calculate several ecological indices of individual and population niche width (Araujo's E, clustering and pairwise similarity among individuals, IS, Petraitis' W, and Roughgarden's WIC/TNW) to assess individual specialization based on data of resource use. Resource use can be quantified by counts of categories, measures of mass or length, or proportions. Monte Carlo resampling procedures are available for hypothesis testing against multinomial null models. Details are provided in Zaccarelli et al. (2013) and associated references. Package: r-cran-rioja Architecture: arm64 Version: 1.0-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 614 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-vegan, r-cran-mgcv Suggests: r-cran-foreach Filename: pool/dists/resolute/main/r-cran-rioja_1.0-7-1.ca2604.1_arm64.deb Size: 461798 MD5sum: 3fc080a99d8d8fbced40940bf5e1a5fa SHA1: b2ccba359a889d299d37f7dbd0e48f3e7c9f14a6 SHA256: 2871e59c60e6e14a3df9f156a0a8b6894cd0f0bcf515af1331551ef794071141 SHA512: 80cfba22102d7bdd1618dbbaba1fa8935479f641f227bf8c46028b51bf982ac44622c463dd6396b45f4e3de4e8b94ab841f5d790d171e172130acd5670b65ce4 Homepage: https://cran.r-project.org/package=rioja Description: CRAN Package 'rioja' (Analysis of Quaternary Science Data) Constrained clustering, transfer functions, and other methods for analysing Quaternary science data. 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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: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 984 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 532542 MD5sum: 5f02ba35a33b701240245b8a75f76f52 SHA1: 1656d6636276fe77d8de493cdc56f4803d1f1353 SHA256: b313c6986cc69a834420aad9f1eefc50cbcb026b300c06155e3ae40135c46fa1 SHA512: 9f4de094d7579d20d83396e3db917af2482bb55c75bbf667d8bdce8508923a43be4c5eb3ef87dc4f265b796cc5d6a70bced91a64b2638bc13708a209d6514f9f 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: arm64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 443 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_arm64.deb Size: 262898 MD5sum: 5702de914ea345e562236618ae2cf44a SHA1: 7dc3b1d61f1a3053bc2a7cb8c60b96e480c47082 SHA256: 9f10bc38d94e7cd2c558fe11aea5a82cd54895ed12b696cafb92c487095e8838 SHA512: d4470be887d72127d7070f5ab6473014fc2c5303a131a1d3c24f33e407d123f1c04100ab89106f0d8529c087ff9bc674bd2acbf6e1e4935dbb07891c0e204083 Homepage: https://cran.r-project.org/package=Rirt Description: CRAN Package 'Rirt' (Data Analysis and Parameter Estimation Using Item ResponseTheory) Parameter estimation, computation of probability, information, and (log-)likelihood, and visualization of item/test characteristic curves and item/test information functions for three uni-dimensional item response theory models: the 3-parameter-logistic model, generalized partial credit model, and graded response model. The full documentation and tutorials are at . Package: r-cran-rising Architecture: arm64 Version: 0.1.0-1.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-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_arm64.deb Size: 91532 MD5sum: ece0d053ba33e1adc3f4df47c6a3fe6e SHA1: 0c71002fe1032b5ed2611d2d3e75c6b9a11c2937 SHA256: 2a8c82f680d1d3c1bb5ecdf703b96eff16d9dc0d2146421677bd2e48b4625db4 SHA512: 3d7e0931f6f95afe4732c051dbf13f62e8410af0778fe6923244109c660d72b37368acec74c60358cca3a1c76ab870673236007f460d0dc0416f89fbcc127b9b Homepage: https://cran.r-project.org/package=rIsing Description: CRAN Package 'rIsing' (High-Dimensional Ising Model Selection) Fits an Ising model to a binary dataset using L1 regularized logistic regression and extended BIC. Also includes a fast lasso logistic regression function for high-dimensional problems. Uses the 'libLBFGS' optimization library by Naoaki Okazaki. Package: r-cran-riskparityportfolio Architecture: arm64 Version: 0.2.2-1.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-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_arm64.deb Size: 1176980 MD5sum: add0a2a9f662d9c64918be117ee1e954 SHA1: 2225de5e9148ad5c681f9fb05a153cbdfbe31357 SHA256: 1a30a759f14a14782e3f11c292f82f59b38c74f3c83adf3e0dde8afb964b5aaf SHA512: e0b3e948b56733571323413cfd9ffe5af85568225cbd901239324e3ad4ceed296526fd12d1c6f1d8c43577f807b1c2fa2f3e6edcc6e299fbd18317134a1478fa Homepage: https://cran.r-project.org/package=riskParityPortfolio Description: CRAN Package 'riskParityPortfolio' (Design of Risk Parity Portfolios) Fast design of risk parity portfolios for financial investment. The goal of the risk parity portfolio formulation is to equalize or distribute the risk contributions of the different assets, which is missing if we simply consider the overall volatility of the portfolio as in the mean-variance Markowitz portfolio. In addition to the vanilla formulation, where the risk contributions are perfectly equalized subject to no shortselling and budget constraints, many other formulations are considered that allow for box constraints and shortselling, as well as the inclusion of additional objectives like the expected return and overall variance. See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the papers: Y. Feng, and D. P. Palomar (2015). SCRIP: Successive Convex Optimization Methods for Risk Parity Portfolio Design. IEEE Trans. on Signal Processing, vol. 63, no. 19, pp. 5285-5300. . F. Spinu (2013), An Algorithm for Computing Risk Parity Weights. . T. Griveau-Billion, J. Richard, and T. Roncalli (2013). A fast algorithm for computing High-dimensional risk parity portfolios. . Package: r-cran-riskregression Architecture: arm64 Version: 2026.03.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2311 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_arm64.deb Size: 1726390 MD5sum: 80c725b2c6bc08b4870d2bc029370538 SHA1: e5d3d15db48b60561aa4909e7fb25f2a55cc5b85 SHA256: 492a858d92db0603c201bb3cea0b2ab8f891d8fc3ed5968d7083031b896d6ec4 SHA512: 00b1b7d1013298dfc78ba660155835abb231120abbcb47cdf8ab405baa175d394bbd4c3904165365687bfdee042c306d934f23c8ac84220dd89b4dd905b48bca Homepage: https://cran.r-project.org/package=riskRegression Description: CRAN Package 'riskRegression' (Risk Regression Models and Prediction Scores for SurvivalAnalysis with Competing Risks) Implementation of the following methods for event history analysis. Risk regression models for survival endpoints also in the presence of competing risks are fitted using binomial regression based on a time sequence of binary event status variables. A formula interface for the Fine-Gray regression model and an interface for the combination of cause-specific Cox regression models. A toolbox for assessing and comparing performance of risk predictions (risk markers and risk prediction models). Prediction performance is measured by the Brier score and the area under the ROC curve for binary possibly time-dependent outcome. Inverse probability of censoring weighting and pseudo values are used to deal with right censored data. Lists of risk markers and lists of risk models are assessed simultaneously. Cross-validation repeatedly splits the data, trains the risk prediction models on one part of each split and then summarizes and compares the performance across splits. Package: r-cran-ritch Architecture: arm64 Version: 0.1.30-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1200 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 572456 MD5sum: fd700ff89f2a3047c3d2b1e2d06685e1 SHA1: 47bed4782c45c08b09aaaa66e351b7a30d87cb54 SHA256: 8cbf2227fdc38a29d10779d23f36d4a9a8a3be1d1f70e0a60a497c437508d2b8 SHA512: ea23c35acb0626761a2f1ef2b5ae7240a85574de7b3751ec0a0af6b4960f3487184ac9f2bca40497cd5bb898229284d33ae8c87d963826217b721c5dd214f0cd Homepage: https://cran.r-project.org/package=RITCH Description: CRAN Package 'RITCH' (Parser for the ITCH Protocol) Efficiently parses, filters, and writes binary ITCH files (Version 5.0) containing detailed financial transactions as distributed by NASDAQ to a data.table. Includes functions to interact with NASDAQ data services at and . Package: r-cran-rivnet Architecture: arm64 Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2744 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_arm64.deb Size: 2220920 MD5sum: 295b1b73ebea4dc0b2f68fe97439670c SHA1: 4ef8c444856287544a2f400841297f951f9be424 SHA256: cfdf75bbd1e85e1543af169245aac85360f2ddb65a9446260d2f22c4a5f01e1c SHA512: b9b96cf4e5ac96f980150b3d6352c5d66cbaad2d29be59f22bbd44a76c6cbcd59c2205287f8764a3635c733741cab52159ea3b54ebdd02f891717eccc1479484 Homepage: https://cran.r-project.org/package=rivnet Description: CRAN Package 'rivnet' (Extract and Analyze Rivers from Elevation Data) Seamless extraction of river networks from digital elevation models data. The package allows analysis of digital elevation models that can be either externally provided or downloaded from open source repositories (thus interfacing with the 'elevatr' package). Extraction is performed via the 'D8' flow direction algorithm of TauDEM (Terrain Analysis Using Digital Elevation Models), thus interfacing with the 'traudem' package. Resulting river networks are compatible with functions from the 'OCNet' package. See Carraro (2023) for a presentation of the package. Package: r-cran-rivr Architecture: arm64 Version: 1.2-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 599 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_arm64.deb Size: 223994 MD5sum: ea7270e86729328e5fb0e91c9d77d1a8 SHA1: 0a6f61c74f3f54cca51c4d02b1193d9f974a9a9a SHA256: 7c3a2c4bfb5f438c779bbb299dde8684af3e995a91d7b3f7dd6d5b6b92b47ed9 SHA512: f905caaee4cc4f56ae9556bfabc9dc5d65012d49a4e9a9d2dd55e66d4d2bb067770f2c1835fe31f152cd2215891f4bd7ba9347f550494cc254cd919242e71e57 Homepage: https://cran.r-project.org/package=rivr Description: CRAN Package 'rivr' (Steady and Unsteady Open-Channel Flow Computation) A tool for undergraduate and graduate courses in open-channel hydraulics. Provides functions for computing normal and critical depths, steady-state water surface profiles (e.g. backwater curves) and unsteady flow computations (e.g. flood wave routing) as described in Koohafkan MC, Younis BA (2015). "Open-channel computation with R." The R Journal, 7(2), 249–262. . Package: r-cran-rjaf Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 141336 MD5sum: fe585e855dc777db8259c5ff59ca92f3 SHA1: bac650ab7e18ab0d42262dd6820f3fbf071f856c SHA256: cf6ec4b51aeddc624b83f0fdb6d88d66aa7e6a6d840aa6ea79ecd4abd1fdc7e6 SHA512: 7c5831cd733736cfafc34ce778c851709b02c5c75531e912b38b37362f9869ab699fc24a2a2c3399b4a66e045b181a68431e780ac8ca608528289552703ef05d Homepage: https://cran.r-project.org/package=rjaf Description: CRAN Package 'rjaf' (Regularized Joint Assignment Forest with Treatment ArmClustering) Personalized assignment to one of many treatment arms via regularized and clustered joint assignment forests as described in Ladhania, Spiess, Ungar, and Wu (2023) . The algorithm pools information across treatment arms: it considers a regularized forest-based assignment algorithm based on greedy recursive partitioning that shrinks effect estimates across arms; and it incorporates a clustering scheme that combines treatment arms with consistently similar outcomes. Package: r-cran-rjafroc Architecture: arm64 Version: 2.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2743 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_arm64.deb Size: 1857236 MD5sum: 2a437ee08fc3324c5c68ddfb1641c585 SHA1: 9d23b8d5956d70a614680295a1bdf09d11c31f25 SHA256: 64482cffc0d233d984423ccf5e19b0dd4a772736ec84c96821ea858c68184c1b SHA512: 28cce5077291f9b783930ad5d9e29a229c6d858ed02efccd52735c6817be2b7d384763232a3a1f17797ca2dbaf6d99d18bf4bd191fd77720ba46870b92e35165 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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Query and pivot support 'JSONpointer', 'JSONpath' or 'JMESpath' expressions. The implementation uses the 'jsoncons' header-only library; the library is easily linked to other packages for direct access to 'C++' functionality not implemented here. Package: r-cran-rjsonio Architecture: arm64 Version: 2.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2647 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rjsonio_2.0.5-1.ca2604.1_arm64.deb Size: 568448 MD5sum: 9b86b4ccc8baf2bb2c3dc8ea3c1c253e SHA1: e5a96524f823f3fb8b8268bcb28defef3c7fff92 SHA256: 0e013cfd8e23b1d0dac4abd3e57f8f6ae708fd6f579e49ab5576d687b15476ea SHA512: 58103e60d7e48a7ce6bc5a255326444ba0c08aaee00f1ce4c2c708a3387157528c86881bcc9cab75592a495b7fe3ddccd636b0476b98b67ae94656df02efb7d5 Homepage: https://cran.r-project.org/package=RJSONIO Description: CRAN Package 'RJSONIO' (Serialize R Objects to JSON) Converts R objects to and from JavaScript Object Notation (JSON). The package provides a stable interface for reading JSON from strings, files, and connections, and for serializing common R objects, including vectors, lists, data frames, arrays, environments, and S4 objects. It also exposes parser handlers, callbacks, and S4 methods for applications that need customized JSON processing while preserving established RJSONIO behavior. Package: r-cran-rkriging Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1997 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.5), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-nloptr, r-cran-rcppeigen, r-cran-bh Filename: pool/dists/resolute/main/r-cran-rkriging_1.0.2-1.ca2604.1_arm64.deb Size: 473520 MD5sum: a806726aa766e6d9d6755eb984eb734e SHA1: 4be6bf89ece85ef30625863c4e2f0585f61a4f24 SHA256: 5dfbb52418ccb411fa19b04b5cf739997b03503612a0c848ddb7012ee331950c SHA512: eef58675c08eb8cde54598f72747da7b635c49e009bf02d0e2b4d1b87e7982eb2bc700553e4f84a6eb74b30b213da035cada9eda4e7bfb022f6dfd0a7e678665 Homepage: https://cran.r-project.org/package=rkriging Description: CRAN Package 'rkriging' (Kriging Modeling) An 'Eigen'-based computationally efficient 'C++' implementation for fitting various kriging models to data. This research is supported by U.S. National Science Foundation grant DMS-2310637. Package: r-cran-rkvo Architecture: arm64 Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: libc6 (>= 2.17), 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-rkvo_0.1-1.ca2604.1_arm64.deb Size: 38810 MD5sum: 40bf36fdc55e4956d47fcf0913f92e8c SHA1: c4fedfec9a33bf5aa41639566d2516d27a8d2b7e SHA256: 2d5eb61478ad0dd3448f4eb84285a82658bd791d98cab15e1349bfffa460b41b SHA512: 7a0ea0284f49c6b8cfc430797f250c6fb152d514bddedef6ec7ebca6a5bbb264a0419b2e2f1c0f792d94c8d125b7ee9a041ceeb3409ef67d25afcf91af41824c Homepage: https://cran.r-project.org/package=rkvo Description: CRAN Package 'rkvo' (Read Key/Value Pair Observations) This package provides functionality to read files containing observations which consist of arbitrary key/value pairs. 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Package: r-cran-rlibeemd Architecture: arm64 Version: 1.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 224 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_arm64.deb Size: 95128 MD5sum: db71bd404382a1f8765642a8931400b0 SHA1: 68b4a645a5f8b38db328b3c334704c5361be67b7 SHA256: fb17ac232f58ecfbb7d961173bbfd1d29763c765b68f2a3cc62afa55337c1d53 SHA512: 8304417073ffd2a8aa8355f2306b11f4d15fe229817e67f858a95c7bc3b07374355a346dc3d489112c50eeecf05374c16c68ad6aed6e016d3e4202cdc46ba603 Homepage: https://cran.r-project.org/package=Rlibeemd Description: CRAN Package 'Rlibeemd' (Ensemble Empirical Mode Decomposition (EEMD) and Its CompleteVariant (CEEMDAN)) An R interface for libeemd (Luukko, Helske, Räsänen, 2016) , a C library of highly efficient parallelizable functions for performing the ensemble empirical mode decomposition (EEMD), its complete variant (CEEMDAN), the regular empirical mode decomposition (EMD), and bivariate EMD (BEMD). Due to the possible portability issues CRAN version no longer supports OpenMP, but you can install OpenMP-supported version from GitHub: . 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Regardless of the size of your dataset, our library delivers efficient and accurate results. Tsz Nam Chan, Leong Hou U, Byron Choi, Jianliang Xu, Reynold Cheng (2023) . Tsz Nam Chan, Rui Zang, Pak Lon Ip, Leong Hou U, Jianliang Xu (2023) . Tsz Nam Chan, Leong Hou U, Byron Choi, Jianliang Xu (2022) . Tsz Nam Chan, Pak Lon Ip, Kaiyan Zhao, Leong Hou U, Byron Choi, Jianliang Xu (2022) . Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Byron Choi, Jianliang Xu (2022) . Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Byron Choi, Jianliang Xu (2022) . Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Weng Hou Tong, Shivansh Mittal, Ye Li, Reynold Cheng (2021) . Tsz Nam Chan, Zhe Li, Leong Hou U, Jianliang Xu, Reynold Cheng (2021) . Tsz Nam Chan, Reynold Cheng, Man Lung Yiu (2020) . Tsz Nam Chan, Leong Hou U, Reynold Cheng, Man Lung Yiu, Shivansh Mittal (2020) . Tsz Nam Chan, Man Lung Yiu, Leong Hou U (2019) . 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However, standard highest-density intervals can be wide due to between-subjects variability and tends to hide within-subject effects, rendering its relationship with the Bayes factor less clear in within-subject (repeated-measures) designs. This urgent issue can be addressed by using within-subject intervals in within-subject designs, which integrate four methods including the Wei-Nathoo-Masson (2023) , the Loftus-Masson (1994) , the Nathoo-Kilshaw-Masson (2018) , and the Heck (2019) interval estimates. Package: r-cran-rmcc Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 561 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 Filename: pool/dists/resolute/main/r-cran-rmcc_0.1.2-1.ca2604.1_arm64.deb Size: 205036 MD5sum: bd90179bcb3bfb03effd65b1e17b32c1 SHA1: 32887c42957059207257049c41929d789dc89106 SHA256: 68b4464f0e4c8dcb3e9dc32d7d34f264f84a027fc67fce4dfca9b1ae4cc746ab SHA512: 098505fdfe2677600e3b8cb52ce1fad9b5d81bd6c1782ec9319905c542fdb7271ff60d137057a3e52262b847111022a8a117134e70b15f85030f7548a261e02c Homepage: https://cran.r-project.org/package=RMCC Description: CRAN Package 'RMCC' (Airborne LiDAR Filtering Method Based on Multiscale Curvature) Multiscale Curvature Classification of ground returns in 3-D LiDAR point clouds, designed for forested environments. 'RMCC' is a porting to R of the 'MCC-lidar' method by Evans and Hudak (2007) . Package: r-cran-rmclab Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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-softimpute, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-rmclab_0.1.0-1.ca2604.1_arm64.deb Size: 150476 MD5sum: 821d2601a4ccb4761bfda91bd63816ef SHA1: b37aca99c15f228fecbd119af5bc325fdfae38fe SHA256: c9d7e74180201a9a28f3bed4063083f9a8a06fa2d256c6a60b5656433af9c0b2 SHA512: e96236f447a0731104286ce78ca09fd9daee85c5ff98b1fbdcbb9830f4a2139fe2c679f7ccfd38c2d1806d47496cf568b1908aaeac898b731495ad8060812d73 Homepage: https://cran.r-project.org/package=RMCLab Description: CRAN Package 'RMCLab' (Lab for Matrix Completion and Imputation of Discrete Rating Data) Collection of methods for rating matrix completion, which is a statistical framework for recommender systems. Another relevant application is the imputation of rating-scale survey data in the social and behavioral sciences. Note that matrix completion and imputation are synonymous terms used in different streams of the literature. The main functionality implements robust matrix completion for discrete rating-scale data with a low-rank constraint on a latent continuous matrix (Archimbaud, Alfons, and Wilms (2025) ). In addition, the package provides wrapper functions for 'softImpute' (Mazumder, Hastie, and Tibshirani, 2010, ; Hastie, Mazumder, Lee, Zadeh, 2015, ) for easy tuning of the regularization parameter, as well as benchmark methods such as median imputation and mode imputation. 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Package: r-cran-rmecabko Architecture: arm64 Version: 0.1.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libmecab2 (>= 0.996), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-stringr Filename: pool/dists/resolute/main/r-cran-rmecabko_0.1.6.2-1.ca2604.1_arm64.deb Size: 91248 MD5sum: f207e8814620523dcfdbcfd044052aa4 SHA1: f0c47e07b9f7a9625739031989acc705bb7f9d97 SHA256: 9f83c87c160caec18f624435febdf8a1e3069ad899671d2b5bc7abf600a0887a SHA512: a4ab9787effa29e58f2e1aaa63f67f9d9313fa9d59fbfc39a3b5695665bc0cf002c3f12b88ac1cd86b171fca5b27d9b9514c6eaf01fe0546261a514c1f203104 Homepage: https://cran.r-project.org/package=RmecabKo Description: CRAN Package 'RmecabKo' (An 'Rcpp' Interface for Eunjeon Project) An 'Rcpp' interface for Eunjeon project . 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Package: r-cran-rmpi Architecture: arm64 Version: 0.7-3.4-1.ca2604.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: libc6 (>= 2.34), libopenmpi40 (>= 5.0.10), r-base-core (>= 4.6.0), r-api-4.0, libopenmpi-dev Filename: pool/dists/resolute/main/r-cran-rmpi_0.7-3.4-1.ca2604.2_arm64.deb Size: 271044 MD5sum: 4c1997537e8282368a3e658c6c19bd75 SHA1: 453f174cf59d0c5acc9560fe945ad52ddd63b71c SHA256: e91e2ba604063ded2a3ad7a58dfab48061a995a7e96cf388a425a775730246c9 SHA512: 42f47bb96f3443e28e606741cd556206dbcf6770eb9994c3eb939d33a2daedbe3c6eedca7b53fe880788b4bb61459c99bcc306c895652392e6f56088254e53b5 Homepage: https://cran.r-project.org/package=Rmpi Description: CRAN Package 'Rmpi' (Interface (Wrapper) to MPI (Message-Passing Interface)) An interface (wrapper) to MPI. It also provides interactive R manager and worker environment. Package: r-cran-rmpsh Architecture: arm64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 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-rmpsh_1.1.1-1.ca2604.1_arm64.deb Size: 44232 MD5sum: 21a029bc89d9b2f599b4f7de3e8cd661 SHA1: bba833d8b6289868cb97688c13e27e30d314b2f3 SHA256: bf3b7121c5101044debe14aca16dcc3c122d7875819359c73b42f76c681a3a9d SHA512: 662ed5c3de41f560b3f4c0f23afb9177e741b92bd0f0b3d797e9a7747770868692850c70f88dd7bd1d3bebded5190cf2df6cabc705304eda83e1046c5950cd2b Homepage: https://cran.r-project.org/package=RMPSH Description: CRAN Package 'RMPSH' (Recursive Modified Pattern Search on Hyper-Rectangle) Optimization of any Black-Box/Non-Convex Function on Hyper-Rectangular Parameter Space. It uses a Variation of Pattern Search Technique. Described in the paper : Das (2016) . Package: r-cran-rms Architecture: arm64 Version: 8.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2867 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-hmisc, r-cran-survival, r-cran-quantreg, r-cran-ggplot2, r-cran-matrix, r-cran-sparsem, r-cran-rpart, r-cran-nlme, r-cran-polspline, r-cran-multcomp, r-cran-htmltable, r-cran-htmltools, r-cran-mass, r-cran-cluster, r-cran-digest, r-cran-colorspace, r-cran-knitr, r-cran-scales Suggests: r-cran-boot, r-cran-plotly, r-cran-mice, r-cran-icenreg, r-cran-rmsb, r-cran-nnet, r-cran-vgam, r-cran-lattice, r-cran-kableextra Filename: pool/dists/resolute/main/r-cran-rms_8.1-1-1.ca2604.1_arm64.deb Size: 2473606 MD5sum: 99aa31ad01bf3e3d5252e58f516426d5 SHA1: 78ab1cff0a51aba14032d1edab5d65d15c9904e9 SHA256: b65e6a808eab0a97bdab2a238a950f6e7600ca846157801bf6d5b87b15e662c5 SHA512: a10459e283884c42101309433ffda3ddab60549f6b3320e6fa0916f2a107d16a618e57a6e7831088acbea7a4a5392e4baadd60c34fcfb48a1ecaf608d54b1ab6 Homepage: https://cran.r-project.org/package=rms Description: CRAN Package 'rms' (Regression Modeling Strategies) Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. 'rms' is a collection of functions that assist with and streamline modeling. It also contains functions for binary and ordinal logistic regression models, ordinal models for continuous Y with a variety of distribution families, and the Buckley-James multiple regression model for right-censored responses, and implements penalized maximum likelihood estimation for logistic and ordinary linear models. 'rms' works with almost any regression model, but it was especially written to work with binary or ordinal regression models, Cox regression, accelerated failure time models, ordinary linear models, the Buckley-James model, generalized least squares for serially or spatially correlated observations, generalized linear models, and quantile regression. Package: r-cran-rmsb Architecture: arm64 Version: 1.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3290 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-rms, r-cran-rcpp, r-cran-rstan, r-cran-hmisc, r-cran-survival, r-cran-ggplot2, r-cran-mass, r-cran-cluster, r-cran-digest, r-cran-knitr, r-cran-loo, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-bayesplot, r-cran-mice Filename: pool/dists/resolute/main/r-cran-rmsb_1.1-2-1.ca2604.1_arm64.deb Size: 1030750 MD5sum: e906dd94b4b331ffd263e51be0f28ccc SHA1: 87baae6f59a27098e56b87d9247d417742abf830 SHA256: cc1dd85ae0404970716a54a3a32646aa130ad8a64f245bcda033b46f65ce9b00 SHA512: ce3a356e8342e90f34da21935257e102f26530eae60182679a5e4dfdfc0e0b0cc9c328692cd6f7acbe85221df19652c35c5f5ee0e7cb6012746657a681a3e0d1 Homepage: https://cran.r-project.org/package=rmsb Description: CRAN Package 'rmsb' (Bayesian Regression Modeling Strategies) A Bayesian companion to the 'rms' package, 'rmsb' provides Bayesian model fitting, post-fit estimation, and graphics. It implements Bayesian regression models whose fit objects can be processed by 'rms' functions such as 'contrast()', 'summary()', 'Predict()', 'nomogram()', and 'latex()'. The fitting function currently implemented in the package is 'blrm()' for Bayesian logistic binary and ordinal regression with optional clustering, censoring, and departures from the proportional odds assumption using the partial proportional odds model of Peterson and Harrell (1990) . Package: r-cran-rmsfuns Architecture: arm64 Version: 1.0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 77 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-readr, r-cran-purrr, r-cran-magrittr, r-cran-dplyr, r-cran-tbl2xts, r-cran-performanceanalytics, r-cran-xts, r-cran-zoo Suggests: r-cran-lubridate, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rmsfuns_1.0.0.1-1.ca2604.1_arm64.deb Size: 32890 MD5sum: 31cd6349d148d24c5bbe5db17937e02e SHA1: 99963a3f3227a1317afb819bc76105308eb1e286 SHA256: 915e1f2362145b39755630ff472f39feeeb6325110543b56c9f1d3fd2fe3709e SHA512: fb08dad323c6ebe984459dea7878cc2831df5aea0652e516a09fa1f8e482e44e1a47b50dffe283c6a27f66a1c1040c8cad04ab051c01b8f9934f3d8813b102a1 Homepage: https://cran.r-project.org/package=rmsfuns Description: CRAN Package 'rmsfuns' (Quickly View Data Frames in 'Excel', Build Folder Paths andCreate Date Vectors) Contains several useful navigation helper functions, including easily building folder paths, quick viewing dataframes in 'Excel', creating date vectors and changing the console prompt to reflect time. Package: r-cran-rmsnumpress Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 196 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_arm64.deb Size: 62036 MD5sum: a03add879b7c5cd741683b13f800d0e8 SHA1: 92d1c2003c10080e1a427ec448f450da5fca5a2e SHA256: 3565acbe91fafca37a331af73a78f61d0c2b8c765ea57d3378a0d22a152df3e9 SHA512: 6b862be14a57dc10011b26bd582a93d22fe454185db7c17be95101086cd064bc8c5605d1f4a8299b2ede0811c4edb15f3284fb3da075164734f9cbb58dba97ba Homepage: https://cran.r-project.org/package=RMSNumpress Description: CRAN Package 'RMSNumpress' ('Rcpp' Bindings to Native C++ Implementation of MS Numpress) 'Rcpp' bindings to the native C++ implementation of MS Numpress, that provides two compression schemes for numeric data from mass spectrometers. The library provides implementations of 3 different algorithms, 1 designed to compress first order smooth data like retention time or M/Z arrays, and 2 for compressing non smooth data with lower requirements on precision like ion count arrays. Refer to the publication (Teleman et al., (2014) ) for more details. Package: r-cran-rmss Architecture: arm64 Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 494 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-srlars, r-cran-robstepsplitreg, r-cran-cellwise, r-cran-robustbase, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-mvnfast Filename: pool/dists/resolute/main/r-cran-rmss_1.2.4-1.ca2604.1_arm64.deb Size: 212958 MD5sum: 8d945fc2e8bac58e278eb5f23c89025a SHA1: a9bf664e2bcb4e0a2854719eb67fca1d1a6700fe SHA256: 610ad40f07bb7b822c5cab0500a7aef9932376dff4078b6ffb40899fa9bfc80a SHA512: b7bffe2797c5060fc2e002b144f7044f43fcfde711c609d791475626e143449c4db248092b7596d0a5f14f3370c5025d3b1a8a09f8a198729ca7b4873ddb7799 Homepage: https://cran.r-project.org/package=RMSS Description: CRAN Package 'RMSS' (Robust Multi-Model Subset Selection) Efficient algorithms for generating ensembles of robust, sparse and diverse models via robust multi-model subset selection (RMSS). The robust ensembles are generated by minimizing the sum of the least trimmed square loss of the models in the ensembles under constraints for the size of the models and the sharing of the predictors. Tuning parameters for the robustness, sparsity and diversity of the robust ensemble are selected by cross-validation. Package: r-cran-rmumps Architecture: arm64 Version: 5.2.1-41-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2892 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-testthat, r-cran-matrix, r-cran-slam Filename: pool/dists/resolute/main/r-cran-rmumps_5.2.1-41-1.ca2604.1_arm64.deb Size: 1202528 MD5sum: bc62e2eb30add062176ac7edebc6d466 SHA1: 773e30223c67f6b0b558b3d43451fd2ddfdf8aa0 SHA256: e8a38da683e2c7c6bbeaffb562083aaab652660afef6473263efeefd5268bef6 SHA512: cddb9dcd1798a08effd7220d8d0f8cbfff426ec75e5729c51676bee00a63a5661ad89623c4d1177456987211c98ae6f035a0cd2909d2e869a8e33ac7bb04db9d Homepage: https://cran.r-project.org/package=rmumps Description: CRAN Package 'rmumps' (Wrapper for MUMPS Library) Some basic features of 'MUMPS' (Multifrontal Massively Parallel sparse direct Solver) are wrapped in a class whose methods can be used for sequentially solving a sparse linear system (symmetric or not) with one or many right hand sides (dense or sparse). There is a possibility to do separately symbolic analysis, LU (or LDL^t) factorization and system solving. Third part ordering libraries are included and can be used: 'PORD', 'METIS', 'SCOTCH'. 'MUMPS' method was first described in Amestoy et al. (2001) and Amestoy et al. (2006) . Package: r-cran-rmutil Architecture: arm64 Version: 1.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 873 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rmutil_1.1.10-1.ca2604.1_arm64.deb Size: 724784 MD5sum: 9907544487d92b7bd28590f899e5f101 SHA1: 97d12319628842c97d9b92ae3ceaad10ae7d4715 SHA256: 425356502e9d3d3761a76b48f57853bf0c3284e6b6b2963d0c6a31e0f720dc89 SHA512: f8e15af7f28f988560c9b500791b369192c009d7e27538c7cc00b897acd18408d147a2ca758d0375de3d7c744a3ef0fd5be142ea4be1943d28855da891a77cd8 Homepage: https://cran.r-project.org/package=rmutil Description: CRAN Package 'rmutil' (Utilities for Nonlinear Regression and Repeated MeasurementsModels) A toolkit of functions for nonlinear regression and repeated measurements not to be used by itself but called by other Lindsey packages such as 'gnlm', 'stable', 'growth', 'repeated', and 'event' (available at ). Package: r-cran-rmvl Architecture: arm64 Version: 1.1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 463 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rmvl_1.1.0.3-1.ca2604.1_arm64.deb Size: 241700 MD5sum: 4c7cc13a27e5d9fab2365ed080773bb0 SHA1: f4eda1b46f7ce2cad08429375f23124dd5fa1ba8 SHA256: a162d7e17ebf4535567be58723993a15d2dc56ba28abe81039da5f3ddbccfe10 SHA512: 94233c41b2d83f85375c752f3b4cbe6b0cc88172dc990e252b3cb7457c12e48dffe270e0962ae47f6c277a366796ca736c613ccf04e5f560506ab3ed18fd764b Homepage: https://cran.r-project.org/package=RMVL Description: CRAN Package 'RMVL' (Mappable Vector Library for Handling Large Datasets) Mappable vector library provides convenient way to access large datasets. Use all of your data at once, with few limits. Memory mapped data can be shared between multiple R processes. Access speed depends on storage medium, so solid state drive is recommended, preferably with PCI Express (or M.2 nvme) interface or a fast network file system. The data is memory mapped into R and then accessed using usual R list and array subscription operators. Convenience functions are provided for merging, grouping and indexing large vectors and data.frames. The layout of underlying MVL files is optimized for large datasets. The vectors are stored to guarantee alignment for vector intrinsics after memory map. The package is built on top of libMVL, which can be used as a standalone C library. libMVL has simple C API making it easy to interchange datasets with outside programs. Large MVL datasets are distributed via Academic Torrents . Package: r-cran-rmvp Architecture: arm64 Version: 1.4.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1936 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-mass, r-cran-bigmemory, r-cran-rhpcblasctl, r-cran-rcpp, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-rcppprogress, r-cran-bh Suggests: r-cran-knitr, r-cran-testthat, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rmvp_1.4.6-1.ca2604.1_arm64.deb Size: 1347424 MD5sum: d6a01b4ff078c97641268ab232507791 SHA1: e1d4f570f62130d60616304c419a8f9c2a2023e6 SHA256: 3d32ea20456e4cfc26dca69caa4cd14ba6267120abcedbb9fd3ad67f2e0f27fa SHA512: 5775cf7b261b9c8609f999534afd0c06d1f78729b096f31c82ec4a9e963a7187c49536f2232c6f8c83b7c10676133e46dd14e6d9a102bc6b9fbbcf3caa9948d1 Homepage: https://cran.r-project.org/package=rMVP Description: CRAN Package 'rMVP' (Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWASTool) A memory-efficient, visualize-enhanced, parallel-accelerated Genome-Wide Association Study (GWAS) tool. It can (1) effectively process large data, (2) rapidly evaluate population structure, (3) efficiently estimate variance components several algorithms, (4) implement parallel-accelerated association tests of markers three methods, (5) globally efficient design on GWAS process computing, (6) enhance visualization of related information. 'rMVP' contains three models GLM (Alkes Price (2006) ), MLM (Jianming Yu (2006) ) and FarmCPU (Xiaolei Liu (2016) ); variance components estimation methods EMMAX (Hyunmin Kang (2008) ;), FaSTLMM (method: Christoph Lippert (2011) , R implementation from 'GAPIT2': You Tang and Xiaolei Liu (2016) and 'SUPER': Qishan Wang and Feng Tian (2014) ), and HE regression (Xiang Zhou (2017) ). Package: r-cran-rmysql Architecture: arm64 Version: 0.11.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 454 Depends: libc6 (>= 2.38), libmysqlclient24 (>= 8.4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dbi Suggests: r-cran-testthat, r-cran-curl Filename: pool/dists/resolute/main/r-cran-rmysql_0.11.3-1.ca2604.1_arm64.deb Size: 286478 MD5sum: bf5a1cda2fd4cadff4a60945763da24a SHA1: 2ea1684585916f8a063ecec1d6aabb95ca565be6 SHA256: 8908fe6e99cd35fe9a3080f4aa860325bbca7a2a584219ea35350cd15fe33cad SHA512: b226c4ee6f3d69160a2f0f860048e85e284b79b432105cb6c8a52963f9f3b3f00a7a9dbeb2ad766b8d7f45482e407b080a352e31ecb0ef705c3ebfc6942e1bcf Homepage: https://cran.r-project.org/package=RMySQL Description: CRAN Package 'RMySQL' (Database Interface and 'MySQL' Driver for R) Legacy 'DBI' interface to 'MySQL' / 'MariaDB' based on old code ported from S-PLUS. A modern 'MySQL' client written in 'C++' is available from the 'RMariaDB' package. Package: r-cran-rnanoflann Architecture: arm64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1603 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-rnanoflann_0.0.3-1.ca2604.1_arm64.deb Size: 175838 MD5sum: 6a41c3c14daa236b0f291abb5c0cdcb4 SHA1: 5ba142faf45ba90971c439446d0da9326b04290a SHA256: 8c441df9d6e27993a6953dff7a4903b59db4cb9082a4e65c9a7d4ac0eb735e88 SHA512: c7c4c59835e21f1a86d2c0a93c39ba0a85eabc432a5f115dcf8c7e3d1fd0a36dea7b684b6d88aa8e617f0b0cd0f6e3f26d1fc2fd44ccaba7dc3f46e64efe4aa3 Homepage: https://cran.r-project.org/package=Rnanoflann Description: CRAN Package 'Rnanoflann' (Extremely Fast Nearest Neighbor Search) Finds the k nearest neighbours for every point in a given dataset using Jose Luis' 'nanoflann' library. There is support for exact searches, fixed radius searches with 'kd' trees and two distances, the 'Euclidean' and 'Manhattan'. For more information see . Also, the 'nanoflann' library is exported and ready to be used via the linking to mechanism. Package: r-cran-rncl Architecture: arm64 Version: 0.8.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1485 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-progress Suggests: r-cran-testthat, r-cran-ape Filename: pool/dists/resolute/main/r-cran-rncl_0.8.9-1.ca2604.1_arm64.deb Size: 493950 MD5sum: 060e2d024756287b9afadf8e4714c5c7 SHA1: 23e25bdd98e44755a87aed992f6c37d237995ff9 SHA256: 76cde2ce3f3da69852794b4d8ab5d978946a863ea095d2f2dec0b122f4a8ceb4 SHA512: d71d8f73ee4fa5e76414641c1e1ca82edaff39548cb6752d6d093e47a5e6b7cafb57aeb35d8ca9b62dbe1769b029eac5d6d5414d494ea78c8b423e0fdad77e62 Homepage: https://cran.r-project.org/package=rncl Description: CRAN Package 'rncl' (An Interface to the Nexus Class Library) An interface to the Nexus Class Library which allows parsing of NEXUS, Newick and other phylogenetic tree file formats. It provides elements of the file that can be used to build phylogenetic objects such as ape's 'phylo' or phylobase's 'phylo4(d)'. This functionality is demonstrated with 'read_newick_phylo()' and 'read_nexus_phylo()'. 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It is a wrapper around the rgraph library (Guimera & Amaral, 2005, ). Package: r-cran-rnetcdf Architecture: arm64 Version: 2.11-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 376 Depends: libc6 (>= 2.17), libnetcdf22 (>= 4.9.0), libudunits2-0 (>= 2.2.14), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bit64 Filename: pool/dists/resolute/main/r-cran-rnetcdf_2.11-1-1.ca2604.1_arm64.deb Size: 226836 MD5sum: 8ab94be9d132486bed5621dd137de559 SHA1: 7e24e1470ed93aa60c3a43986fa8caa3a02f5787 SHA256: 84f152e49cb6e785d2a6a743848eb3221ee97ba0ab346d282139146f527b92f1 SHA512: a334342a4dc3182dcf53603237add385b5211dd3800c0c417c220c6f99a828d73ea1c0a8174e4f588adfd9a19c54a931583461e108b6a7190f0b45871c2680d3 Homepage: https://cran.r-project.org/package=RNetCDF Description: CRAN Package 'RNetCDF' (Interface to 'NetCDF' Datasets) An interface to the 'NetCDF' file formats designed by Unidata for efficient storage of array-oriented scientific data and descriptions. Most capabilities of 'NetCDF' version 4 are supported. 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Package: r-cran-rngwell Architecture: arm64 Version: 0.10-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rngwell_0.10-10-1.ca2604.1_arm64.deb Size: 32996 MD5sum: d2589df955d916771021a858040121e5 SHA1: 59e1617820eb166a2646364ff341b937a7357407 SHA256: 10646477c6ca4935e143159950ff0a8240ff70c69c98ba1d0bc3a3373be88d9a SHA512: b61cc4c4033b8cfb441b16088184674286dbced4c296e732d8357558ac504c4d46428bc2baa246f74af4e97226d762bfcd8f356b2b11eaa81f4bb2b5c50edf13 Homepage: https://cran.r-project.org/package=rngWELL Description: CRAN Package 'rngWELL' (Toolbox for WELL Random Number Generators) It is a dedicated package to WELL pseudo random generators, which were introduced in Panneton et al. (2006), ``Improved Long-Period Generators Based on Linear Recurrences Modulo 2'', see . But this package is not intended to be used directly, you are strongly __encouraged__ to use the 'randtoolbox' package, which depends on this package. Package: r-cran-rnifti Architecture: arm64 Version: 1.9.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1687 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.0), libstdc++6 (>= 14), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-tinytest, r-cran-covr, r-cran-reportr, r-cran-shades, r-cran-jsonlite Filename: pool/dists/resolute/main/r-cran-rnifti_1.9.0-1.ca2604.1_arm64.deb Size: 901346 MD5sum: 218f5ec41bf668568eb8653aea7c379f SHA1: 894921b932d36f00bc9407793051802a7033618a SHA256: 95394f27ac4f2e67b761f8229c7ef794e936f3efe18361273fc8e5c4d7430f13 SHA512: b5fcd90aa324a7b0e98c6b9c33e9fb51a7e9eee43a1f74e3f54815436555371e597905f2f3dc3b567b9cc0fb3964ee22fa38090682b4e68351d7efb0ddb5cb90 Homepage: https://cran.r-project.org/package=RNifti Description: CRAN Package 'RNifti' (Fast R and C++ Access to NIfTI Images) Provides very fast read and write access to images stored in the NIfTI-1, NIfTI-2 and ANALYZE-7.5 formats, with seamless synchronisation of in-memory image objects between compiled C and interpreted R code. Also provides a simple image viewer, and a C/C++ API that can be used by other packages. Not to be confused with 'RNiftyReg', which performs image registration and applies spatial transformations. Package: r-cran-rniftyreg Architecture: arm64 Version: 2.8.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4226 Depends: libc6 (>= 2.38), 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-rnifti, r-cran-rcppeigen Suggests: r-cran-jpeg, r-cran-loder, r-cran-mmand, r-cran-tinytest, r-cran-covr Filename: pool/dists/resolute/main/r-cran-rniftyreg_2.8.5-1.ca2604.1_arm64.deb Size: 2657356 MD5sum: d6cc2af401d590d506380cc7b4551404 SHA1: d7087f0013a47988cbe67e480ee23288c815ddab SHA256: 5444b7631622a74e5b1a9d219caf777baa3a2232af7b16f25ac018edd59c1cc4 SHA512: 6869d96931feb202ad2245fb9442bc44ad7984acb682045f4105fc324c2edbf9d1a5acb3fe1d7fece9e7eea456e24a372c74512d77385aa716d021ab42eeb49f Homepage: https://cran.r-project.org/package=RNiftyReg Description: CRAN Package 'RNiftyReg' (Image Registration Using the 'NiftyReg' Library) Provides an 'R' interface to the 'NiftyReg' image registration tools . Linear and nonlinear registration are supported, in two and three dimensions. Package: r-cran-rnmr1d Architecture: arm64 Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3597 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-base64enc, r-cran-mass, r-cran-matrix, r-cran-scales, r-cran-doparallel, r-cran-foreach, r-cran-igraph, r-bioc-impute, r-bioc-massspecwavelet, r-cran-ptw, r-cran-signal, r-cran-xml, r-cran-ggplot2, r-cran-plotly, r-cran-plyr, r-cran-minqa Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rnmr1d_1.3.2-1.ca2604.1_arm64.deb Size: 2107502 MD5sum: aafecd1fe60f934b4b323cf4341c6053 SHA1: e5cc88080495285f544fe35abb75f419099f8b98 SHA256: a9057c4c37db4153cd28f219ec6db8d06bf802b8faa4aaaf49ed5235e520458a SHA512: ab979f7f08548f7290d4cfeb3e594baac7956df6418b3ca805edddf7c30bc5e883ab399c4d1bfa4d1ce9d67df279552260df0d2395846a3a8b0ac4d9cf7fda89 Homepage: https://cran.r-project.org/package=Rnmr1D Description: CRAN Package 'Rnmr1D' (Perform the Complete Processing of a Set of Proton NuclearMagnetic Resonance Spectra) Perform the complete processing of a set of proton nuclear magnetic resonance spectra from the free induction decay (raw data) and based on a processing sequence (macro-command file). An additional file specifies all the spectra to be considered by associating their sample code as well as the levels of experimental factors to which they belong. More detail can be found in Jacob et al. (2017) . Package: r-cran-rnndescent Architecture: arm64 Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1882 Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dqrng, r-cran-matrix, r-cran-rcpp, r-cran-bh, r-cran-sitmo Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rnndescent_0.1.8-1.ca2604.1_arm64.deb Size: 659440 MD5sum: 15af3002779d2c98e9622b1c7c71410d SHA1: 596172c1ebd9e9a58a617273d5a67a16b5890bf5 SHA256: 1b3c1290be16e87ed1292711dfcbb2387f1f42eb3337447fdda4dd988d364605 SHA512: 366172f3ec9c428614ef0fcf8bfb5c1718d717f2fe3132b495bf9719dd0b943c2c9e53951a74f484ca4e1a1917b16c5f43f6cba2107bb469de5e9c03e3150833 Homepage: https://cran.r-project.org/package=rnndescent Description: CRAN Package 'rnndescent' (Nearest Neighbor Descent Method for Approximate NearestNeighbors) The Nearest Neighbor Descent method for finding approximate nearest neighbors by Dong and co-workers (2010) . Based on the 'Python' package 'PyNNDescent' . Package: r-cran-rnomni Architecture: arm64 Version: 1.0.1.2-1.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), 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_arm64.deb Size: 199062 MD5sum: 69adb55430369dde0421650ca3983835 SHA1: b5012344452c8622cffaa7fcef9a7591449b8a7b SHA256: 74f2abb5d49f1189e0fa2e6d0eda807f0803a054ffc7986e9d93152e7ee145f4 SHA512: 05d0fa62c6629e6fe7a1bb38a55e7b174c5b7cd38779fe0a825e81b0f242de5045a397c689415cf3ef55b174c711ba33327b36addf46ddd0003ec24a75f4206a Homepage: https://cran.r-project.org/package=RNOmni Description: CRAN Package 'RNOmni' (Rank Normal Transformation Omnibus Test) Inverse normal transformation (INT) based genetic association testing. These tests are recommend for continuous traits with non-normally distributed residuals. INT-based tests robustly control the type I error in settings where standard linear regression does not, as when the residual distribution exhibits excess skew or kurtosis. Moreover, INT-based tests outperform standard linear regression in terms of power. These tests may be classified into two types. In direct INT (D-INT), the phenotype is itself transformed. In indirect INT (I-INT), phenotypic residuals are transformed. The omnibus test (O-INT) adaptively combines D-INT and I-INT into a single robust and statistically powerful approach. See McCaw ZR, Lane JM, Saxena R, Redline S, Lin X. "Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies" . Package: r-cran-robcat Architecture: arm64 Version: 0.2-1.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_arm64.deb Size: 305270 MD5sum: 4b66595a8b231c642dbd012e7d11ecf6 SHA1: 0f6e9c81220504d42cbab9d8dd4210e423255695 SHA256: 08eb0b7899204ab5ccc355cd16628f156ebc0cecce71e5f26639b6420c097340 SHA512: 84d4b6aec3341226ab5ae08198ee5b1608a94f402fa572f1b83e3a507ab26847509b7abacb09379b01c69694a51e9c9da23e96dac5ed34d66d8fd12073c6c7e5 Homepage: https://cran.r-project.org/package=robcat Description: CRAN Package 'robcat' (Robust Categorical Data Analysis) Robust categorical data analysis based on the theory of C-estimation developed in Welz (2024) . For now, the package only implements robust estimation of polychoric correlation as proposed in Welz, Mair and Alfons (2026) and robust estimation of polyserial correlation (Welz, 2026 ) with methods for printing and plotting. We will implement further models in future releases. In addition, the package is still experimental, so input arguments and class structure may change in future releases. Package: r-cran-robcompositions Architecture: arm64 Version: 2.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3043 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), 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_arm64.deb Size: 2614838 MD5sum: 0f92ad59ab917bfaa7ffb1df06461b01 SHA1: 736dccf8a0c1f29e7c6c0afcbb0aa60460fd9c7f SHA256: cc269ea510dae8971a3df7a0f84d25a6df2232c8e6eff5c17733d63c1fd41053 SHA512: 7b3a093ec1928cac394be2c578640a0987c1a1d5064791f586ab4abcf23775c44ac392bf91d2aeee95acade6a131617ec61968b70dfb6aa0a81702a1adbb91c6 Homepage: https://cran.r-project.org/package=robCompositions Description: CRAN Package 'robCompositions' (Compositional Data Analysis) Methods for analysis of compositional data including robust methods (), imputation of missing values (), methods to replace rounded zeros (, , ), count zeros (), methods to deal with essential zeros (), (robust) outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors, functional data analysis () and p-splines (), contingency () and compositional tables (, , ) and (robust) Anderson-Darling normality tests for compositional data as well as popular log-ratio transformations (addLR, cenLR, isomLR, and their inverse transformations). In addition, visualisation and diagnostic tools are implemented as well as high and low-level plot functions for the ternary diagram. Package: r-cran-robcp Architecture: arm64 Version: 0.3.10-1.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-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_arm64.deb Size: 2075712 MD5sum: 3e97ca7e3e9c5796708bb7e0213d87a2 SHA1: f5376ef6bd237ceeabbad558c1365c288d7f95e1 SHA256: 55e02472f87842a80943caae50a9ddb4efd7f2adc4d02944ccd41a324a79929c SHA512: 13218fc765f9c7b56321f7211ec22a97caf43ac1387ab3d0e50807324e25930353058bfc04b699a97c577255dc6902b077f8e53ca64b586370ae861ffa172f62 Homepage: https://cran.r-project.org/package=robcp Description: CRAN Package 'robcp' (Robust Change-Point Tests) Provides robust methods to detect change-points in uni- or multivariate time series. They can cope with corrupted data and heavy tails. Focus is on the detection of abrupt changes in location, but changes in the scale or dependence structure can be detected as well. This package provides tests for change detection in uni- and multivariate time series based on Huberized versions of CUSUM tests proposed in Duerre and Fried (2019) , and tests for change detection in univariate time series based on 2-sample U-statistics or 2-sample U-quantiles as proposed by Dehling et al. (2015) and Dehling, Fried and Wendler (2020) . Furthermore, the packages provides tests on changes in the scale or the correlation as proposed in Gerstenberger, Vogel and Wendler (2020) , Dehling et al. (2017) , and Wied et al. (2014) . Package: r-cran-roben Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1108 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 673180 MD5sum: a5ccf202de93ebc090dac51ab4c73971 SHA1: c16c74fb546a8e41f5b8732cc877442b747777cd SHA256: 1d419b7208c857bc389637a95edaf661e598624aefd8321929384ef47841cc8a SHA512: a7093f45eda6cb37da5cbd37e2c75bf799e4239b14b14c85ecb2e7d50504b30eabe1e21aabe15bb238766cf3d04cf8ae5af2167f30ccc5ce1a0731a2b4032035 Homepage: https://cran.r-project.org/package=roben Description: CRAN Package 'roben' (Robust Bayesian Variable Selection for Gene-EnvironmentInteractions) Gene-environment (G×E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G×E studies have been commonly encountered, leading to the development of a broad spectrum of robust penalization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a robust Bayesian variable selection method for G×E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects. An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++. Package: r-cran-robextremes Architecture: arm64 Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1639 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1114378 MD5sum: feb0a169522306fc5cfb0a72495ab883 SHA1: 05bfdee9bb06c464b8fa2cc9d6b7f41f72100b8b SHA256: 52ef6b49ad633ce742a2f9ac3636586a686bd37c789381a1ddad44e9d8c514ee SHA512: bc8b0a53d62071c0bd16200a888847241b0799f04d45b637eac13c6a0f8866af0a8582e312f8235c01da75320d5176baa719c14c13f603d3ccee95051c3c2ca7 Homepage: https://cran.r-project.org/package=RobExtremes Description: CRAN Package 'RobExtremes' (Optimally Robust Estimation for Extreme Value Distributions) Optimally robust estimation for extreme value distributions using S4 classes and methods (based on packages 'distr', 'distrEx', 'distrMod', 'RobAStBase', and 'ROptEst'); the underlying theoretic results can be found in Ruckdeschel and Horbenko, (2013 and 2012), \doi{10.1080/02331888.2011.628022} and \doi{10.1007/s00184-011-0366-4}. Package: r-cran-robfilter Architecture: arm64 Version: 4.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 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_arm64.deb Size: 462766 MD5sum: 793cbf897972759e0bdb6abed340dc0c SHA1: 33adc00d06906a4c9f879e973c6f4cdd0d0a38fb SHA256: 35bebea76c8672751aaa564c04e70794f89df148cea404d698acae16951531ce SHA512: 2f8a806693edf9bcecb4259a9d48cde6c7ca098c41b339428e231f74c4b9d0b1981089a68da6c100e74f0bab33e3acbdce6272a066a94120fa483f1cea2995c8 Homepage: https://cran.r-project.org/package=robfilter Description: CRAN Package 'robfilter' (Robust Time Series Filters) Implementations for several robust procedures that allow for (online) extraction of the signal of univariate or multivariate time series by applying robust regression techniques to a moving time window are provided. Included are univariate filtering procedures based on repeated-median regression as well as hybrid and trimmed filters derived from it; see Schettlinger et al. (2006) . The adaptive online repeated median by Schettlinger et al. (2010) and the slope comparing adaptive repeated median by Borowski and Fried (2013) choose the width of the moving time window adaptively. Multivariate versions are also provided; see Borowski et al. (2009) for a multivariate online adaptive repeated median and Borowski (2012) for a multivariate slope comparing adaptive repeated median. Furthermore, a repeated-median based filter with automatic outlier replacement and shift detection is provided; see Fried (2004) . Package: r-cran-robgarchboot Architecture: arm64 Version: 1.2.0-1.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_arm64.deb Size: 86404 MD5sum: 7416b882026ff30f01c605e2017ea156 SHA1: db5ea341b2b182fe1f3fea875ec4477e1502eb09 SHA256: 5d463f5f3278df8c32dd2ad0f509352d3a0a15d1915aaf2d18d4e041de78f710 SHA512: d40a68f45453815117397e406f43bca0de7423f8206919237ae5c2a0d31b1598b933b27f98cefc6992c26f2fdc681f0eddc89bf07124add15142fd1b2444199c Homepage: https://cran.r-project.org/package=RobGARCHBoot Description: CRAN Package 'RobGARCHBoot' (Robust Bootstrap Forecast Densities for GARCH Models) Bootstrap forecast densities for GARCH (Generalized Autoregressive Conditional Heteroskedastic) returns and volatilities using the robust residual-based bootstrap procedure of Trucios, Hotta and Ruiz (2017) . Package: r-cran-robkf Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 784 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_arm64.deb Size: 283330 MD5sum: 354608d402a85840f7f2a00d8af6f8fe SHA1: 9d972a4cd5dfb005478e45ee890de7f5954ec9be SHA256: 171ca4212ba105e6c4c6f56456f40383bc0db855112ea5318b5fe6be780a10c1 SHA512: e34315eac13af58dc268d91d9ab349628e5cae2643045c9bf01fd5066ccf53da634c97d58e418d39191d8a400b9d70baf2a7a6cf2f0c023f3118c5bf8acdf10d 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. 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Package: r-cran-robma Architecture: arm64 Version: 4.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10365 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_arm64.deb Size: 5041282 MD5sum: 18b027c5e5026d309cdcb70483a6bae5 SHA1: 1fe88113093afee3b3e4c2cc5064bebe2c3d2402 SHA256: 54b65e1020c1a141192a53b25c71d8da4005500f5aacd14761164056a6e32e09 SHA512: ec66d09b5ff0482444caa005a7271114a979ee4fa141a8ae6ac9fd4e2c90183a1b6e19fdfdd8e72e07a1c7d55f649a6f20e4477701e3d121f8d34f0eebb13eca 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. 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Package: r-cran-robregcc Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 636 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_arm64.deb Size: 390098 MD5sum: 7cdc7d0b3d9981ae36a1201b662d003d SHA1: 06cb0116f621957c85afda2bacf2da699b3a5059 SHA256: 7c14bf67d88c9693ce441f069e572ec15426bcf065e2645e03118e40a71b6c3b SHA512: bfac33b497067c80d5cb208485dd1bf24afa0d8bbb91795377eece50d01aceb7826537898c2722cc293b4ca7c2f2aca955c883ed0ff25cace0c97e4c34757e4e 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. 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Package: r-cran-robsel Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 382 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-glasso, r-cran-rcpp, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-robsel_0.1.0-1.ca2604.1_arm64.deb Size: 244950 MD5sum: 7ca6df7753013e69b2565f93bff81a26 SHA1: d4a844f11ebd5e60d8fa4a1513bc0aed6a2a1845 SHA256: df9cd2f9d6578f3c9838834c2c96fd5ec662cb9049bc665e19ea703c343abdca SHA512: 24b758342b7e6b50594247fad98e8d8f2ac46df04018762485195190614348e62d5434957ad2606e30bce57d90537cee24fea8186d84393cd84d2f4253e03344 Homepage: https://cran.r-project.org/package=robsel Description: CRAN Package 'robsel' (Robust Selection Algorithm) An implementation of algorithms for estimation of the graphical lasso regularization parameter described in Pedro Cisneros-Velarde, Alexander Petersen and Sang-Yun Oh (2020) . 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The implementation is based on algorithms by Dillencourt et. al (1992) and Matousek et. al (1998) . The implementations are detailed in Raymaekers (2023) and Raymaekers J., Dufey F. (2022) . All algorithms run in quasilinear time. Package: r-cran-robstattm Architecture: arm64 Version: 1.0.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1448 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-pyinit, r-cran-rrcov, r-cran-robustbase Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-robstattm_1.0.11-1.ca2604.1_arm64.deb Size: 1156938 MD5sum: 085d855fdfb6bc496948fdde891ee6d0 SHA1: f34b0a3babf74fe1d4eec230b82639bc9f344b57 SHA256: f204d976d33c5708e463116873dbc7126c6c2aa9fb8bc961f7450c221fa8544a SHA512: 233a8de646d8aa462b93e5289a87a6b5f2b554b8e9f002eed24f80817d9a99ce1593500df861234cc36a62085deda0b727377f614cb7444f191ef981f2d3e0b4 Homepage: https://cran.r-project.org/package=RobStatTM Description: CRAN Package 'RobStatTM' (Robust Statistics: Theory and Methods) Companion package for the book: "Robust Statistics: Theory and Methods, second edition", . 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Package: r-cran-robstepsplitreg Architecture: arm64 Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 70 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-cellwise, r-cran-glmnet Suggests: r-cran-testthat, r-cran-mvnfast Filename: pool/dists/resolute/main/r-cran-robstepsplitreg_2.0.0-1.ca2604.1_arm64.deb Size: 40120 MD5sum: 89b8722299b7cb33d16f169702099a02 SHA1: 8ceca78b7ca6a2774371784c238f5da9a2577bab SHA256: e868b42a4e3b481f1916b436617ad0869ae97c2ea44bc6d39e542e9b7159ce94 SHA512: e535f706a1329d08f40a2fa6d2f2c9da2e904fdb17587310a3982794dfaff4dee836a07e94a6cb2f2367f2683f354c372b6105b18b627db110979d53598d605e Homepage: https://cran.r-project.org/package=robStepSplitReg Description: CRAN Package 'robStepSplitReg' (Robust Stepwise Split Regularized Regression) Functions to perform robust stepwise split regularized regression. The approach first uses a robust stepwise algorithm to split the variables into the models of an ensemble. An adaptive robust regularized estimator is then applied to each subset of predictors in the models of an ensemble. Package: r-cran-robsurvey Architecture: arm64 Version: 0.7-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4095 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernsmooth, r-cran-survey Suggests: r-cran-hexbin, r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-wbacon Filename: pool/dists/resolute/main/r-cran-robsurvey_0.7-3-1.ca2604.1_arm64.deb Size: 1373000 MD5sum: 9399332d570cd5ed604cab90ffcb5281 SHA1: 0608a98ba74b49710efa01860aeaa06d166608ce SHA256: c2ed41613e0db4b61298e714110ae642ee8114ce8efc286dd5ceece9cc4798e1 SHA512: 1f4693479b8892e305fdeb940bebf588c5a975909b01ef43f17f4faa79560c25970b26bc62d830cadeea14649cbe46adf34b0f64d33aaf70731f7a50f3137e28 Homepage: https://cran.r-project.org/package=robsurvey Description: CRAN Package 'robsurvey' (Robust Survey Statistics Estimation) Robust (outlier-resistant) estimators of finite population characteristics like of means, totals, ratios, regression, etc. Available methods are M- and GM-estimators of regression, weight reduction, trimming, and winsorization. The package extends the 'survey' package. Package: r-cran-robtt Architecture: arm64 Version: 1.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5506 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), 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-bayestools, r-cran-bridgesampling, r-cran-ggplot2, r-cran-rdpack, r-cran-stanheaders, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-vdiffr, r-cran-knitr, r-cran-rmarkdown, r-cran-covr Filename: pool/dists/resolute/main/r-cran-robtt_1.3.1-1.ca2604.1_arm64.deb Size: 1416350 MD5sum: db8dc5bb291c59d08b1afe304e9c7c69 SHA1: 7ce7ad9ef2fd9f088b2a8113181bbff4988bf941 SHA256: 89082abf13ddf4652eb0a52d2900faa52b3255e1fb4271149884a2b1fd7bc68f SHA512: 321909c980a8d071114678d5ec05731a063095c89a9c4345dd1bf7839db9b46b7eea1a05eff8b30938bfe598ab6635174db7f9048a6129971b345164a748faeb Homepage: https://cran.r-project.org/package=RoBTT Description: CRAN Package 'RoBTT' (Robust Bayesian T-Test) An implementation of Bayesian model-averaged t-tests that allows users to draw inferences about the presence versus absence of an effect, variance heterogeneity, and potential outliers. The 'RoBTT' package estimates ensembles of models created by combining competing hypotheses and applies Bayesian model averaging using posterior model probabilities. Users can obtain model-averaged posterior distributions and inclusion Bayes factors, accounting for uncertainty in the data-generating process (Maier et al., 2024, ). The package also provides a truncated likelihood version of the model-averaged t-test, enabling users to exclude potential outliers without introducing bias (Godmann et al., 2024, ). Users can specify a wide range of informative priors for all parameters of interest. The package offers convenient functions for summary, visualization, and fit diagnostics. Package: r-cran-robust.prioritizr Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2796 Depends: libc6 (>= 2.17), 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-rlang, r-cran-cli, r-cran-assertthat, r-cran-terra, r-cran-sf, r-cran-tibble, r-cran-units, r-cran-prioritizr, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-highs Filename: pool/dists/resolute/main/r-cran-robust.prioritizr_1.0.3-1.ca2604.1_arm64.deb Size: 1553446 MD5sum: afb292873d608857762eb7cee6a21cc5 SHA1: 598ecc15046f6c5fdaaeffcee029bd9d35bb4f3f SHA256: 64d17beb8f12e2cd90ddc01c46fb5933c11344bb3ff976c7fedf1b826813d00a SHA512: 3af1eb8a0b6708a509db47a1da0286ebb0234d0a265ef1ec276a3aa49630ba2bb7b347abc650a1352606388fb7ffc22348b45c7273c05c51003fddab497de6d2 Homepage: https://cran.r-project.org/package=robust.prioritizr Description: CRAN Package 'robust.prioritizr' (Robust Systematic Conservation Prioritization) Systematic conservation prioritization with robust optimization techniques. This is important because conservation prioritizations typically only consider the most likely outcome associated with a conservation action (e.g., establishing a protected area will safeguard a threatened species population) and fail to consider other outcomes and their consequences for meeting conservation objectives. By extending the 'prioritizr' package, this package can be used to generate conservation prioritizations that account of uncertainty in the climate change scenario projections, species distribution models, ecosystem service models, and measurement errors. In particular, prioritizations can be generated to be fully robust to uncertainty by minimizing (or maximizing) objectives under the worst possible outcome. Since reducing the uncertainty associated with achieving conservation objectives may sacrifice other objectives (e.g., minimizing protected area implementation costs), prioritizations can also be generated to be partially robust based on a specified confidence level parameter. Partially robust prioritizations can be generated based on the chance constrained programming problem (Charnes & Cooper 1959, ) and the conditional value-at-risk problem (Rockafellar & Uryasev 2000, ). Package: r-cran-robust Architecture: arm64 Version: 0.7-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 856 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-fit.models, r-cran-lattice, r-cran-mass, r-cran-robustbase, r-cran-rrcov Filename: pool/dists/resolute/main/r-cran-robust_0.7-5-1.ca2604.1_arm64.deb Size: 628372 MD5sum: 6dc8791352e3fb457bc18f590049483e SHA1: 2b621c6c1e6f71c1c54cf69e2a35a513335f8302 SHA256: 73ecfe58afceaa433622f66a909aae582e348d0fe60fba70f4013ad8ce1a2627 SHA512: 8b78b28521ec5c212dd1fabd2e09b0d7cff2381307b610dd90fb46e43b468d8180f91e353829644f5fd7328830f35e7a882f52564c4aaf18501cecb1c7a34661 Homepage: https://cran.r-project.org/package=robust Description: CRAN Package 'robust' (Port of the S+ "Robust Library") Methods for robust statistics, a state of the art in the early 2000s, notably for robust regression and robust multivariate analysis. Package: r-cran-robustaft Architecture: arm64 Version: 1.4-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 638 Depends: libc6 (>= 2.29), libgfortran5 (>= 8), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-robustbase, r-cran-deoptimr Filename: pool/dists/resolute/main/r-cran-robustaft_1.4-7-1.ca2604.1_arm64.deb Size: 477196 MD5sum: 0618531aa7228325dfc54a7f1659397f SHA1: 727f655c5258834c815694125f379f4c1df004f2 SHA256: 9c634bbf7c5a5412dc506f443e42fc674767f1f0dde7d7e421045540b507624c SHA512: 1a61b4cd76e32c4ee853152dd634e6f35d5a04ca0be04faef4bc271d9b2101471ae94f30c0fa66dccd25333639eb33f8e9a18c0ef586eb72928c37a3c4ce6c4b Homepage: https://cran.r-project.org/package=RobustAFT Description: CRAN Package 'RobustAFT' (Truncated Maximum Likelihood Fit and Robust Accelerated FailureTime Regression for Gaussian and Log-Weibull Case) R functions for the computation of the truncated maximum likelihood and the robust accelerated failure time regression for gaussian and log-Weibull case. Package: r-cran-robustarima Architecture: arm64 Version: 0.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 288 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-splustimedate, r-cran-splustimeseries Filename: pool/dists/resolute/main/r-cran-robustarima_0.2.7-1.ca2604.1_arm64.deb Size: 171628 MD5sum: dcd3d220521d8a072b0b927ebdb49f1c SHA1: ad3a2f4e5066bab9ed140f5e263b6c0c10313392 SHA256: 6752d13ebf21d5fff5592f14522d29ac10f230d49fa0ae838bc4c246638f86a2 SHA512: 17071c508c299893d13a05a3f7486b45611ca7dfdc6cc8473789260bc84f2a352b33f036aec5833d257831f90d1c30997e36864a77d0fbfb8ed7053c49fd2662 Homepage: https://cran.r-project.org/package=robustarima Description: CRAN Package 'robustarima' (Robust ARIMA Modeling) Functions for fitting a linear regression model with ARIMA errors using a filtered tau-estimate. The methodology is described in Maronna et al (2017, ISBN:9781119214687). Package: r-cran-robustbase Architecture: arm64 Version: 0.99-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3696 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-deoptimr Suggests: r-cran-mass, r-cran-lattice, r-cran-boot, r-cran-cluster, r-cran-matrix, r-cran-robust, r-cran-fit.models, r-cran-mpv, r-cran-xtable, r-cran-ggplot2, r-cran-ggally, r-cran-rcolorbrewer, r-cran-reshape2, r-cran-sfsmisc, r-cran-catdata, r-cran-doparallel, r-cran-foreach, r-cran-skewt Filename: pool/dists/resolute/main/r-cran-robustbase_0.99-7-1.ca2604.1_arm64.deb Size: 3066402 MD5sum: 8f1a7c8cda5a2e37aeb0a56bd6e9b9db SHA1: f7dcf78ea111ef731ec6aea9fa3bce9acf4de157 SHA256: c4bd44f419924ee260f0b3391217e080c308faa5ba2ac4c53094eead2c245652 SHA512: 72edf3a6269dfc4f9cafb7c722553a1d48e65297597a1ae07b90bd5316e18764d1902481982451a70bfc0887403d45b78315e74b6daf962ced8d7a230ef059f4 Homepage: https://cran.r-project.org/package=robustbase Description: CRAN Package 'robustbase' (Basic Robust Statistics) "Essential" Robust Statistics. Tools allowing to analyze data with robust methods. This includes regression methodology including model selections and multivariate statistics where we strive to cover the book "Robust Statistics, Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006. Package: r-cran-robustbayesiancopas Architecture: arm64 Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 109 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-statip, r-cran-rjags Filename: pool/dists/resolute/main/r-cran-robustbayesiancopas_2.0-1.ca2604.1_arm64.deb Size: 77678 MD5sum: 26c072574265487ea21bf19ba645c0a9 SHA1: f9f9551bffbf186cf77e88ea3408c9924fc2cf59 SHA256: 2027287eae7ce072400b5fdea0a6b979b97fb1dba59bf77de2d942b6508a093d SHA512: 712298228d334d3c3e64da173b153a660b92fed0e99217137c49987a39fad95b7cd2a80c3df2e274b5b0b9f4d779ee42d99d886ff12ae7aa0dda8301860eac53 Homepage: https://cran.r-project.org/package=RobustBayesianCopas Description: CRAN Package 'RobustBayesianCopas' (Robust Bayesian Copas Selection Model) Fits the robust Bayesian Copas (RBC) selection model of Bai et al. (2020) for correcting and quantifying publication bias in univariate meta-analysis. Also fits standard random effects meta-analysis and the Copas-like selection model of Ning et al. (2017) . Package: r-cran-robustcalibration Architecture: arm64 Version: 0.5.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 825 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-robustgasp, r-cran-nloptr, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-robustcalibration_0.5.6-1.ca2604.1_arm64.deb Size: 542276 MD5sum: 97f23dd9a1c78d3bdc07bea5849fd08d SHA1: 20ac77a1a4e2cd2dee180c3341731222357212ad SHA256: 708601c7c58ffb3ede5d417a3eee5f3d9e47d07a33dfb3c839ebfc1252b7012e SHA512: baa40bf55482e5feebcbc1916670844dd6f2660cde72c666398a9211fbc7715d2edb89250a1c45220a696799826e5fdcb9f21f1d5e24600b530d1ced78d345ff Homepage: https://cran.r-project.org/package=RobustCalibration Description: CRAN Package 'RobustCalibration' (Robust Calibration of Imperfect Mathematical Models) Implements full Bayesian analysis for calibrating mathematical models with new methodology for modeling the discrepancy function. It allows for emulation, calibration and prediction using complex mathematical model outputs and experimental data. See the reference: Mengyang Gu and Long Wang, 2018, Journal of Uncertainty Quantification; Mengyang Gu, Fangzheng Xie and Long Wang, 2022, Journal of Uncertainty Quantification; Mengyang Gu, Kyle Anderson and Erika McPhillips, 2023, Technometrics. Package: r-cran-robustest Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2388 Depends: libc6 (>= 2.17), 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-robustest_1.1.0-1.ca2604.1_arm64.deb Size: 2302468 MD5sum: 35904c302b0f640ef42876117cb45c18 SHA1: 356f416daebe9531923d181aa011d76c69e4b504 SHA256: 5461bd8266adcc406f4c3ec60b36424a284e63296b8238d47ff5f12743c69461 SHA512: 6809191583c685f118c51af8845c81d78a02d223a9a4c52b589ec6cb28b1c951733340274eddab66464e23cc704b6b31018d5a1d665b20f2e00582b4ac75cf89 Homepage: https://cran.r-project.org/package=robusTest Description: CRAN Package 'robusTest' (Calibrated Correlation and Two-Sample Tests) Implementation of corrected two-sample tests. A corrected version of the Pearson and Kendall correlation tests, the Mann-Whitney (Wilcoxon) rank sum test, the Wilcoxon signed rank test and a variance test are implemented. The package also proposes a test for the median and an independence test between two continuous variables of Kolmogorov-Smirnov's type. All these corrected tests are asymptotically calibrated in the sense that the probability of rejection under the null hypothesis is asymptotically equal to the level of the test. See for more details on the statistical tests. Package: r-cran-robustetm Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 123 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-robustetm_1.0-1.ca2604.1_arm64.deb Size: 29068 MD5sum: 13f425ed342f0c337a5bdc8e27e47134 SHA1: 5341534a0bf132825f4e01da2b78bc0f682f4311 SHA256: d4341edbd6c9473ce2da0ab75114ab6b9d7f066fa730200361d4ab250f7aac6d SHA512: 12e1bfb7db7b45ab49fba30584acfe2507c0f0bdbc3389b4675746bdffc842d86464c412cfe85b199078c8204d88369008ee93e2ca63f87015ab05bafc13c345 Homepage: https://cran.r-project.org/package=robustETM Description: CRAN Package 'robustETM' (Robust Methods using Exponential Tilt Model) Testing homogeneity for generalized exponential tilt model. This package includes a collection of functions for (1) implementing methods for testing homogeneity for generalized exponential tilt model; and (2) implementing existing methods under comparison. Package: r-cran-robustgasp Architecture: arm64 Version: 0.6.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1013 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, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-robustgasp_0.6.8-1.ca2604.1_arm64.deb Size: 652718 MD5sum: ab283715682f4e5b8879a71918ff30a3 SHA1: 26b522547f98b3d1500d9e7d9df41f1ae34c27ef SHA256: 506fd40d723d09beb145bc26572f619bdb3f9bb47fde54920b6e328c76f2109f SHA512: ebe80986638bd84ec83a1b2eb04245b1b5bb72f1dae0d3b0de96b9e8528f827b7258cda9fe555cdd5c5cf8efdb17a79def0e624cd517451a3d64884a02edc0be Homepage: https://cran.r-project.org/package=RobustGaSP Description: CRAN Package 'RobustGaSP' (Robust Gaussian Stochastic Process Emulation) Robust parameter estimation and prediction of Gaussian stochastic process emulators. It allows for robust parameter estimation and prediction using Gaussian stochastic process emulator. It also implements the parallel partial Gaussian stochastic process emulator for computer model with massive outputs See the reference: Mengyang Gu and Jim Berger, 2016, Annals of Applied Statistics; Mengyang Gu, Xiaojing Wang and Jim Berger, 2018, Annals of Statistics. Package: r-cran-robusthd Architecture: arm64 Version: 0.8.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2407 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-ggplot2, r-cran-perry, r-cran-robustbase, r-cran-mass, r-cran-rcpp, r-cran-rlang, r-cran-rcpparmadillo Suggests: r-cran-lars, r-cran-mvtnorm, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-robusthd_0.8.4-1.ca2604.1_arm64.deb Size: 2133156 MD5sum: 088296e70e6479f59b933b25b57c1c3a SHA1: be6e95591f05d0a4f322f89d0de37ce092eea908 SHA256: a9dc6f59e8d4ffe8805af9e53e3acae2c1b88f1542857fb3f3fe472e64e2aae1 SHA512: 758c0ae61339e5b68ada10930a46a636fa7a0ef4ff0538928caec92ca9d0336448123064a354104ad5c4db6a9e8d251df6b1e7fb74cbaa1497a1d43ff9bcd297 Homepage: https://cran.r-project.org/package=robustHD Description: CRAN Package 'robustHD' (Robust Methods for High-Dimensional Data) Robust methods for high-dimensional data, in particular linear model selection techniques based on least angle regression and sparse regression. Specifically, the package implements robust least angle regression (Khan, Van Aelst & Zamar, 2007; ), (robust) groupwise least angle regression (Alfons, Croux & Gelper, 2016; ), and sparse least trimmed squares regression (Alfons, Croux & Gelper, 2013; ). 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Robustness is achieved by modification of the scoring equations combined with the Design Adaptive Scale approach. 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Package: r-cran-robustrank Architecture: arm64 Version: 2024.1-28-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-kyotil Suggests: r-cran-runit, r-cran-vgam, r-cran-copula, r-cran-mvtnorm, r-cran-pracma Filename: pool/dists/resolute/main/r-cran-robustrank_2024.1-28-1.ca2604.1_arm64.deb Size: 130158 MD5sum: 213669879da8ed70c4225652da9da56e SHA1: 0cb8938816f6f28ea89bc228fe058fbe80bb9f51 SHA256: 4f9f3fabb66711617cd1f6666e61990a945e9ad2f0008eb724894703031b8c1c SHA512: ae768a811f3fd096c626df9cf5dea002052318dc7da9fb31c57234c4a7dbc8b6b069782289ef9a59821b742d77c69473bd111aaf873a497cbbc22e0454a21d2d Homepage: https://cran.r-project.org/package=robustrank Description: CRAN Package 'robustrank' (Robust Rank-Based Tests) Implements two-sample tests for paired data with missing values (Fong, Huang, Lemos and McElrath 2018, Biostatics, ) and modified Wilcoxon-Mann-Whitney two sample location test, also known as the Fligner-Policello test. Package: r-cran-robustreg Architecture: arm64 Version: 0.1-11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 Depends: libc6 (>= 2.17), 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 Filename: pool/dists/resolute/main/r-cran-robustreg_0.1-11-1.ca2604.1_arm64.deb Size: 64954 MD5sum: 7a482b2a61223433ea1f3e5d2f10b93f SHA1: 5fe7767e8a323a97c41a5f19846356866d25ceb5 SHA256: ad10e7fba416565eef534c7368d37434a51ca6dc2a83fb03fde58e0d0147481e SHA512: cf5b858ed8d6c503a0df90e1b61b2c213d20755706bd3afc2a792203f54f75cf7c6d49a386341221b135338b13aaefc9cd47b580fb5380581c3c039e30ec32dd Homepage: https://cran.r-project.org/package=robustreg Description: CRAN Package 'robustreg' (Robust Regression Functions) Linear regression functions using Huber and bisquare psi functions. Optimal weights are calculated using IRLS algorithm. Package: r-cran-robustsfa Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 212 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-truncnorm, r-cran-rcpp, r-cran-frontier, r-cran-bh Filename: pool/dists/resolute/main/r-cran-robustsfa_0.2.0-1.ca2604.1_arm64.deb Size: 85152 MD5sum: 1e07c262511f2b6d4884b32c55e7ddbe SHA1: 2c101d8d570009b4393d21ddca8f26930d240185 SHA256: 58087a68b709d6ab7e7581a1dccbcf29b1d881c29eeeed38840fa3649027834d SHA512: f6918b4cfd80391d255b0bd56246b7afa92a188d5bb34c6a221e3a87b34f456bc021be385bfe28cfebbd10f4193ed2c6e6e5a1cd5c4ba119b34f9965ba7ff5e6 Homepage: https://cran.r-project.org/package=robustSFA Description: CRAN Package 'robustSFA' (Robust Estimation of Stochastic Frontier Models with MDPDE) This provides a robust estimator for stochastic frontier models, employing the Minimum Density Power Divergence Estimator (MDPDE) for enhanced robustness against outliers. 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Package: r-cran-rococo Architecture: arm64 Version: 1.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 839 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 Filename: pool/dists/resolute/main/r-cran-rococo_1.1.10-1.ca2604.1_arm64.deb Size: 588436 MD5sum: 38381ce721555ce27bd9daa071abc3f5 SHA1: 488756652cec581dad2e581793323e5ee468c2bd SHA256: 243de4efc8387c3c92147995d65f2fe6ef0845159c847f6616f7a472810e393b SHA512: c81ff67fe0dba45369a909884aac093ba4f5fc7657e826d87a56962467524bb82ca1e2a23d01628f2f18a71b6dc374f623d163eab17daacb88c166a24efa1667 Homepage: https://cran.r-project.org/package=rococo Description: CRAN Package 'rococo' (Robust Rank Correlation Coefficient and Test) Provides the robust gamma rank correlation coefficient as introduced by Bodenhofer, Krone, and Klawonn (2013) along with a permutation-based rank correlation test. 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The 'rollshap' package decomposes the coefficient of determination (R-squared) of a linear regression into nonnegative contributions from each explanatory variable using the Shapley value from cooperative game theory (Shapley, 1953, ). For each window, the exact Shapley value is computed by fitting all subsets of the explanatory variables and averaging the marginal contribution to R-squared across all orderings, which returns an order-invariant attribution that sums to the full-model R-squared. Use cases include variable importance, factor attribution, and feature selection in time-series regression. The package supports rolling and expanding windows, weights, and handling of missing values via 'min_obs', 'complete_obs', and 'na_restore' arguments. The implementation uses the online and offline algorithms from the 'roll' package to compute rolling and expanding cross-products efficiently with parallelism across columns and windows provided by 'RcppParallel'. 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Includes routines that: (1) generate gradient and jacobian matrices (full and banded), (2) find roots of non-linear equations by the 'Newton-Raphson' method, (3) estimate steady-state conditions of a system of (differential) equations in full, banded or sparse form, using the 'Newton-Raphson' method, or by dynamically running, (4) solve the steady-state conditions for uni-and multicomponent 1-D, 2-D, and 3-D partial differential equations, that have been converted to ordinary differential equations by numerical differencing (using the method-of-lines approach). Includes fortran code. Package: r-cran-ropj Architecture: arm64 Version: 0.3-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 391 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_arm64.deb Size: 126972 MD5sum: 47ef042057df2a67bee225f321aa713a SHA1: 818b13fc455492cc16d479cf7addee5c60b2de1d SHA256: 03c1daceaaead52f22a6d1675f863741553ca9c212da7415cdf5033f8c109d5c SHA512: 72d5cd9a11d01ad3cae1af9811eded96e7a9e5a3b71f2ba599602fb643158c33f7243ec187beba121fbc8f1f1c9032a648d63db1abee226d69e7b7138f69f0c7 Homepage: https://cran.r-project.org/package=Ropj Description: CRAN Package 'Ropj' (Import Origin(R) Project Files) Read the data from Origin(R) project files ('*.opj') . No write support is planned. Package: r-cran-roptim Architecture: arm64 Version: 0.1.7-1.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-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_arm64.deb Size: 251476 MD5sum: 5aab758554f58f826ba2020fe0ddb05e SHA1: 9af37165cc0ad44d7568b6a008ba2ff7913cb593 SHA256: 36eec4bbcf8dc95f98996466ae1c668c0618524d8031dafae25ae15943b316fa SHA512: 19530181901077cdc3a26684df1d2491e1c11c2da80351b597addf1121d2fae71736f1f0ea60040a061282b7c407011c076b90cbbfbf232a4388b3a83db316b7 Homepage: https://cran.r-project.org/package=roptim Description: CRAN Package 'roptim' (General Purpose Optimization in R using C++) Perform general purpose optimization in R using C++. A unified wrapper interface is provided to call C functions of the five optimization algorithms ('Nelder-Mead', 'BFGS', 'CG', 'L-BFGS-B' and 'SANN') underlying optim(). 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This package provides a method called OptSpace, which was proposed by Keshavan, R.H., Oh, S., and Montanari, A. (2009) for a case under low-rank assumption. Package: r-cran-rotasym Architecture: arm64 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_arm64.deb Size: 1857340 MD5sum: caf30b21da77fb63885ebd7de07ded10 SHA1: ea8f7d4555399a201ff5c8a52e4c647e456e9be2 SHA256: dadc2fb333384f1b9c6475997b10c395e163687571760763251910c5493cc8ba SHA512: b2f1a877caf6dd375c21da58957adb329db500f8aeaf44cb7606f77db44f3c2ed747f26570c0d44d6be02b625885fcce477ad05ca0ac48f5e521491d8fdbfc09 Homepage: https://cran.r-project.org/package=rotasym Description: CRAN Package 'rotasym' (Tests for Rotational Symmetry on the Hypersphere) Implementation of the tests for rotational symmetry on the hypersphere proposed in García-Portugués, Paindaveine and Verdebout (2020) . The package also implements the proposed distributions on the hypersphere, based on the tangent-normal decomposition, and allows for the replication of the data application considered in the paper. Package: r-cran-rotations Architecture: arm64 Version: 1.6.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5516 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_arm64.deb Size: 5092868 MD5sum: 9402bcc26abacdc00c75ddadafbfcbac SHA1: bb56729821083bb4619a71aed683f4bd777da4d2 SHA256: 39f90d6626d15bff025cd4a0c6fb98418a16b22305e54b7ccb506fa2d48682d7 SHA512: 7d4f52ce08a6154cb9fa4648b849fe0abbfbde8d968329572cc8ca8a8fc1f11794b72a1bf5dc7967ea4cfa124ed685b619febc75e6785b974f8626113b712c0c 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: arm64 Version: 1.3-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 888 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 691344 MD5sum: 61a894a7ccded6abd2c5f3ff4ab7b819 SHA1: c16f8471f99157bf971d63016f4d5b9542e36221 SHA256: 71f5a47f66fc64ee8ef5b9be522411725c1c9ca9c4a87aa90808be6d728a63e4 SHA512: d522c5dcfb842c2d9e1b97d352cceeebefb546a94352ceb8687a7e117c0b547ef53a13d33d9460a35864ba9a7107377b43e25aa5880f86e742c311bf5ce37b38 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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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). 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Package: r-cran-rpatternjoin Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 107510 MD5sum: fbbd13f77049177c6b7f88d64d5d30bc SHA1: 8a57010f57e805036d4e4cc65504e689215b2896 SHA256: e3b3a1dbad768e6d065d99bc7e77af17acbe76cf15651471febc75c7d91b2833 SHA512: 128f2e282aac5b4d2478882f0e08218a8566cfb748c162a62acfaa3be5e2c6cff9477513a26ec59e9eea5c86d2783ceb2cbdc0520bc12384492825691aca4568 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. It works for Levenshtein/Hamming distances and words from any alphabet. The software was originally developed for joining amino-acid/nucleotide sequences from Adaptive Immune Repertoires, where the number of words is relatively large (10^5-10^6) and the average length of words is relatively small (10-100). Package: r-cran-rpbk Architecture: arm64 Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3185 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-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-loo, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rpbk_0.2.5-1.ca2604.1_arm64.deb Size: 1431296 MD5sum: da63fb2ed15109bf1065be227fc4bf3b SHA1: 7c0026ca8578128977b6cc120be8c258a1f69a19 SHA256: cc6b041adbc945dae8396a0938100a37f69d75c43a74a0abdd33305e4de386b0 SHA512: e89e72d759e8cb07d6e5b647c223e238fe982fb46aadd9d055b23d3f861e1c9e2cbdfda3d6a4cfe0dbbc66375ce7068625ca7c11d210c824a359caf75be66db1 Homepage: https://cran.r-project.org/package=rPBK Description: CRAN Package 'rPBK' (Inference and Prediction of Generic Physiologically-BasedKinetic Models) Fit and simulate any kind of physiologically-based kinetic ('PBK') models whatever the number of compartments. Moreover, it allows to account for any link between pairs of compartments, as well as any link of each of the compartments with the external medium. Such generic PBK models have today applications in pharmacology (PBPK models) to describe drug effects, in toxicology and ecotoxicology (PBTK models) to describe chemical substance effects. In case of exposure to a parent compound (drug or chemical) the 'rPBK' package allows to consider metabolites, whatever their number and their phase (I, II, ...). Last but not least, package 'rPBK' can also be used for dynamic flux balance analysis (dFBA) to deal with metabolic networks. See also Charles et al. (2022) . Package: r-cran-rpc Architecture: arm64 Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 192 Depends: libc6 (>= 2.17), 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 Suggests: r-cran-matrixextra Filename: pool/dists/resolute/main/r-cran-rpc_2.0.3-1.ca2604.1_arm64.deb Size: 68160 MD5sum: 7238f314d9e33b7a8c50e70a1cc45166 SHA1: 341edd974989d2204f561bb0b33d1f5becda638c SHA256: 5dd889a930f2a88cfd39128333085479ce9b4a315d663817e202fcec96504e89 SHA512: ca145cd77049a9bd96905ce9ab0529c7215b563641dd36c190ede4b863280368243d7080ca9784696ec3d64740008d9087554e8e920e35a7cab89b9173e87313 Homepage: https://cran.r-project.org/package=rpc Description: CRAN Package 'rpc' (Ridge Partial Correlation) Computes the ridge partial correlation coefficients in a high or ultra-high dimensional linear regression problem. An extended Bayesian information criterion is also implemented for variable selection. Users provide the matrix of covariates as a usual dense matrix or a sparse matrix stored in a compressed sparse column format. Detail of the method is given in the manual. Package: r-cran-rpeglmen Architecture: arm64 Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 449 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-rpeif, r-cran-rcppeigen Suggests: r-cran-r.rsp, r-cran-testthat, r-cran-performanceanalytics Filename: pool/dists/resolute/main/r-cran-rpeglmen_1.1.4-1.ca2604.1_arm64.deb Size: 285096 MD5sum: c159c0ef3394451e744559ccb2d3f07c SHA1: 6cdab59a8a7539e7a64150889ed1bf37c26e8db3 SHA256: ad6b005261b580ff65c4eb27d807ae7a7e3f6f9bf69e31bfd320c687ffd049db SHA512: ff8a04bc069b5126c8e6739f494fb63eb623226f944cdc0231827387803bebf75a499502fc273cd74ff74f631b454a94ff13d5b0f02314b80800452a71fa5d1b Homepage: https://cran.r-project.org/package=RPEGLMEN Description: CRAN Package 'RPEGLMEN' (Gamma and Exponential Generalized Linear Models with Elastic NetPenalty) Implements the fast iterative shrinkage-thresholding algorithm (FISTA) algorithm to fit a Gamma distribution with an elastic net penalty as described in Chen, Arakvin and Martin (2018) . An implementation for the case of the exponential distribution is also available, with details available in Chen and Martin (2018) . Package: r-cran-rpesto Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3854 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_arm64.deb Size: 1160048 MD5sum: 4849e925537d9ffe9bdb2d46ea92573a SHA1: 1c175c2b728758ac26253b90f5827ddf45dcaa57 SHA256: c8b7321f724f4bbb961a8d6815bda96764846b05b382fe83056de302684dffe3 SHA512: 545e1a706147b1d5da53de1056c9e7d67dc29080cdd27b8d998d04cc47e92ed94bb607868711ead4f23cbe3ee0fbbaee9158f38e39cfb3b16767bdc4164e4546 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. It leverages belief propagation techniques to calculate branch-specific diversification rates, see Kopperud & Hoehna (2025) . Package: r-cran-rpf Architecture: arm64 Version: 1.0.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1465 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-mvtnorm, r-cran-lifecycle, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-roxygen2, r-cran-ggplot2, r-cran-reshape2, r-cran-gridextra, r-cran-numderiv, r-cran-knitr, r-cran-mirt, r-cran-markdown Filename: pool/dists/resolute/main/r-cran-rpf_1.0.15-1.ca2604.1_arm64.deb Size: 897762 MD5sum: 91e39a8a422e3ce01dbd618400b7e5b1 SHA1: 3f2d2fd0322d644009d090018eba197609b21627 SHA256: fde0a7cccd4216099482bab45a7a9b98e9f087d672435f4df1c92de8f178aa01 SHA512: f85fe4c8dedb7672932b53414abac2a02f5bb9fd7d9f4d197c91178c0c8a1d4299b0c99aa0114bee13339503376b73d8df3be8b36f69a0a73ffd86071d2ec32a Homepage: https://cran.r-project.org/package=rpf Description: CRAN Package 'rpf' (Response Probability Functions) Factor out logic and math common to Item Factor Analysis fitting, diagnostics, and analysis. It is envisioned as core support code suitable for more specialized IRT packages to build upon. Complete access to optimized C functions are made available with R_RegisterCCallable(). This software is described in Pritikin & Falk (2020) . Package: r-cran-rphosfate Architecture: arm64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2194 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-checkmate, r-cran-rcpp, r-cran-terra, r-cran-yaml, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-tinytest, r-cran-whitebox Filename: pool/dists/resolute/main/r-cran-rphosfate_2.0.1-1.ca2604.1_arm64.deb Size: 1545756 MD5sum: c5b0d1491ef05f73151fefe870216df1 SHA1: 1761362b896890c4fccee53e5bed08377a39fbfb SHA256: b84215470c1f15929fe3e5c7ab08c280bc4ffc9f797028b58f5bb0d5a717ce63 SHA512: d8de9a4d6e53f4dad00175cbdb6dcebb08ea068acfd15a88ec2a1e7466bd9c2a59f167be56e6261352411a6eefd79e0b409b0c6bf4b7c67ee576f7d4ef7d9db5 Homepage: https://cran.r-project.org/package=RPhosFate Description: CRAN Package 'RPhosFate' (Soil and Chemical Substance Emission and Transport Model) An enhanced version of the semi-empirical, spatially distributed emission and transport model PhosFate implemented in 'R' and 'C++'. It is based on the D-infinity, but also supports the D8 flow method. The currently available substances are suspended solids (SS) and particulate phosphorus (PP). A major feature is the allocation of substance loads entering surface waters to their sources of origin, which is a basic requirement for the identification of critical source areas and in consequence a cost-effective implementation of mitigation measures. References: Hepp et al. (2022) ; Hepp and Zessner (2019) ; Kovacs (2013) . Package: r-cran-rphylopars Architecture: arm64 Version: 0.3.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 601 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ape, r-cran-rcpp, r-cran-doby, r-cran-phylolm, r-cran-phytools, r-cran-matrix, r-cran-mass, r-cran-numderiv, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-rphylopars_0.3.10-1.ca2604.1_arm64.deb Size: 320692 MD5sum: ed03211eb9e45f9f5e64b8af6265f76b SHA1: a1a1cb8139eb0987990c01939c3df7b2a9a44447 SHA256: 8701c90f25a9ee231adfb240da4a98f6febf039f3da8d18af946a1f02ae0a233 SHA512: 1c2bd0ced9b66c222d6642176f823edaaf8da8cd4093c17d75d876ed4b4c5395cca81e8793f92f122287fd6acdb2b0b9ed92ae8fec8c3665b62390dc5cc7de81 Homepage: https://cran.r-project.org/package=Rphylopars Description: CRAN Package 'Rphylopars' (Phylogenetic Comparative Tools for Missing Data andWithin-Species Variation) Tools for performing phylogenetic comparative methods for datasets with with multiple observations per species (intraspecific variation or measurement error) and/or missing data (Goolsby et al. 2017). 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. Package: r-cran-rpm Architecture: arm64 Version: 0.7-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 905 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-future, r-cran-dorng, r-cran-rcpp, r-cran-nloptr, r-cran-matrixstats, r-cran-mass, r-cran-dplyr, r-cran-ggplot2, r-cran-coda, r-cran-dofuture, r-cran-foreach, r-cran-rcpparmadillo Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rpm_0.7-4-1.ca2604.1_arm64.deb Size: 577986 MD5sum: 62d5fc0978625f2ae7f2163fe4b9a3bd SHA1: 74da6ddc7dd080e9100eafb7433a7d1affff2dc0 SHA256: e016a2ef6b971577bddc49f79e31fca2a45ed28184e3e20133a32df494262dee SHA512: c4e14f187fa14d1cb0a04b0c4032dae7f0cbe93b0ea08177976bb3eb7fb8d9d6aca8f8cf1fd8097015dbe3729ad3f7a829836d1b748846319db465a7d758dd0e Homepage: https://cran.r-project.org/package=rpm Description: CRAN Package 'rpm' (Modeling of Revealed Preferences Matchings) Statistical estimation of revealed preference models from data collected on bipartite matchings. The models are for matchings within a bipartite population where individuals have utility for people based on known and unknown characteristics. People can form a partnership or remain unpartnered. The model represents both the availability of potential partners of different types and preferences of individuals for such people. The software estimates preference parameters based on sample survey data on partnerships and population composition. The simulation of matchings and goodness-of-fit are considered. See Goyal, Handcock, Jackson, Rendall and Yeung (2022) . Package: r-cran-rpms Architecture: arm64 Version: 0.5.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4388 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 2928508 MD5sum: f1606c83a2e35e5633adb17709c6f686 SHA1: d81781e214cfb9de289dce4be042915d3cdc8da8 SHA256: 8b42ecc525487c0f6144caec44de20d28a863599e8cc84cb75b4922b7e79eb02 SHA512: ef198382334930e9f39431653952c058abc5ca4c229a5a7de26796d8759a96074781f284c2665187b44abd618a8c5a044c5f612fef5b81952edb800f4e094850 Homepage: https://cran.r-project.org/package=rpms Description: CRAN Package 'rpms' (Recursive Partitioning for Modeling Survey Data) Functions to allow users to build and analyze design consistent tree and random forest models using survey data from a complex sample design. The tree model algorithm can fit a linear model to survey data in each node obtained by recursively partitioning the data. The splitting variables and selected splits are obtained using a randomized permutation test procedure which adjusted for complex sample design features used to obtain the data. Likewise the model fitting algorithm produces design-consistent coefficients to any specified least squares linear model between the dependent and independent variables used in the end nodes. The main functions return the resulting binary tree or random forest as an object of "rpms" or "rpms_forest" type. The package also provides methods modeling a "boosted" tree or forest model and a tree model for zero-inflated data as well as a number of functions and methods available for use with these object types. Package: r-cran-rpoppler Architecture: arm64 Version: 0.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 121 Depends: libc6 (>= 2.17), libglib2.0-0t64 (>= 2.12.0), libpoppler-glib8t64 (>= 0.18.0), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rpoppler_0.1-3-1.ca2604.1_arm64.deb Size: 29120 MD5sum: 74a192b0513a52f46861105a26f2937e SHA1: 38ad6dd1c6187eefa78551dd9a2a884d61cfac50 SHA256: 8b8309f884d8bd008f8f8680f8083160f9efbcbb66d3b5e8bfc9550213b200ca SHA512: 74656ad56ba6ccb46ee947b0db92e745bfa6ccf7db1c9e6bbd2fcef32d5a59e8e7032bf735dc8ef3b62c5f38e5e604cab16e3bae516d94131f4399d65740af8d Homepage: https://cran.r-project.org/package=Rpoppler Description: CRAN Package 'Rpoppler' (PDF Tools Based on Poppler) PDF tools based on the Poppler PDF rendering library. See for more information on Poppler. Package: r-cran-rpostgres Architecture: arm64 Version: 1.4.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 757 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libpq5 (>= 9.2~beta3), libstdc++6 (>= 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_arm64.deb Size: 432940 MD5sum: daedce5e02e4b7b405aef444ed09fd5c SHA1: 134e9dabfea012a52eb30f63c485421b2f2196aa SHA256: 476a60338987266fb188f01c36c7dbc1518be0c9cde1bc7fd626e36aea3f3eac SHA512: 1bf31c08dccb364b5cfd60b0a4bd5060e4a8fd7a853a7c87bcd3103d20f31ac8aa0750b3c34187bdd2c2d42b59f7716cc9592d30a680f775460ba6b9d97f1e55 Homepage: https://cran.r-project.org/package=RPostgres Description: CRAN Package 'RPostgres' (C++ Interface to PostgreSQL) Fully DBI-compliant C++-backed interface to PostgreSQL , an open-source relational database. Package: r-cran-rpostgresql Architecture: arm64 Version: 0.7-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 546 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_arm64.deb Size: 364266 MD5sum: 7a0dec693404f8317a7b5df3c7ac5e72 SHA1: dc6516a3941fb8fdd67170bcf48f88555cbba070 SHA256: 8ca7bf2cac0bed3d62278fad11973504d288fb4c741a0f316f4c1865d8c5c572 SHA512: 2bcc8300b315f0b6a402f7e20803348175f3e9e4aad92d303f5d8baad809e40102371db39a93fbdc377bb32f6837cbc8c5a09fd0bc82f7ad67aa04ba09cd03fd Homepage: https://cran.r-project.org/package=RPostgreSQL Description: CRAN Package 'RPostgreSQL' (R Interface to the 'PostgreSQL' Database System) Database interface and 'PostgreSQL' driver for 'R'. This package provides a Database Interface 'DBI' compliant driver for 'R' to access 'PostgreSQL' database systems. In order to build and install this package from source, 'PostgreSQL' itself must be present your system to provide 'PostgreSQL' functionality via its libraries and header files. These files are provided as 'postgresql-devel' package under some Linux distributions. On 'macOS' and 'Microsoft Windows' system the attached 'libpq' library source will be used. Package: r-cran-rpql Architecture: arm64 Version: 0.8.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 184372 MD5sum: 50fed7fdb3a2c5d0e8e327b20b92d331 SHA1: ae55b8210bd3293c2a5ba330b961b5e7a8e96b70 SHA256: eaefaca4a2a93866b42a7a337bb56e12168b83e1689dbed847a2cbee924e61a4 SHA512: 1014fdd072f1a6e313e1596ec92f2b89a0b8b66669e304984ffb18d44814a9c0143871bf9ee368e69b6a4b3610666cd31864d7419f1b66073a13a08e3abc49f1 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. 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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) ). 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Types of models that can be estimated with this code include the family of discrete choice models (Multinomial Logit, Mixed Logit, Nested Logit, Error Components Logit and Latent Class) as well ordered response models like ordered probit and ordered logit. In addition, the package allows for flexibility in specifying parameters as either fixed (non-varying across individuals) or random with continuous distributions. Parameter distributions supported include normal, positive/negative log-normal, positive/negative censored normal, and the Johnson SB distribution. Kenneth Train's Matlab and Gauss code for doing Hierarchical Bayesian estimation has served as the basis for a few of the functions included in this package. These Matlab/Gauss functions have been rewritten to be optimized within R. Considerable code has been added to increase the flexibility and usability of the code base. Train's original Gauss and Matlab code can be found here: See Train's chapter on HB in Discrete Choice with Simulation here: ; and his paper on using HB with non-normal distributions here: . The authors would also like to thank the invaluable contributions of Stephane Hess and the Choice Modelling Centre: . Package: r-cran-rshift Architecture: arm64 Version: 3.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 719 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-dplyr, r-cran-ggplot2 Suggests: r-cran-r.rsp Filename: pool/dists/resolute/main/r-cran-rshift_3.1.2-1.ca2604.1_arm64.deb Size: 448326 MD5sum: d16cbd44f19b2e3dc448bbe872615aaf SHA1: 97eaba9f9d5914ef92be5ca788c65762094c020f SHA256: ff41ade1c64bff65e426ac2bbf40c140afb424f44e1e6f466a83e90db914aa73 SHA512: d516b76242fb5b905daddcf4ea05ba0604c2b10dcd72b03c1c75a8d100daa205b84e8e6da04aecad16a99fc87deeb07ab9deef1b32e6b7fc131169f6819f2820 Homepage: https://cran.r-project.org/package=rshift Description: CRAN Package 'rshift' (Paleoecology Functions for Regime Shift Analysis) Contains a variety of functions, based around regime shift analysis of paleoecological data. Citations: Rodionov() from Rodionov (2004) Lanzante() from Lanzante (1996) Hellinger_trans from Numerical Ecology, Legendre & Legendre (ISBN 9780444538680) rolling_autoc from Liu, Gao & Wang (2018) Sample data sets lake_data & lake_RSI processed from Bush, Silman & Urrego (2004) Sample data set January_PDO from NOAA: . Package: r-cran-rsides Architecture: arm64 Version: 0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 914 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-survival, r-cran-xml2, r-cran-foreach, r-cran-doparallel, r-cran-dorng, r-cran-officer, r-cran-flextable, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-rsides_0.1-1.ca2604.1_arm64.deb Size: 432872 MD5sum: 754b5587f8ed66390735b89a2ac287ae SHA1: 1d4890ad4d387e3a522a7e37fafbe005eb433b55 SHA256: 39c3174f0fd17b6aec71cd169a10deaa9a4190b209fa12dee94fae7c5b8c94b2 SHA512: 33e65b652fe3e90d7f02c1721800e383c38ce10c9df156d43ab83888f26a7034ebe70f8da18ef7e7f2bc9c6cb8a9b082a9cc19e7eeb529d39b8db275db8a92ed Homepage: https://cran.r-project.org/package=rsides Description: CRAN Package 'rsides' (SIDES-Based Subgroup Search Algorithms) R implementation of SIDES-based subgroup search algorithms (Lipkovich et al. (2017) ). Package: r-cran-rsiena Architecture: arm64 Version: 1.6.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3426 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix, r-cran-lattice, r-cran-mass, r-cran-xtable, r-cran-network Suggests: r-cran-codetools Filename: pool/dists/resolute/main/r-cran-rsiena_1.6.6-1.ca2604.1_arm64.deb Size: 2107858 MD5sum: 149f252604ce11fe990509241b870448 SHA1: c2621d12e1ceae579ced56011f8d19a8903d02e4 SHA256: 46ef1d1d4045cc28156a4c35794378bf74574cd16d5c88b3744e677c822ecb98 SHA512: 393370967a9b2584bd0957fee6c0b68e79e2e55b7524f5fe9fc389b69c3a709bda4c267f887ddea5ca41ec288f504745b6331330d1faf02ccfaf1066a2cc06e7 Homepage: https://cran.r-project.org/package=RSiena Description: CRAN Package 'RSiena' (Siena - Simulation Investigation for Empirical Network Analysis) The main purpose of this package is to perform simulation-based estimation of stochastic actor-oriented models for longitudinal network data collected as panel data. Dependent variables can be single or multivariate networks, which can be directed, non-directed, or two-mode; and associated actor variables. There are also functions for testing parameters and checking goodness of fit. An overview of these models is given in Snijders (2017), . Package: r-cran-rsixel Architecture: arm64 Version: 0.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 388 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-png Suggests: r-cran-jpeg, r-cran-magick Filename: pool/dists/resolute/main/r-cran-rsixel_0.0.4-1.ca2604.1_arm64.deb Size: 183912 MD5sum: ac57243db2354e1a62156cf0e575d4e9 SHA1: 865ab1ba835161aeb581119c98eaf27db01f213f SHA256: 9dea936ba86f17d0c9cda1a2752128f109c6bb6b108add097bbf0ce99c738be8 SHA512: e3da86a95089ff02729b5ffe5b8ee7938a008bbdf42d0aa0bf9c6d473d2697e1ff2f4371a93098db6416d10a95294ab7f8821142ba21e30f8aefe241ea518de8 Homepage: https://cran.r-project.org/package=rsixel Description: CRAN Package 'rsixel' (Encoding and Decoding Sixel Images) Provides a native R implementation for encoding and decoding 'sixel' graphics (), and a dedicated 'sixel' graphics device that allows plots to be rendered directly within compatible terminal emulators. Package: r-cran-rskc Architecture: arm64 Version: 2.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 712 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-flexclust Filename: pool/dists/resolute/main/r-cran-rskc_2.4.2-1.ca2604.1_arm64.deb Size: 627942 MD5sum: f4d1dae0b0accc1b62d4cc25c99d69c0 SHA1: 3f6d8ffba2df9ce141b6a93ec220d47e32fc977e SHA256: 382bf11c98ad72a5ba12a81e12a0a38c6cbc2df9f3977e4be475b30ef06d61f7 SHA512: 118ffe4a774a65294fb6c18362f9f0abc8c83c3e3d6194f0ff1ffe0aea833b73f01896baef0cd4f63c76086b7a982bf0e826e310542556bd5d3631281b9c3f4e Homepage: https://cran.r-project.org/package=RSKC Description: CRAN Package 'RSKC' (Robust Sparse K-Means) This RSKC package contains a function RSKC which runs the robust sparse K-means clustering algorithm. Package: r-cran-rsnns Architecture: arm64 Version: 0.4-18-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1747 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-scatterplot3d, r-cran-neuralnettools, r-cran-plot3d Filename: pool/dists/resolute/main/r-cran-rsnns_0.4-18-1.ca2604.1_arm64.deb Size: 1075004 MD5sum: d8fa4046799e21c1e41d89ed8a610288 SHA1: 90902b842c65e0b46dfc3feaae5bd204377b52f2 SHA256: 5a6211b6f9651015791ff2a9e42abc35615e33c4cab3ed9b9f2c52d82ee3c9db SHA512: 1815bceffe7ce3641551b4901bfe6bccb964fe80e95c30ce65dd6770705feae9897329a12ba11600c6f8776d7ae54640ae08ea82fc2deb44301c8e87254b4eb9 Homepage: https://cran.r-project.org/package=RSNNS Description: CRAN Package 'RSNNS' (Neural Networks using the Stuttgart Neural Network Simulator(SNNS)) The Stuttgart Neural Network Simulator (SNNS) is a library containing many standard implementations of neural networks. This package wraps the SNNS functionality to make it available from within R. Using the 'RSNNS' low-level interface, all of the algorithmic functionality and flexibility of SNNS can be accessed. Furthermore, the package contains a convenient high-level interface, so that the most common neural network topologies and learning algorithms integrate seamlessly into R. Package: r-cran-rsofun Architecture: arm64 Version: 5.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3156 Depends: libc6 (>= 2.43), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-purrr, r-cran-tidyr, r-cran-magrittr, r-cran-gensa, r-cran-bayesiantools, r-cran-lubridate, r-cran-multidplyr Suggests: r-cran-covr, r-cran-constructive, r-cran-cowplot, r-cran-rcmdcheck, r-cran-testthat, r-cran-rmarkdown, r-cran-ggplot2, r-cran-knitr, r-cran-sensitivity, r-cran-rpmodel, r-cran-rlang Filename: pool/dists/resolute/main/r-cran-rsofun_5.1.0-1.ca2604.1_arm64.deb Size: 2097152 MD5sum: e054f36c4afcbb25fea33f75103d5e9b SHA1: ae03e9ecdc643ff6690a6ae2527b11b6c8b70e29 SHA256: ac27b3d969011efc50265b13fe0e2d96f58d1277143b23cc3fe9aacfe4ac1324 SHA512: 0c6a225b5b4b06809634d070fd0fd575abfc54e34c0c93d88fec10cb69f34e576c49d83969eb51e3b370454ad4e5fc5171327610f023f6d7c4127de295412563 Homepage: https://cran.r-project.org/package=rsofun Description: CRAN Package 'rsofun' (The P-Model and BiomeE Modelling Framework) Implements the Simulating Optimal FUNctioning framework for site-scale simulations of ecosystem processes, including model calibration. It contains 'Fortran 90' modules for the P-model (Stocker et al. (2020) ), SPLASH (Davis et al. (2017) ) and BiomeE (Weng et al. (2015) ). Package: r-cran-rsolnp Architecture: arm64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 939 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-truncnorm, r-cran-numderiv, r-cran-future.apply, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-rsolnp_2.0.1-1.ca2604.1_arm64.deb Size: 612140 MD5sum: 94e6a3337343833f770d24cd9aec93f2 SHA1: d03b615e6205441d86bd91288520dc0ca9e76ab9 SHA256: 26771dead1884c2a4cb488cb97f8f2ba681bf006c11f295998cd1d01d4d7ff2e SHA512: 00599cdba8091f37940af69abf9414056f9cd2b6daccc56685702d405a10aa14d074c52bc8b58adc9eb5496b29bd0953edbaf48144ba18298c277d6e0eb840f8 Homepage: https://cran.r-project.org/package=Rsolnp Description: CRAN Package 'Rsolnp' (General Non-Linear Optimization) General Non-linear Optimization Using Augmented Lagrange Multiplier Method. Package: r-cran-rsomoclu Architecture: arm64 Version: 1.7.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 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-kohonen, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-rsomoclu_1.7.7-1.ca2604.1_arm64.deb Size: 59796 MD5sum: 57dfcabfd8abcbbb35d61f9fd1a2fef0 SHA1: c7f60c502be26ad10fe0b9f11ff545f285a64e66 SHA256: 627d236064004ca50533bcc9fc9832d93eefd8dd0be4ecd14ba67f98a58e5064 SHA512: 1fe07bc3d074926a173512664f368a05090264a9de323bc6d5cfdc0c9dfa12be2a8e1bafba5e1c33f546a82d23795c5645edb7009d69338d677c080cfc0fa337 Homepage: https://cran.r-project.org/package=Rsomoclu Description: CRAN Package 'Rsomoclu' (Somoclu) Somoclu is a massively parallel implementation of self-organizing maps. It exploits multicore CPUs and it can be accelerated by CUDA. The topology of the map can be planar or toroid and the grid of neurons can be rectangular or hexagonal . Details refer to (Peter Wittek, et al (2017)) . Package: r-cran-rspa Architecture: arm64 Version: 0.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 86674 MD5sum: 2c6ec889f5c9eb26d6c3e8e2c0726bc8 SHA1: 3db732dd21e2d37c02e76c888e8df48e6eec6502 SHA256: 3b5fb23f87ab99d8a7660e5b205b62ed00162caea6a57c1bbd0d68cb66fbe9ee SHA512: a22e29d8787ee3ce52def4a0bb50f1bdacf9bbf658ade97d18030517342f8a7cf7af8791181e4f8f211db60b52d05bccc6a069d3cb6f7207ae2b02dc750f55d4 Homepage: https://cran.r-project.org/package=rspa Description: CRAN Package 'rspa' (Adapt Numerical Records to Fit (in)Equality Restrictions) Minimally adjust the values of numerical records in a data.frame, such that each record satisfies a predefined set of equality and/or inequality constraints. The constraints can be defined using the 'validate' package. The core algorithms have recently been moved to the 'lintools' package, refer to 'lintools' for a more basic interface and access to a version of the algorithm that works with sparse matrices. Package: r-cran-rsparse Architecture: arm64 Version: 0.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1332 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.2), 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_arm64.deb Size: 801074 MD5sum: 1b45d7c2775e5fcfe191d2b27a25f57b SHA1: c85ad805e2b1e301f4614874e707d5fc1a14fd4e SHA256: ddcb0d4ea90eff6a378598ac4da27ea4470171b3ba6f313b8ae3e3bc3e72d630 SHA512: 883d52aacd78776de436d8085141b73d32d193a7f3bcb34a77092b70807a68495e670f8b42b95fc4b4c33a489401876c6c65c7e0bd5cac6f99bb5da364c871e7 Homepage: https://cran.r-project.org/package=rsparse Description: CRAN Package 'rsparse' (Statistical Learning on Sparse Matrices) Implements many algorithms for statistical learning on sparse matrices - matrix factorizations, matrix completion, elastic net regressions, factorization machines. Also 'rsparse' enhances 'Matrix' package by providing methods for multithreaded matrix products and native slicing of the sparse matrices in Compressed Sparse Row (CSR) format. List of the algorithms for regression problems: 1) Elastic Net regression via Follow The Proximally-Regularized Leader (FTRL) Stochastic Gradient Descent (SGD), as per McMahan et al(, ) 2) Factorization Machines via SGD, as per Rendle (2010, ) List of algorithms for matrix factorization and matrix completion: 1) Weighted Regularized Matrix Factorization (WRMF) via Alternating Least Squares (ALS) - paper by Hu, Koren, Volinsky (2008, ) 2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro (2005, ) 3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD, Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder et al. (2014, ) 4) Linear-Flow matrix factorization, from 'Practical linear models for large-scale one-class collaborative filtering' by Sedhain, Bui, Kawale et al (2016, ISBN:978-1-57735-770-4) 5) GlobalVectors (GloVe) matrix factorization via SGD, paper by Pennington, Socher, Manning (2014, ) Package is reasonably fast and memory efficient - it allows to work with large datasets - millions of rows and millions of columns. This is particularly useful for practitioners working on recommender systems. Package: r-cran-rspectra Architecture: arm64 Version: 0.16-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1462 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 424534 MD5sum: c9b7f0b7d5199a256527bf7d15a6110d SHA1: 39a795f3441cd08ee581e0d9f715083d118a27b1 SHA256: c3c7a26a77cb65976d27b6e6fcdcdd9db6437429bf7b889058f3f9596cda1d84 SHA512: 6239fceff2c38f48e66ec9416d2d6c15a62d39ee07c423f521af92476ff6f8aeb63160120e8c38cb1425b12ff5a5d308103099cca6054c2b4c83648d198f280d Homepage: https://cran.r-project.org/package=RSpectra Description: CRAN Package 'RSpectra' (Solvers for Large-Scale Eigenvalue and SVD Problems) R interface to the 'Spectra' library for large-scale eigenvalue and SVD problems. It is typically used to compute a few eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues, which is usually more efficient than eigen() if k << n. This package provides the 'eigs()' function that does the similar job as in 'Matlab', 'Octave', 'Python SciPy' and 'Julia'. It also provides the 'svds()' function to calculate the largest k singular values and corresponding singular vectors of a real matrix. The matrix to be computed on can be dense, sparse, or in the form of an operator defined by the user. Package: r-cran-rspectral Architecture: arm64 Version: 1.0.0.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1018 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 737988 MD5sum: f657baee4d4ff0cee133e82f41bf2661 SHA1: bc0cc211209634f3f28f1760a32a7f58135a4fae SHA256: 3e1d288e4878bf1c75632236e81dd440386a5b98627909cde590ef3bc522ea92 SHA512: 408e50535256ca5741396a73e1d615ecc3790c096632362bfe5be2ecadd83a749f2c4a55b07d43e3645ceaa89b235f551405da71648e4158282eef3c978fc705 Homepage: https://cran.r-project.org/package=rSpectral Description: CRAN Package 'rSpectral' (Spectral Modularity Clustering) Implements the network clustering algorithm described in Newman (2006) . The complete iterative algorithm comprises of two steps. In the first step, the network is expressed in terms of its leading eigenvalue and eigenvector and recursively partition into two communities. Partitioning occurs if the maximum positive eigenvalue is greater than the tolerance (10e-5) for the current partition, and if it results in a positive contribution to the Modularity. Given an initial separation using the leading eigen step, 'rSpectral' then continues to maximise for the change in Modularity using a fine-tuning step - or variate thereof. The first stage here is to find the node which, when moved from one community to another, gives the maximum change in Modularity. This node’s community is then fixed and we repeat the process until all nodes have been moved. The whole process is repeated from this new state until the change in the Modularity, between the new and old state, is less than the predefined tolerance. A slight variant of the fine-tuning step, which can improve speed of the calculation, is also provided. Instead of moving each node into each community in turn, we only consider moves of neighbouring nodes, found in different communities, to the community of the current node of interest. The two steps process is repeatedly applied to each new community found, subdivided each community into two new communities, until we are unable to find any division that results in a positive change in Modularity. 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The source for the SQLite engine and for various extensions is included. System libraries will never be consulted because this package relies on static linking for the plugins it includes; this also ensures a consistent experience across all installations. Package: r-cran-rsrd Architecture: arm64 Version: 0.1.8-1.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-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_arm64.deb Size: 156226 MD5sum: 8d847d6bc98939efc5dd8009bccbd4e0 SHA1: 14bb022865b8c8011829f6b1230bdbddb31d1f63 SHA256: fdb0f6a2716f709a84be9077ff2ea6396a854a5bbd80b65dab56097774d9ef6d SHA512: a0f41b6e42150193cd94d900988f1762e46d282aebd446549c6919e46128afe27803ad2efe525b00f93632960291d30936c6b7ceb7dc44a47d33d4f70ffd06a8 Homepage: https://cran.r-project.org/package=rSRD Description: CRAN Package 'rSRD' (Sum of Ranking Differences Statistical Test) We provide an implementation for Sum of Ranking Differences (SRD), a novel statistical test introduced by Héberger (2010) . The test allows the comparison of different solutions through a reference by first performing a rank transformation on the input, then calculating and comparing the distances between the solutions and the reference - the latter is measured in the L1 norm. The reference can be an external benchmark (e.g. an established gold standard) or can be aggregated from the data. The calculated distances, called SRD scores, are validated in two ways, see Héberger and Kollár-Hunek (2011) . A randomization test (also called permutation test) compares the SRD scores of the solutions to the SRD scores of randomly generated rankings. The second validation option is cross-validation that checks whether the rankings generated from the solutions come from the same distribution or not. For a detailed analysis about the cross-validation process see Sziklai, Baranyi and Héberger (2021) . The package offers a wide array of features related to SRD including the computation of the SRD scores, validation options, input preprocessing and plotting tools. Package: r-cran-rssa Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1659 Depends: libc6 (>= 2.17), libfftw3-double3 (>= 3.3.10), r-base-core (>= 4.5.0), 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_arm64.deb Size: 1497984 MD5sum: d3a3a8fc708b9eba14dca1abf21d1ad7 SHA1: 12ceb542dda614c4c0e9245835d42a44d3c45d81 SHA256: 1391709616556ed4282417861cbfc292782eea7a354841d79801179196ebc1ff SHA512: f016bba3209ceab00741f8f9d46ab38ceb92dddf40ed79df734d9cfee0c5761de93a439e7fa10aa71ea63481f01a5e05056dc5d5ae167ec4be0c2c08742c14b3 Homepage: https://cran.r-project.org/package=Rssa Description: CRAN Package 'Rssa' (A Collection of Methods for Singular Spectrum Analysis) Methods and tools for Singular Spectrum Analysis including decomposition, forecasting and gap-filling for univariate and multivariate time series. General description of the methods with many examples can be found in the book Golyandina (2018, ). See 'citation("Rssa")' for details. Package: r-cran-rssl Architecture: arm64 Version: 0.9.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2216 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_arm64.deb Size: 1849696 MD5sum: b284a08a52ba9c08b127f70c98fa303b SHA1: 060d747c7397e8e697a5f2d123f59e05748d05b9 SHA256: f39c593079b43ada23795a133028ddf0f823427220504de9d339744ec2c31fe4 SHA512: dc85a2b23225de249885e11573e8082b9469fa7758cd9b928ef9a2badab6416da128e50d35bc02ca212f0854e8f9c2f0bb3d1f6e4218eed1b4061df4a280c84b Homepage: https://cran.r-project.org/package=RSSL Description: CRAN Package 'RSSL' (Implementations of Semi-Supervised Learning Approaches forClassification) A collection of implementations of semi-supervised classifiers and methods to evaluate their performance. The package includes implementations of, among others, Implicitly Constrained Learning, Moment Constrained Learning, the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic Likelihood estimation, S4VM and WellSVM. Package: r-cran-rstan Architecture: arm64 Version: 2.32.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5823 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_arm64.deb Size: 1987734 MD5sum: 2a7822c1e319108712d8f954f134d4fd SHA1: c9098537489796748e8bd1b80baad920fb70fd8c SHA256: 30bbf90b0ee0216da5b8151e2b53e1da8c9ce817ac14fe504035bac4be55fa40 SHA512: 1c49111480a6b967f1b9dabf008c862d618dcfc27ab01bf57b899be881332408dc719fa4aaa8ce6784494512310ccb0da1cc3aca35f3b128f8c1a25a9b7d7c53 Homepage: https://cran.r-project.org/package=rstan Description: CRAN Package 'rstan' (R Interface to Stan) User-facing R functions are provided to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the 'StanHeaders' package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via 'variational' approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives. Package: r-cran-rstanarm Architecture: arm64 Version: 2.32.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 20095 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_arm64.deb Size: 7855086 MD5sum: 998a33cc9fc8877034c5207929b62512 SHA1: 056fd1557c52d746bd4914d040b0d5db319b0b68 SHA256: 8f9c233065260d10cb5079c72ac89f4d924867893f60c08f1d65a9670c47b1b4 SHA512: 54ab4afe0f2e940509aa621cf89b53e313b7e316dac45e168463d2307eb630e66239ddbbb0ca14d4d9905e8c798b0372ffcb17d2f993cd5d4719ac4a82efc806 Homepage: https://cran.r-project.org/package=rstanarm Description: CRAN Package 'rstanarm' (Bayesian Applied Regression Modeling via Stan) Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors. Package: r-cran-rstanbdp Architecture: arm64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4122 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_arm64.deb Size: 875882 MD5sum: 9c1043ce1830843b931e7d3da2d67a4d SHA1: fe0c270146f8833c05e56661e08d307f342b0834 SHA256: e77074e8d395143ae78a8e7b809e9f63385bc941948859ea0050707d3b618aa7 SHA512: 4b7ca78e77f43745f53bf89be6578575c0821338b69752c8932afeb543d5b4f3093cd0608110b65927ac063edbadd7e047587c6060d2f37d1bd7888af8d2ca47 Homepage: https://cran.r-project.org/package=rstanbdp Description: CRAN Package 'rstanbdp' (Bayesian Deming Regression for Method Comparison) Regression methods to quantify the relation between two measurement methods are provided by this package. The focus is on a Bayesian Deming regressions family. With a Bayesian method the Deming regression can be run in a traditional fashion or can be run in a robust way just decreasing the degree of freedom d.f. of the sampling distribution. With d.f. = 1 an extremely robust Cauchy distribution can be sampled. Moreover, models for dealing with heteroscedastic data are also provided. For reference see G. Pioda (2024) . Package: r-cran-rstanemax Architecture: arm64 Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2665 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_arm64.deb Size: 911464 MD5sum: 6a8df0deff8da7d09ce1b551962e6338 SHA1: 51782d98660714b3b270e3b63517acaf668bb715 SHA256: 195ac28de2b8e5645928c03626cddbbc0dad65f51f14b14282632e4222ec3e28 SHA512: 611e581119ba586f1ec0056871515114c779b830ba6ae3c7f3ce163d2596e6ff41ba392b688fab2f4fd53423030e18d85b9602d6b3f9213cc01ea3a393c04372 Homepage: https://cran.r-project.org/package=rstanemax Description: CRAN Package 'rstanemax' (Emax Model Analysis with 'Stan') Perform sigmoidal Emax model fit using 'Stan' in a formula notation, without writing 'Stan' model code. Package: r-cran-rstiefel Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 622 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_arm64.deb Size: 498948 MD5sum: 908a5b226a6595f6e0a933473885a8b2 SHA1: 3527bc4e6784a8cd5ef3ece09b73f627af80e1d8 SHA256: bb5c3b6dff7728dc4fa4777f92e7192b8706cb55f2d97a25989f668ca066cbca SHA512: 1742981aa25aac6062108a8345f94614dbb81c2724dae497869b346ee386eacbf42370f6e1fca0972b761c962c2133424f7b577e778b4b9d086f38d508105b83 Homepage: https://cran.r-project.org/package=rstiefel Description: CRAN Package 'rstiefel' (Random Orthonormal Matrix Generation and Optimization on theStiefel Manifold) Simulation of random orthonormal matrices from linear and quadratic exponential family distributions on the Stiefel manifold. The most general type of distribution covered is the matrix-variate Bingham-von Mises-Fisher distribution. Most of the simulation methods are presented in Hoff(2009) "Simulation of the Matrix Bingham-von Mises-Fisher Distribution, With Applications to Multivariate and Relational Data" . The package also includes functions for optimization on the Stiefel manifold based on algorithms described in Wen and Yin (2013) "A feasible method for optimization with orthogonality constraints" . Package: r-cran-rstoolbox Architecture: arm64 Version: 1.0.2.2-1.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_arm64.deb Size: 2057636 MD5sum: d0864ddbfb2f282e3588423d5544dc8e SHA1: f04aa4725ead58ab8f4863cc235c8760e3422bd3 SHA256: bc624d7747c7e8e2d0da9eeab9519e86a880b79720dab5d61ec02d6b65d511d8 SHA512: c338ce6434d93f233defeb00257936140a981b4a9c9bbffc531a6cfdd85841802328ce95ebcd0189e550082c1c7dd737d91e3a97dc86e42b392734efddeef65a Homepage: https://cran.r-project.org/package=RStoolbox Description: CRAN Package 'RStoolbox' (Remote Sensing Data Analysis) Toolbox for remote sensing image processing and analysis such as calculating spectral indexes, principal component transformation, unsupervised and supervised classification or fractional cover analyses. Package: r-cran-rstpm2 Architecture: arm64 Version: 1.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3708 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_arm64.deb Size: 2157580 MD5sum: 846a50f2f35700864efb7ac21201cd94 SHA1: f01671998fc58bf62ecf8ec2a62fcd6fa208db04 SHA256: 4dacc04f473c3a42775a0267df307308db7e2bee35d04abc9b29984b32c529b4 SHA512: 21215f8e62ae028becbad49e2146e903595d383c329390a50bee8c8e3111c06f1261f368a19066f874fa188a4c0b6efb8a668f3e051a9637f54695bc372093cb Homepage: https://cran.r-project.org/package=rstpm2 Description: CRAN Package 'rstpm2' (Smooth Survival Models, Including Generalized Survival Models) R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth . For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects , and copulas. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation. Package: r-cran-rstr Architecture: arm64 Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2603 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1845982 MD5sum: 2be0080089b9723bae40823bd925eb5e SHA1: 30bb97a6d4d32cb4692c65d2dbddc7b311eb3f46 SHA256: 95476064efb03ac279bed684bfe1f9934423150301a5575e04de79beed8d2c28 SHA512: 2f6f28b263fcd29e82b8aa9a06f8f29fba5b5921d0130a8abcaffdbed2e1a5b5f6665da86312c9bcaf1fa015681e25006cfb3f0a90b9fd07235a999a4d735091 Homepage: https://cran.r-project.org/package=RSTr Description: CRAN Package 'RSTr' (Gibbs Samplers for Discrete Bayesian Spatiotemporal Models) Takes Poisson or Binomial discrete spatial data and runs a Gibbs sampler for a variety of Spatiotemporal Conditional Autoregressive (CAR) models. Includes measures to prevent estimate over-smoothing through a restriction of model informativeness for select models. Also provides tools to load output and get median estimates. Implements methods from Besag, York, and Mollié (1991) "Bayesian image restoration, with two applications in spatial statistics" , Gelfand and Vounatsou (2003) "Proper multivariate conditional autoregressive models for spatial data analysis" , Quick et al. (2017) "Multivariate spatiotemporal modeling of age-specific stroke mortality" , and Quick et al. (2021) "Evaluating the informativeness of the Besag-York-Mollié CAR model" . Package: r-cran-rstream Architecture: arm64 Version: 1.3.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 518 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rstream_1.3.7-1.ca2604.1_arm64.deb Size: 361856 MD5sum: f5eb35ac422f26396177042745f4790f SHA1: 5e19651f6e646ac3a464a2a964d9b8a6b137d085 SHA256: 407ad8588c0527be5e912f12dab893b62e915a12d90f047f70ac540b6f674ac7 SHA512: 58e591a4a01481a9c8cb3f0371876164cbf0b2575a6cba17a0d9550d5038dc1187c41a15a4afee51c2cd1ff2262242b5ceb5962ad431df2688d573a25024064e Homepage: https://cran.r-project.org/package=rstream Description: CRAN Package 'rstream' (Streams of Random Numbers) Unified object oriented interface for multiple independent streams of random numbers from different sources. Package: r-cran-rsubbotools Architecture: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 738 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_arm64.deb Size: 288046 MD5sum: 1aeb93a8ca285de073c7b678a6f0bab0 SHA1: 8a16c3af559072c1cac59f5e75eacfb572aa5a86 SHA256: 6d02418565293f806ce17f09032bfa39bf09ba2206c84be3afa5b8dccbad5268 SHA512: 101e7022cddeae7579892c2c2872506bc6dc043fd37ab7c8d04907d50e0db011a865341ea534377f67ffa2fa7f9512130a99d814f19e6aef51ab1b8a6be6b55b Homepage: https://cran.r-project.org/package=Rsubbotools Description: CRAN Package 'Rsubbotools' (Fast Estimation of Subbottin and AEP Distributions (GeneralizedError Distribution)) Create densities, probabilities, random numbers, quantiles, and maximum likelihood estimation for several distributions, mainly the symmetric and asymmetric power exponential (AEP), a.k.a. the Subbottin family of distributions, also known as the generalized error distribution. Estimation is made using the design of Bottazzi (2004) , where the likelihood is maximized by several optimization procedures using the 'GNU Scientific Library (GSL)', translated to 'C++' code, which makes it both fast and accurate. The package also provides methods for the gamma, Laplace, and Asymmetric Laplace distributions. Package: r-cran-rsvddpd Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), 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_arm64.deb Size: 105668 MD5sum: e99f99351fbc69e7e7a04474556b62d9 SHA1: c4d92c1ab767774cc44ec607eae457c9b2ef7e4a SHA256: 2b2c7e11fee04d79e876558f9187dc9c7c1f5427c70a805a24c7312aca3e88b7 SHA512: a721d9b6859224a9079aa753d39960493f51d1b88973bc97fd3c57f3c4e718f75e02ec74dcbcb9dce48ae04963ba0da12385cd8cc0af0fa0c66614296ed098f9 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. 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Package: r-cran-rtcc Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 112428 MD5sum: e6a2f0ec321207ef55a287be5c2a569a SHA1: a3dd1fc5622a0da451bbd21d8ca1b50bb99a8133 SHA256: 9b124e515559d27bb5d9599d22d8d4e8a4da28a860ef18f64a6b724be4771b8d SHA512: 81d6115d85dfb16f5411eae7d229f930e8153fa7fe2c5bb68cabaa8b2eb0da39dcd166ff881458d43adf35c9af9936f89e50c12481044d70690255fa0211d817 Homepage: https://cran.r-project.org/package=RTCC Description: CRAN Package 'RTCC' (Detecting Trait Clustering in Environmental Gradients) The Randomized Trait Community Clustering method (Triado-Margarit et al., 2019, ) is a statistical approach which allows to determine whether if an observed trait clustering pattern is related to an increasing environmental constrain. 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Package: r-cran-rtdists Architecture: arm64 Version: 0.11-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1232 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_arm64.deb Size: 720978 MD5sum: c74452a7af4145a56574f6b908e80137 SHA1: ad086c8ded9d0f6c04a4c4d4bab6ca72b06f019f SHA256: 91dbdf315a4d86726dd22710c0fc52f0a3e7d2558a16a8bb8581c614e4207766 SHA512: 18186aff2490171d77f398bef57d78a18f5105dfad042adc1216a3991e10859897fa64a9819d8a8c2196f908a803a2ddb28600acebb65dd8189902ffd9d084f9 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. 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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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Package: r-cran-rtmb Architecture: arm64 Version: 1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10316 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_arm64.deb Size: 3337456 MD5sum: 2208aa0c4b135463beeb8aad1874adf3 SHA1: edfe1b96aa3cb5e42128d8d2d8b0d249da421642 SHA256: 54ff36b25a58c36496a85653d7392bdcaa0a84afc2a6d71a6a579ce533922f42 SHA512: ffad0e31f5154196a3a5b9bc354bc445358f0a682a5de5625c4bc4f4f2da76888372384510cfda735cf546e4488595c8e0102f372b18262a4d79ad49ef0bab64 Homepage: https://cran.r-project.org/package=RTMB Description: CRAN Package 'RTMB' ('R' Bindings for 'TMB') Native 'R' interface to 'TMB' (Template Model Builder) so models can be written entirely in 'R' rather than 'C++'. Automatic differentiation, to any order, is available for a rich subset of 'R' features, including linear algebra for dense and sparse matrices, complex arithmetic, Fast Fourier Transform, probability distributions and special functions. 'RTMB' provides easy access to model fitting and validation following the principles of Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H., & Bell, B. M. (2016) and Thygesen, U.H., Albertsen, C.M., Berg, C.W. et al. (2017) . Package: r-cran-rtmpt Architecture: arm64 Version: 2.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1407 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_arm64.deb Size: 769734 MD5sum: 41d7717cc65dadc5f5252ece75b867b3 SHA1: cd80ab9ba7a097442bafb3d8c61955170b0cb71d SHA256: 04cb51f31d9aa8b69442dda6a57b574274f992c15f5c22763d5836683b5cc51a SHA512: c41483b723da252b145e29a542d07e93a00b47f700f2fadf29254d77d8b7c3e908dfdbb02b957ae33ada74ed1e87ed1bff19cc570f601ca8329a9ac7331e0c8d Homepage: https://cran.r-project.org/package=rtmpt Description: CRAN Package 'rtmpt' (Fitting (Exponential/Diffusion) RT-MPT Models) Fit (exponential or diffusion) response-time extended multinomial processing tree (RT-MPT) models by Klauer and Kellen (2018) and Klauer, Hartmann, and Meyer-Grant (submitted). The RT-MPT class not only incorporate frequencies like traditional multinomial processing tree (MPT) models, but also latencies. This enables it to estimate process completion times and encoding plus motor execution times next to the process probabilities of traditional MPTs. 'rtmpt' is a hierarchical Bayesian framework and posterior samples are sampled using a Metropolis-within-Gibbs sampler (for exponential RT-MPTs) or Hamiltonian-within-Gibbs sampler (for diffusion RT-MPTs). Package: r-cran-rtop Architecture: arm64 Version: 0.6-17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2035 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_arm64.deb Size: 930350 MD5sum: d831df981415c01e2f8a7a2e0f9bd9c9 SHA1: 9f0da6ee8d473e09a3d302aa0822466e9a5f365d SHA256: c67f4341c14e490367693d5e7c100c70e3db3e85cd0955697a6444b293fe89a4 SHA512: e66d0f94558e6a6b0e2f0fe302ae8f584938869b9032f35f3da5c33eec63a00231e51c99f7fea87f834b93a8bfbf440e0bd44b9d00fadfc18308251175997ee9 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: arm64 Version: 0.2.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 905 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 615294 MD5sum: abd1044eeab2e87261bf67a5f56253f2 SHA1: 1f50b559f87b613e2e51f66f9fcc3ad466469965 SHA256: b5b9aa07c4ae186738a43dff9478f4d41c39dbc11f54922bc06191116cb0d360 SHA512: ac41eeaacbca7131b5e87209853ecfa2be6910db0462115a7fbe9321d13d244721d7034451b435964f4d500700843aaf48d7a2202b578f6b6ddf01e16787c7a7 Homepage: https://cran.r-project.org/package=RTransferEntropy Description: CRAN Package 'RTransferEntropy' (Measuring Information Flow Between Time Series with Shannon andRenyi Transfer Entropy) Measuring information flow between time series with Shannon and Rényi transfer entropy. See also Dimpfl and Peter (2013) and Dimpfl and Peter (2014) for theory and applications to financial time series. Additional references can be found in the theory part of the vignette. Package: r-cran-rtrend Architecture: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 957 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 744870 MD5sum: af21fe360e3c7c203d2db4cb967ad8f7 SHA1: 498fb015d0002172d009063e580b5ffdc956ab61 SHA256: 7e338db6471d89e781b1cddf8f1a8f769cc2a142733b1ae525fb5f5f6f7505fb SHA512: 4fa263b578c10b9445ae23be78085f105d6129280307a936dba2374e8077c9a05d2e623d3a7794a664b9c75f0991f67f4b277205236040687347900792744f9c Homepage: https://cran.r-project.org/package=rtrend Description: CRAN Package 'rtrend' (Trend Estimating Tools) The traditional linear regression trend, Modified Mann-Kendall (MK) non-parameter trend and bootstrap trend are included in this package. Linear regression trend is rewritten by '.lm.fit'. MK trend is rewritten by 'Rcpp'. Finally, those functions are about 10 times faster than previous version in R. Reference: Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated data. Journal of hydrology, 204(1-4), 182-196. . Package: r-cran-rtriangle Architecture: arm64 Version: 1.6-0.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 274 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 165964 MD5sum: 72fd9016a28490254af3f9c84e433892 SHA1: 4d6cf3c70a3f97d95bb007eb5ed429d975b558e8 SHA256: e8ca60c95a7958cb0736eda3fd1c702311f9d503ecee75ea9d24e59726111b59 SHA512: f961e0f3993105e8ad7f391284642160468bda01da0210c68e159ddc854f61eb9f7daf6bfcf7af10951d3f55ac9ed3594b190067ff36f6af2f6632943e80e4db 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. 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Package: r-cran-rtrng Architecture: arm64 Version: 4.23.1-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10951 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_arm64.deb Size: 883378 MD5sum: aaf954d65c1697c9699c0092c7ffb737 SHA1: d25f74735183f35c3f3f5a0c31e1e4d023ea97d7 SHA256: 62ae90b56212e2bbc129e878388f977ee7e0c4a5f7011361ddbffcfa5965e0cb SHA512: 297f6fdc18f0a1dcdd97ffb62268d41a53851e41e1c26c8227c962770643d64549c02d1557ca7141c9db4f0b002f5428c2d1b624f6238ad05742e7e84c773d32 Homepage: https://cran.r-project.org/package=rTRNG Description: CRAN Package 'rTRNG' (Advanced and Parallel Random Number Generation via 'TRNG') Embeds sources and headers from Tina's Random Number Generator ('TRNG') C++ library. 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Package: r-cran-rts2 Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5526 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 3019958 MD5sum: 30faa84cbf1ac9f449bd6a6f818a3851 SHA1: 5f5b8329319cc650f382d324cdb970aa9710d38b SHA256: 05f9823b77edd3bdc954adf3c020168e6a0b0edb21791a25008411dc6ff69b1e SHA512: db6c19f570cdb1153cd059663878f098f2f0e068d7d637e9c6016c3a093f1b1ad52e6b5280847fa9b8cba2af7e8591ce6f14d0fe025d62c87af6c9ada2ae653a Homepage: https://cran.r-project.org/package=rts2 Description: CRAN Package 'rts2' (Real-Time Disease Surveillance) Supports modelling real-time case data to facilitate the real-time surveillance of infectious diseases and other point phenomena. 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Package: r-cran-rtsne Architecture: arm64 Version: 0.17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 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_arm64.deb Size: 101058 MD5sum: 5fdb0fbef59c8824db7cae0052b21282 SHA1: 59d6fc0aa0392acd854e634bf46c1a3fd7516c2c SHA256: 0239b01b60ecdd0511d73f9c8d7ee0bc01b48e2e0ec91363e75e9b658865fa6a SHA512: b4aea80394b706f923dfba88b35ffe68c48aab6cd206fc68b5ca2e1ef4ce01f5a01c3182c6b474bc66433b43c0977f7783ddec4d4bf71e16fae5d7d9ea65a57d 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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(2012) , which enables ultrafast subsequence search for a best match under Dynamic Time Warping and Euclidean Distance. 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Package: r-cran-rupturesrcpp Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1798 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-r6, r-cran-ggplot2, r-cran-patchwork, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-reticulate, r-cran-binsegrcpp Filename: pool/dists/resolute/main/r-cran-rupturesrcpp_1.0.2-1.ca2604.1_arm64.deb Size: 571272 MD5sum: e130c86eed3138eb6d366e09105bccd9 SHA1: f884d6fc845268aa68bc18532166e7744bbea271 SHA256: 8503d400dfd528f80139ad18d98154fb10c2bf34158c8b2a6b6afdd0fcbee6b4 SHA512: 020d9c7693b874f7a83bb0e31da4544e35a3d21b22f953d60b15267d440eb19eef8614b8de470e8582e6db475ceff52a1aa3b60faa65383289c1f6fb72f574cd Homepage: https://cran.r-project.org/package=rupturesRcpp Description: CRAN Package 'rupturesRcpp' (Object-Oriented Interface for Offline Change-Point Detection) A collection of efficient implementations of popular offline change-point detection algorithms, featuring a consistent, object-oriented interface for practical use. Package: r-cran-rush Architecture: arm64 Version: 1.1.0-1.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_arm64.deb Size: 259738 MD5sum: 7cd067151f79bba17e6d906cf22f5edf SHA1: cff8700ea1525ef4fa439dd25ac87a4a403b2940 SHA256: 5c536eae432549f37abe944b2fb53c62fec530d9938b144bbafbbb7ca4490d43 SHA512: 3b8197acb6f54efbd2bed7a6c0d1b6cb6b3cc25aab19ba3777ecea4b29c1137dde007967473435d340442502485942132e3fff25cfea724c2940046481343e76 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: arm64 Version: 1.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1078 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 454950 MD5sum: 1665f385473e06ba1bdd074d2ce5ecc9 SHA1: 22a04c395b682556816d6cb3a6f86de5bcfd8bff SHA256: d2a997faf10c27823f38256c9016ba11fbabfd0c9fc70b55173934d009be1743 SHA512: bc264346a644c3e5debe459b36c5391d38f980cfb8c03cd2a51d1b85231a5328559f41be63dddf02b7166daebcf963c889e6552d10718733c9935e239005e93e Homepage: https://cran.r-project.org/package=rust Description: CRAN Package 'rust' (Ratio-of-Uniforms Simulation with Transformation) Uses the generalized ratio-of-uniforms (RU) method to simulate from univariate and (low-dimensional) multivariate continuous distributions. The user specifies the log-density, up to an additive constant. The RU algorithm is applied after relocation of mode of the density to zero, and the user can choose a tuning parameter r. For details see Wakefield, Gelfand and Smith (1991) , Efficient generation of random variates via the ratio-of-uniforms method, Statistics and Computing (1991) 1, 129-133. A Box-Cox variable transformation can be used to make the input density suitable for the RU method and to improve efficiency. In the multivariate case rotation of axes can also be used to improve efficiency. From version 1.2.0 the 'Rcpp' package can be used to improve efficiency. Package: r-cran-ruv Architecture: arm64 Version: 0.9.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 283376 MD5sum: 514818579a520d0b605257b6cc44474f SHA1: fae360a74797cc96b6a072d24c4f974351e61382 SHA256: 22485132ee0acf6d8dc100f08a968db9e0b5a4527067d25b0c659c955e819409 SHA512: 3245045e717336aa992602f6eb76fdbddeae373a68c96d670e4d870161c1aa990443aeaec2cbe62c7adcf72cdac7bbbc0eedf8a45056014c2e6cfeba1ef30674 Homepage: https://cran.r-project.org/package=ruv Description: CRAN Package 'ruv' (Detect and Remove Unwanted Variation using Negative Controls) Implements the 'RUV' (Remove Unwanted Variation) algorithms. These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data. The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well. These algorithms were proposed in Gagnon-Bartsch and Speed (2012) , Gagnon-Bartsch, Jacob and Speed (2013), and Molania, et. al. (2019) . The algorithms require the user to specify a set of negative control variables, as described in the references. The algorithms included in this package are 'RUV-2', 'RUV-4', 'RUV-inv', 'RUV-rinv', 'RUV-I', and RUV-III', along with various supporting algorithms. Package: r-cran-rvalues Architecture: arm64 Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2826 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rvalues_0.7.1-1.ca2604.1_arm64.deb Size: 2776724 MD5sum: 4db2870dc7eb7a91b1263e6ac95f46fb SHA1: 4a37afe4a516c02c6cebce7055887a803da1b60a SHA256: e30d9b6ecd9c8b6016c1dc937a9f2f1730a6155e063ee79291fb369d7f8d057a SHA512: 0073b32ef3c16ed0b789b8a7dca4c9b0f2d5d2b6aa1d4b061aac864824389c463c7e24b47a93057b35f50770c82feacfaa488f9817e0973ad7bea566bcaa6ff3 Homepage: https://cran.r-project.org/package=rvalues Description: CRAN Package 'rvalues' (R-Values for Ranking in High-Dimensional Settings) A collection of functions for computing "r-values" from various kinds of user input such as MCMC output or a list of effect size estimates and associated standard errors. 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Package: r-cran-rvcg Architecture: arm64 Version: 0.25-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3325 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_arm64.deb Size: 1870468 MD5sum: c4904ebaa563562bfb89f9e50ccb790a SHA1: bf413bc0f5891f36b56e7c36afe5817d8e82c488 SHA256: d6049f300f78e6e2a188be293e09035ff3e97ca4a6b705e31fe4e9f1f690b7a8 SHA512: ed727958fbe9512b3e58fb7d730292f5da9844322456d13361a8e7650c44e69ceb5ba29639aabb51a72937772b8abf89175296985185c4af83795523ceec4550 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'. 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Package: r-cran-rvcompare Architecture: arm64 Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 125764 MD5sum: 5a27660b4d1dd56404914d0ad892e908 SHA1: 02fc0178340e7de03553a2aee0fbb823ab5f9a3a SHA256: 52dd78bc4fd2a387a70beb046316ea3250c7555f8222da781f726fd2ddd45c13 SHA512: 40a7076d433d9fe62d5a042a12321a742a1f30aec6f4c8d3ce75bf09bd9f530fe19a89bf024012132ef4604621b60701aa2f69149df3f85b4cfc3cf7142e5886 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. 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Package: r-cran-rvinecopulib Architecture: arm64 Version: 0.7.3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 10042 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_arm64.deb Size: 2121282 MD5sum: e257335fc389050f5e3d2297adb2a1d3 SHA1: 3c0c968de9f990241fb669a8c14b641450e86f9a SHA256: d272d9562eb99e04568349697ab6ff5196cc9f3fc0479d6bd26f08607e7f203e SHA512: 7a00ef017cc94111cf28ff6d825ec6657ab26575f310c23862e189565af2ea4105c0db40ab3847d17e1f57c27885097462e1ba5bfadef9bf438e8b31f5b0f200 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. 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Package: r-cran-rvmf Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 189 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_arm64.deb Size: 51146 MD5sum: 40984a36f3c7dae3a662bf12ae20357c SHA1: a2dc346a4405eb7075e67a6a3f2d64443be868fd SHA256: bd2416ac79bea3a73089b039ecbccc2a5d5be85ea95e76ba368d1a2aa8b32501 SHA512: 5ca59ae3cd5d0529a79b6562e84ad0b791b4f5863fb97526b221bb39eced0ea5241a4900d1fc19104a15800c7401513768b3a989128c31886f97cea715647c82 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. 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Package: r-cran-rwave Architecture: arm64 Version: 2.6-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1197 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-rwave_2.6-5-1.ca2604.1_arm64.deb Size: 1008304 MD5sum: abd20f27f0cff82ee55a8429d5d0d008 SHA1: 2820ef2cd96fba4019a8448fa6223564cd5ce22e SHA256: c4a49851dcd73a55177426885cdfe9cafcd12f00dc8ab1705e04f2c12b11c311 SHA512: 12ec8222bdd3db3796c3e0747cf10980465694cb9582470d8842b8023c7059cba9aad1347acd18c3dedb25245eb24a776ccc2aeb925df05b91be528fbc8624af Homepage: https://cran.r-project.org/package=Rwave Description: CRAN Package 'Rwave' (Time-Frequency Analysis of 1-D Signals) A set of R functions which provide an environment for the Time-Frequency analysis of 1-D signals (and especially for the wavelet and Gabor transforms of noisy signals). It was originally written for Splus by Rene Carmona, Bruno Torresani, and Wen L. Hwang, first at the University of California at Irvine and then at Princeton University. Credit should also be given to Andrea Wang whose functions on the dyadic wavelet transform are included. Rwave is based on the book: "Practical Time-Frequency Analysis: Gabor and Wavelet Transforms with an Implementation in S", by Rene Carmona, Wen L. Hwang and Bruno Torresani (1998, eBook ISBN:978008053942), Academic Press. Package: r-cran-rwdataplyr Architecture: arm64 Version: 0.6.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1393 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_arm64.deb Size: 357782 MD5sum: f0a2be285438717beecbfd86a39389fa SHA1: 2380ec4de37821b1e257d2d19df83493a4f7711a SHA256: 6ff19b5b6a25f26c936f0545476673c77869b1bfa77dc91583053009ad3ac495 SHA512: 588fed6b1191a4efe446c04501087a9849388725f49fa95e77c200af655fceefe23baf40cf048c56f362f4badd052a5bc0020a745a67315b609a9beee0f44d3d 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. 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Package: r-cran-rwfec Architecture: arm64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 36428 MD5sum: b838c5e629f85229b587102ce292309f SHA1: 08e95f99b78ea94cd620479784da95e073419c98 SHA256: bdc40496b41328ac49f0ed1c2fc0d663eca4856973008de781ce84eb53f31c07 SHA512: 5ae38626f568ba80fccdba07b787867232e9e469d2e4fadc7c70a7e1dd2e7bd61b252f83d53db94b33c771f3bab01b783ac6f22675bf8dbf34647e9a29c86e0b 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: arm64 Version: 1.3-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 199 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_arm64.deb Size: 106866 MD5sum: 41f6fa846b64f553fd9651591961313b SHA1: 3d39d0140f219c7519db080b67317a3a87f245fa SHA256: 829adbb2a08dfa53ef9ed634cf275f77ae2cf56515eb3328e2f18728ce7527fd SHA512: 80b858e3816d64d61e9076ac98c6855f5889494421f563c274ba37e1de58536298113fc77ecea3a645d0d1271c961cff4a908375797b33776a2d12e71f5d70b4 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. 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Package: r-cran-rwnn Architecture: arm64 Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 519 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 340446 MD5sum: 97d6307b48a9c8f85197a8179b608c25 SHA1: a1e72497b1f0499ef66c3b76b83f11dcacf0bfee SHA256: 6cd03780b760a2eb4f72433b757365bdfd6fcfc8a17fb1e33d58d2bd652dbda6 SHA512: e01b0b0455d5d7edcfa63a3d63f4a46e07b6ef96e8208a50273806d34bdf647ed85b006921f6f087f991f8869876382f49fd2d72f49146c8484a376195fdfdc6 Homepage: https://cran.r-project.org/package=RWNN Description: CRAN Package 'RWNN' (Random Weight Neural Networks) Creation, estimation, and prediction of random weight neural networks (RWNN), Schmidt et al. (1992) , including popular variants like extreme learning machines, Huang et al. (2006) , sparse RWNN, Zhang et al. (2019) , and deep RWNN, Henríquez et al. (2018) . It further allows for the creation of ensemble RWNNs like bagging RWNN, Sui et al. (2021) , boosting RWNN, stacking RWNN, and ensemble deep RWNN, Shi et al. (2021) . Package: r-cran-rwofost Architecture: arm64 Version: 0.8-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1707 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_arm64.deb Size: 376190 MD5sum: 5152e758d68f391c7632a1d206fa7985 SHA1: fd8f5ab57106fdaad061ae9050a67e4461ca57e2 SHA256: 9c68171fef4f3dd2aa8b9d5021723210d270a8e435c49b494be46674e5374039 SHA512: 964668a94a650bee5b7d10d96394a71b1fd1a9149f466510468ef4b4adb975ef1be2bb0b6e3530fea3e26c9c374106839533126d12a99ee40d9e82bdd3e244e2 Homepage: https://cran.r-project.org/package=Rwofost Description: CRAN Package 'Rwofost' (WOFOST Crop Growth Simulation Model) An implementation of the WOFOST ("World Food Studies") crop growth model. WOFOST is a dynamic simulation model that uses daily weather data, and crop, soil and management parameters to simulate crop growth and development. See De Wit et al. (2019) for a recent review of the history and use of the model. Package: r-cran-rxode2 Architecture: arm64 Version: 5.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8447 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 3847656 MD5sum: 4e64f54aa55cfda66ddc52b763e573ab SHA1: da8e989113d08a9ea871911482f6c449f2400680 SHA256: f4366f3f8fb9a52e416c58930a8932e51d2b80d68684bd809c03b4377dcd08f1 SHA512: bbdcf15b410c6c11ffa974a9ea0f3fdb803590c395cfac1b4c220fe4749de26ac6a3d1b0bd7af78c084fe500689477d086123cf84091648fbac998afc0f00e20 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: arm64 Version: 2.0.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 546 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_arm64.deb Size: 157204 MD5sum: 229f17a2c524e8194fc05cb8d1dec78e SHA1: 7a70aba9e63bab559f9e9176c31df53cf70f7c46 SHA256: 09a5aaad57d14cfa1dd1110b90ae4aa9a8329a633e46907f600534673dfac066 SHA512: c6c230252b3b208a6edd870970d7088f5f9f1297ecad9a117251d49449fe0f7c40c67073cac765603a0c5784da1bb8cf2f52054aaff9a17b217cafc1489cb46c 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: arm64 Version: 0.2.14-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 914 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_arm64.deb Size: 282828 MD5sum: 499b345e44c621ae75c1c94716068576 SHA1: 3788555ccd43747187c08813d42f6554fb8eb887 SHA256: f3cd7cf648eb1967df57845389e145acf40ea24e447de3722014400c0d4e3cd9 SHA512: 4a205de2bff5ef445db7fe8cfcd663446fcf849365757fe4127316b60846b78764561767cb5798647da4a77b0a24e2ad312543c029af8a77c232f5662ccdd29b Homepage: https://cran.r-project.org/package=rxylib Description: CRAN Package 'rxylib' (Import XY-Data into R) Provides access to the 'xylib' C library for to import xy data from powder diffraction, spectroscopy and other experimental methods. 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Package: r-cran-rzigzag Architecture: arm64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 332 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_arm64.deb Size: 133570 MD5sum: 0b71af354a828369541134ac6f0c054d SHA1: 98b9224a11bca838f4cee44572cc21dcf745224b SHA256: 24a383e2a415651fda955c02d39a6813a6fa6b068a8b576c2a1a82785850ba99 SHA512: 43f6cd3421e161a833b5be1fdd70cfc5017459b5329f58ad556b24652420288a58679ba9b8e63220e57b6ff2afdb403fdbb0c642ba4ea46240591db1b5785f73 Homepage: https://cran.r-project.org/package=RZigZag Description: CRAN Package 'RZigZag' (Zig-Zag Sampler) Implements the Zig-Zag algorithm (Bierkens, Fearnhead, Roberts, 2016) applied and Bouncy Particle Sampler for a Gaussian target and Student distribution. 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Package: r-cran-rzooroh Architecture: arm64 Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4542 Depends: libc6 (>= 2.38), 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_arm64.deb Size: 2210900 MD5sum: 5f6edbf33a3f15ce4947664c43d9be62 SHA1: bef4580be0e990483c14c13c5dd863a1d7e9093b SHA256: f71fd74b9c442c71ac1b2760881f0918b2213d54e9750559f3d72c91e0c79bc6 SHA512: 38cff88b79e0d3fcbfe6eed624574f6b2dac659e86fe4f61901abe3d40ab49638ca7dd7781cd87dd5f9cb238e9b8629eb1fc5cbc1ca7b0d332866daee8401daf Homepage: https://cran.r-project.org/package=RZooRoH Description: CRAN Package 'RZooRoH' (Partitioning of Individual Autozygosity into MultipleHomozygous-by-Descent Classes) Functions to identify Homozygous-by-Descent (HBD) segments associated with runs of homozygosity (ROH) and to estimate individual autozygosity (or inbreeding coefficient). HBD segments and autozygosity are assigned to multiple HBD classes with a model-based approach relying on a mixture of exponential distributions. The rate of the exponential distribution is distinct for each HBD class and defines the expected length of the HBD segments. These HBD classes are therefore related to the age of the segments (longer segments and smaller rates for recent autozygosity / recent common ancestor). The functions allow to estimate the parameters of the model (rates of the exponential distributions, mixing proportions), to estimate global and local autozygosity probabilities and to identify HBD segments with the Viterbi decoding. The method is fully described in Druet and Gautier (2017) and Druet and Gautier (2022) . Package: r-cran-s2 Architecture: arm64 Version: 1.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3275 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_arm64.deb Size: 1751592 MD5sum: cf81b11154399ce3ce92f7c558d555f1 SHA1: 89ff3c34c56f106dd529b8acc243a4751362c3eb SHA256: 7e30521b399e7b4b469254e00ca6bc991f776a4d7c410bd33436150290904e6a SHA512: 4e71b0b3fd511b256f6ac1d882804f9ae913bfec8e9c6cf985fc8bf7a0c53002ababb46f038a8eb32271c9e6b5c5c765b08362be5d64d02161b626a17d28fa9f Homepage: https://cran.r-project.org/package=s2 Description: CRAN Package 's2' (Spherical Geometry Operators Using the S2 Geometry Library) Provides R bindings for Google's s2 library for geometric calculations on the sphere. High-performance constructors and exporters provide high compatibility with existing spatial packages, transformers construct new geometries from existing geometries, predicates provide a means to select geometries based on spatial relationships, and accessors extract information about geometries. Package: r-cran-s2net Architecture: arm64 Version: 1.0.7-1.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_arm64.deb Size: 309210 MD5sum: e9e911354406aca4074a81cbb04b1360 SHA1: 8971a76ef9d12ca989d8e5df005a8c95357e039f SHA256: 9405ff05790aeaec93f2c298740b851d0b8a1256fb855d2fd467afd2a4f51397 SHA512: 8a548f24c8aa3ce46d3158926845ae4c54efc11f4e719e8e7ec4e63c6778287f2f134540cddb106ed8a5935e1c2bda7bc1bdd230fbd91156b5c0b6d332d334be Homepage: https://cran.r-project.org/package=s2net Description: CRAN Package 's2net' (The Generalized Semi-Supervised Elastic-Net) Implements the generalized semi-supervised elastic-net. This method extends the supervised elastic-net problem, and thus it is a practical solution to the problem of feature selection in semi-supervised contexts. Its mathematical formulation is presented from a general perspective, covering a wide range of models. We focus on linear and logistic responses, but the implementation could be easily extended to other losses in generalized linear models. We develop a flexible and fast implementation, written in 'C++' using 'RcppArmadillo' and integrated into R via 'Rcpp' modules. See Culp, M. 2013 for references on the Joint Trained Elastic-Net. Package: r-cran-s7 Architecture: arm64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 654 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-bench, r-cran-callr, r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-tibble Filename: pool/dists/resolute/main/r-cran-s7_0.2.2-1.ca2604.1_arm64.deb Size: 299304 MD5sum: f18b17c0832844891043277aa7fa1772 SHA1: 33859bb3d678bd4c2981cd5f57d43cadaf6f7f25 SHA256: 7d315ae37c9fe658f6db6c55dd310113f69e7dc81a8b98c984654f41a847ddee SHA512: b09120a496897180e94aeb3dd094fe17a020499abc4e98aac60a4b7f4b98bf872441387bd8b457dda27859e70355325fc662afc112742326cfd385e51eac593e Homepage: https://cran.r-project.org/package=S7 Description: CRAN Package 'S7' (An Object Oriented System Meant to Become a Successor to S3 andS4) A new object oriented programming system designed to be a successor to S3 and S4. It includes formal class, generic, and method specification, and a limited form of multiple dispatch. It has been designed and implemented collaboratively by the R Consortium Object-Oriented Programming Working Group, which includes representatives from R-Core, 'Bioconductor', 'Posit'/'tidyverse', and the wider R community. Package: r-cran-saccadr Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 540 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 267230 MD5sum: 94e15556239c3205a9acfc6afab3d8ce SHA1: 3235299929493369cddb096818375cbc6ea6adf0 SHA256: 2405e2b5475dcec23bbdfbf1809cc49309cde9103354ad062b3707f3782e6e57 SHA512: a8f6b8c0b437296014e0d9a40423b1ae38477706d5bcc46da16fa8b5ef6a5bf7ef6db387b07bc00a7501ae27947431c48b893349d52ae50d4a843eb224fe627c Homepage: https://cran.r-project.org/package=saccadr Description: CRAN Package 'saccadr' (Extract Saccades via an Ensemble of Methods Approach) A modular and extendable approach to extract (micro)saccades from gaze samples via an ensemble of methods. Although there is an agreement about a general definition of a saccade, the more specific details are harder to agree upon. Therefore, there are numerous algorithms that extract saccades based on various heuristics, which differ in the assumptions about velocity, acceleration, etc. The package uses three methods (Engbert and Kliegl (2003) , Otero-Millan et al. (2014), and Nyström and Holmqvist (2010) ) to label individual samples and then applies a majority vote approach to identify saccades. The package includes three methods but can be extended via custom functions. It also uses a modular approach to compute velocity and acceleration from noisy samples. Finally, you can obtain methods votes per gaze sample instead of saccades. Package: r-cran-sacrebleu Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 209 Depends: libc6 (>= 2.17), 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-vctrs, r-cran-withr Filename: pool/dists/resolute/main/r-cran-sacrebleu_0.2.0-1.ca2604.1_arm64.deb Size: 60592 MD5sum: d6702f88d113ec8a01c377a9064cb245 SHA1: c0d6b74d8d0fdffc5c141ae0a8e1b1d330040731 SHA256: d6767f00910f56c4cea31b92530ac9eb1f5920bc3ab72a41bf9d8582832e24fe SHA512: fcbee78fc4110c22caf246653e2d7988505808af34c2c00c3b0780441ae3874dc0cb159caaa335ab5e8b930afde581a54da93d1fc89a36b73867271e527cf866 Homepage: https://cran.r-project.org/package=sacRebleu Description: CRAN Package 'sacRebleu' (Metrics for Assessing the Quality of Generated Text) Implementation of the BLEU-Score in 'C++' to evaluate the quality of generated text. The BLEU-Score, introduced by Papineni et al. (2002) , is a metric for evaluating the quality of generated text. It is based on the n-gram overlap between the generated text and reference texts. Additionally, the package provides some smoothing methods as described in Chen and Cherry (2014) . Package: r-cran-sads Architecture: arm64 Version: 0.6.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1195 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bbmle, r-cran-mass, r-cran-vgam, r-cran-poilog, r-cran-guilds, r-cran-powerlaw Suggests: r-cran-vegan Filename: pool/dists/resolute/main/r-cran-sads_0.6.5-1.ca2604.1_arm64.deb Size: 898422 MD5sum: c30c69e6ff2371abb762432a59608a9f SHA1: 59f6598b74700a86dc21ef1c920a153c677b53ca SHA256: dae2a6e509e777fc0a35a6995aa9aa8a44bc040a8997ac9c3f47303870b6856a SHA512: 12f99efff69b3c62295dd3b65ee43741db6ca5d063a5358d0061bf4bf0009caa47ca1ea56dc5ee4cb581ab18dd395d438fba773ebf12ca0cb448c33526fd60a9 Homepage: https://cran.r-project.org/package=sads Description: CRAN Package 'sads' (Maximum Likelihood Models for Species Abundance Distributions) Maximum likelihood tools to fit and compare models of species abundance distributions and of species rank-abundance distributions. Package: r-cran-saeczi Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 879 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_arm64.deb Size: 397830 MD5sum: b099dee78c2876b86ebe837649e81f03 SHA1: 81577bcba953e6110b2941e51af40db9c36ef651 SHA256: 1ff06df3ad24018c145f12f7f0bfa8017baf822ca2bdc80b0e67b1982439e6f5 SHA512: fd115ae578445035a94cf14a0a8b0ef10180c052e1d759a2af6c64e3308e598757e38ecfb9d6c83315618270422039f357531259468234316c650c14ac183127 Homepage: https://cran.r-project.org/package=saeczi Description: CRAN Package 'saeczi' (Small Area Estimation for Continuous Zero Inflated Data) Provides functionality to fit a zero-inflated estimator for small area estimation. This estimator is a combines a linear mixed effects regression model and a logistic mixed effects regression model via a two-stage modeling approach. The estimator's mean squared error is estimated via a parametric bootstrap method. Chandra and others (2012, ) introduce and describe this estimator and mean squared error estimator. White and others (2024+, ) describe the applicability of this estimator to estimation of forest attributes and further assess the estimator's properties. Package: r-cran-saehb.tf.beta Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2364 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_arm64.deb Size: 753796 MD5sum: 327680850eb3af611028ca1b1f8d7f6d SHA1: 506132621fb8cb75914ddb0d6d8ba683e0741a42 SHA256: c5dbb1c9b268e571f6d46feaa6ec3724e21b3ff9363d9271057ea0b3df3fbeb1 SHA512: 1b324a45faa255f47f74338db5269f96b54fbadb634358c84788a17e37d8f032fb8d91fdf10067d313acc4ee315132181889592a7edb5604624f5a046181ba44 Homepage: https://cran.r-project.org/package=saeHB.TF.beta Description: CRAN Package 'saeHB.TF.beta' (SAE using HB Twofold Subarea Model under Beta Distribution) Estimates area and subarea level proportions using the Small Area Estimation (SAE) Twofold Subarea Model with a hierarchical Bayesian (HB) approach under Beta distribution. A number of simulated datasets generated for illustration purposes are also included. The 'rstan' package is employed to estimate parameters via the Hamiltonian Monte Carlo and No U-Turn Sampler algorithm. The model-based estimators include the HB mean, the variation of the mean, and quantiles. For references, see Rao and Molina (2015) , Torabi and Rao (2014) , Leyla Mohadjer et al.(2007) , Erciulescu et al.(2019) , and Yudasena (2024). Package: r-cran-saemspe Architecture: arm64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 750 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 437428 MD5sum: 866d429d8346b050518c48b6abbfc3f4 SHA1: 4b11a5c2fd75208c65f5f68596416d9a4acff3d8 SHA256: fa73240501af76d71fd5f74ee5236d793e2b10baba2c6eb9a726332eb9849a9d SHA512: e5191111cf58e21894770969a23404f5ac9f198f942e1347597f9b56a19252febc56b150fa757a146b4d1bfb29c3a113b5a6144f120ccc87247aab6503ba6744 Homepage: https://cran.r-project.org/package=saeMSPE Description: CRAN Package 'saeMSPE' (Computing MSPE Estimates in Small Area Estimation) Compute various common mean squared predictive error (MSPE) estimators, as well as several existing variance component predictors as a byproduct, for FH model (Fay and Herriot, 1979) and NER model (Battese et al., 1988) in small area estimation. Package: r-cran-saerobust Architecture: arm64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 460 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 260214 MD5sum: b612c312998254262f059cfe4489ac8d SHA1: 9e87761bd214dccc3240510d3d175253f6fa13dd SHA256: b3d2b4073a84cdb57e8cecee0d91b28361f8df0b59de5c977b3cede0d83f04dd SHA512: 933e7606790221a92c5b2208c12696a6408ef94cde34022ed31f84dc92046e266f9a3339c45b35355fedfe98c159afd84ecd5d5192f2f7c890a04f598c6af863 Homepage: https://cran.r-project.org/package=saeRobust Description: CRAN Package 'saeRobust' (Robust Small Area Estimation) Methods to fit robust alternatives to commonly used models used in Small Area Estimation. The methods here used are based on best linear unbiased predictions and linear mixed models. At this time available models include area level models incorporating spatial and temporal correlation in the random effects. Package: r-cran-safepg Architecture: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 164 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-safepg_0.0.1-1.ca2604.1_arm64.deb Size: 54374 MD5sum: 2785607a2429df1c487a1b23102df71b SHA1: 17ea96f697df26a7d95109285947486ecf05ea13 SHA256: 79fb3d32bc7244d31c33e7ddf698ccd046b79de7778600c219e3649436b63e11 SHA512: 5067d93b1f0b41ecb0f185a9319ff70b57aef874a9e8057c538baa11d9663106c12cf08f49345031f487779e625908035830d6bad9b40c9de05350ca94ad0f7a Homepage: https://cran.r-project.org/package=SAFEPG Description: CRAN Package 'SAFEPG' (A Novel SAFE Model for Predicting Climate-Related Extreme Losses) The goal of 'SAFEPG' is to predict climate-related extreme losses by fitting a frequency-severity model. It improves predictive performance by introducing a sign-aligned regularization term, which ensures consistent signs for the coefficients across the frequency and severity components. This enhancement not only increases model accuracy but also enhances its interpretability, making it more suitable for practical applications in risk assessment. Package: r-cran-sagmm Architecture: arm64 Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 73364 MD5sum: 67b67c5e1d2875717a6749d01727513e SHA1: 2f9108729bf34ac9d213846d95e5aa7a9a755d8f SHA256: c5a2522e79aa2631a9963a8f98f0f11d8e56c5b5ed6e7a63c30a71de0ec12a7e SHA512: 529c00e2c9a4749487b70af772d9e1571a40e6972a334aab9be17d4bd23f2c9907e85cc56c134ea10cc279f99c6d240aff9568a02aa111be22aba7cfb8c92fd2 Homepage: https://cran.r-project.org/package=SAGMM Description: CRAN Package 'SAGMM' (Clustering via Stochastic Approximation and Gaussian MixtureModels) Computes clustering by fitting Gaussian mixture models (GMM) via stochastic approximation following the methods of Nguyen and Jones (2018) . It also provides some test data generation and plotting functionality to assist with this process. Package: r-cran-sakura Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 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_arm64.deb Size: 20520 MD5sum: d84bdb0231bee324c00d207a144d1068 SHA1: c99a6ff50ca099acbb63434cf7c2f6c0c35aa288 SHA256: 20ca1b7a377f528d3b3b52f434401425aeb7cc087017102d2fc591adcfc858f8 SHA512: aeb2528dab540abd5e01064f2202cef48c37791e54dc7c3b8b47768e2658727cb2539421a2abd574ee0e61d0e2350ecf6fb79222481fdf9c48fc7347d6eee1c6 Homepage: https://cran.r-project.org/package=sakura Description: CRAN Package 'sakura' (Extension to R Serialization) Extends the functionality of R serialization by augmenting the built-in reference hook system. This enhanced implementation allows optimal, one-pass integrated serialization that combines R serialization with third-party serialization methods. Facilitates the serialization of even complex R objects, which contain non-system reference objects, such as those accessed via external pointers, for use in parallel and distributed computing. Package: r-cran-sales Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 236 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_arm64.deb Size: 149128 MD5sum: 1a7107b656ee65f4aa2b97a1d44d8b6c SHA1: cd560c07257013bbf6f3afdd943cc92b6920f7c7 SHA256: 2e79817dc0e9231f71bc23a6815733d2bac8dba4028f1470b450b4ec0441fa90 SHA512: 320628fc800a5bb2473be71b3e9ec3cefca45555dc7ef992fb29f5526c740590cc81faa38de053263e4ead8bc50313f9abe51bccd87bc458053d791a204a5f5e Homepage: https://cran.r-project.org/package=SALES Description: CRAN Package 'SALES' (The (Adaptive) Elastic Net and Lasso Penalized Sparse AsymmetricLeast Squares (SALES) and Coupled Sparse Asymmetric LeastSquares (COSALES) using Coordinate Descent and ProximalGradient Algorithms) A coordinate descent algorithm for computing the solution paths of the sparse and coupled sparse asymmetric least squares, including the (adaptive) elastic net and Lasso penalized SALES and COSALES regressions. Package: r-cran-salso Architecture: arm64 Version: 0.3.78-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1400 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-salso_0.3.78-1.ca2604.1_arm64.deb Size: 666328 MD5sum: 1d9b908fe0668de47ee18eb2dc1dd056 SHA1: 122cfe53e014d304896b1eebfeb796ad705e7559 SHA256: a15bdcf813ba8cb7a53ab2d73064e220db21d15f443352c0077d5184122bdca8 SHA512: 0cdd5f01c14cded1bf01edef0164bec626577050960d7baefdec05ce7902fd0fe8d05b28833d6641122136bb64de4137648c951d58cf2f27685a95289f23a91c Homepage: https://cran.r-project.org/package=salso Description: CRAN Package 'salso' (Search Algorithms and Loss Functions for Bayesian Clustering) The SALSO algorithm is an efficient randomized greedy search method to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. The algorithm is implemented for many loss functions, including the Binder loss and a generalization of the variation of information loss, both of which allow for unequal weights on the two types of clustering mistakes. Efficient implementations are also provided for Monte Carlo estimation of the posterior expected loss of a given clustering estimate. See Dahl, Johnson, Müller (2022) . Package: r-cran-sam Architecture: arm64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 392 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-sam_1.3-1.ca2604.1_arm64.deb Size: 190522 MD5sum: 75210855f5e0eeb55ce4c09652fa7460 SHA1: ad3f7e3d69586b11c472adce6a4d297034036e13 SHA256: b8fb342ee08e73aff1aa4e8e5caa720e20db6025b5d470951ca69aafce2b9709 SHA512: 6f0bc313dae765efb086b5e799bbc5f0d46ac8f5d70c1033ff34bfcdcca57bad142d506a5a2e58386ed729ec9919d7dafd2169e531ec885909e7c35137d0b8c1 Homepage: https://cran.r-project.org/package=SAM Description: CRAN Package 'SAM' (Sparse Additive Modelling) Computationally efficient tools for high dimensional predictive modeling (regression and classification). SAM is short for sparse additive modeling, and adopts the computationally efficient basis spline technique. We solve the optimization problems by various computational algorithms including the block coordinate descent algorithm, fast iterative soft-thresholding algorithm, and newton method. The computation is further accelerated by warm-start and active-set tricks. Package: r-cran-samc Architecture: arm64 Version: 4.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1600 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_arm64.deb Size: 1060054 MD5sum: 866f8d0fb9eb95addec678b94107e4ea SHA1: 121cb9c2ced8c6b583c0b96378acdc7f1b8d6ada SHA256: 7842ef5ce3454b121f47aba9a58caa7508dc63f4973481ce59ab65d09898cd10 SHA512: 7cabf62bec74382b12d6f974319906ce67be0ef062a18ddac1675a9abfcbf0867da5f0403701bf723ba5ce8f98f6df8052ee4c4bdb1be84010f63d4484b57630 Homepage: https://cran.r-project.org/package=samc Description: CRAN Package 'samc' (Spatial Absorbing Markov Chains) Implements functions for working with absorbing Markov chains. The implementation is based on the framework described in "Toward a unified framework for connectivity that disentangles movement and mortality in space and time" by Fletcher et al. (2019) , which applies them to spatial ecology. This framework incorporates both resistance and absorption with spatial absorbing Markov chains (SAMC) to provide several short-term and long-term predictions for metrics related to connectivity in landscapes. Despite the ecological context of the framework, this package can be used in any application of absorbing Markov chains. Package: r-cran-samgep Architecture: arm64 Version: 0.1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2308 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 2212104 MD5sum: fb60c130084ddc10e16fe6ed9aea6fb8 SHA1: f0bc82cb562dc24605362e2873b902bb808251ca SHA256: 571a03b1f79c7e49a486ba941ab868250e68d4400ad2d7d86b4ccd97c1fab219 SHA512: d9ab19f6b49dc9f430f67e9b80df88bf4e7e8b1a8a5a46ec81517dc6843bcfc6c9f7818812e994b4598dcbfb2db1063a9f22530480d75590d191531575176b00 Homepage: https://cran.r-project.org/package=SAMGEP Description: CRAN Package 'SAMGEP' (A Semi-Supervised Method for Prediction of Phenotype Event Times) A novel semi-supervised machine learning algorithm to predict phenotype event times using Electronic Health Record (EHR) data. Package: r-cran-samon Architecture: arm64 Version: 4.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2577 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-samon_4.0.2-1.ca2604.1_arm64.deb Size: 2165996 MD5sum: 7ab11422c2569e6d80eed68ffd9aec4f SHA1: 954a16a17f5d4264191deb97bf3f73e38c684c5b SHA256: ee4558e83bbb53031ace82db52677a0ea7eab72089e0ec1a1491188767a9ce56 SHA512: 4f68f3f8ddc379aa57dd8f511ddecc4b0c55d47be70aa2cf8e6d70471b5e3715bd9aabe9300ca2fded6cf854d789483b114d2a7985bc314d071c0c2b34707aec Homepage: https://cran.r-project.org/package=samon Description: CRAN Package 'samon' (Sensitivity Analysis for Missing Data) In a clinical trial with repeated measures designs, outcomes are often taken from subjects at fixed time-points. The focus of the trial may be to compare the mean outcome in two or more groups at some pre-specified time after enrollment. In the presence of missing data auxiliary assumptions are necessary to perform such comparisons. One commonly employed assumption is the missing at random assumption (MAR). The 'samon' package allows the user to perform a (parameterized) sensitivity analysis of this assumption. In particular it can be used to examine the sensitivity of tests in the difference in outcomes to violations of the MAR assumption. The sensitivity analysis can be performed under two scenarios, a) where the data exhibit a monotone missing data pattern (see the samon() function), and, b) where in addition to a monotone missing data pattern the data exhibit intermittent missing values (see the samonIM() function). Package: r-cran-sampling Architecture: arm64 Version: 2.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1037 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_arm64.deb Size: 757848 MD5sum: 67450485c649073e3e580227684e2dbd SHA1: 7f39cca2c89375e86cea0e6e4d292ad9e5d5cf52 SHA256: f8a4fabb747ab0316debbcbe03520c817dbf2d09689cb0bca06bb9212a9170de SHA512: 3e1e828210681bbf4390a3dd78f3720d792bd6218083b71ccf85befff2104c3d73ed838a6bb9c52bdba61999b0df307cb32d51e6a0416b81e49628ece2fb391b Homepage: https://cran.r-project.org/package=sampling Description: CRAN Package 'sampling' (Survey Sampling) Functions to draw random samples using different sampling schemes are available. Functions are also provided to obtain (generalized) calibration weights, different estimators, as well some variance estimators. Package: r-cran-samplingbigdata Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 119 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-samplingbigdata_1.0.0-1.ca2604.1_arm64.deb Size: 30222 MD5sum: f924fe83a836ad482a708788e3627184 SHA1: ebc9a70eae689ecf463523f0262edafca62e18c2 SHA256: a76bc4eba7a68f7e70d9eaf3d4d10ce4674f1f5ecf0f59788429c55649efc469 SHA512: f862c733aa5e23638cca22cbcda376519366a4c2cae8ab61a700e48263249b073ac2360a957a6b9b76dd32e3ca3aff4a7323fd76b269ac2b6fbbb34befdeb90f Homepage: https://cran.r-project.org/package=SamplingBigData Description: CRAN Package 'SamplingBigData' (Sampling Methods for Big Data) Select sampling methods for probability samples using large data sets. This includes spatially balanced sampling in multi-dimensional spaces with any prescribed inclusion probabilities. All implementations are written in C with efficient data structures such as k-d trees that easily scale to several million rows on a modern desktop computer. Package: r-cran-samplingvarest Architecture: arm64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 578 Depends: r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-samplingvarest_1.5-1.ca2604.1_arm64.deb Size: 448348 MD5sum: 2bce2823e865bc012ae1eaa0ec8f1ee8 SHA1: e607db86ec7ea407a9526e2d2a76aed01d63321c SHA256: 906e895fa0af01f8d58b16f45bf3d67bdfd2a3c7bd4a2f19b1cb2077c763fc43 SHA512: a7130648b91933ac6053f80c6fe446707dd8d8261bfd0eef6958e5c65052ccc69623655ab50ac45ac0ad8bf127c6e6ffb7a2a57d763947fd22c7ec255f2d828e Homepage: https://cran.r-project.org/package=samplingVarEst Description: CRAN Package 'samplingVarEst' (Sampling Variance Estimation) Functions to calculate some point estimators and estimate their variance under unequal probability sampling without replacement. Single and two-stage sampling designs are considered. Some approximations for the second-order inclusion probabilities (joint inclusion probabilities) are available (sample and population based). A variety of Jackknife variance estimators are implemented. Almost every function is written in C (compiled) code for faster results. The functions incorporate some performance improvements for faster results with large datasets. Package: r-cran-samplr Architecture: arm64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2056 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_arm64.deb Size: 966442 MD5sum: c8a3308471ee1af604b4fb9c584acad1 SHA1: c91a867a3a88b7fdccc2bc413766260319048188 SHA256: b9063cd96bb2255dbf223f6b011e464c7ac11da86a8132c9a7de3a4313eac84a SHA512: 35302e7e76a36ac3f4a85baa70894d370f04852b741779ecf574c47c0b1b185986405c21bdcb9c20e73bfaf6b74192f142087627d7c5abc73f48e1c30e80288e Homepage: https://cran.r-project.org/package=samplr Description: CRAN Package 'samplr' (Compare Human Performance to Sampling Algorithms) Understand human performance from the perspective of sampling, both looking at how people generate samples and how people use the samples they have generated. A longer overview and other resources can be found at . Package: r-cran-samr Architecture: arm64 Version: 3.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3959 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-bioc-impute, r-cran-matrixstats, r-cran-shiny, r-cran-shinyfiles, r-cran-openxlsx, r-cran-gsa Filename: pool/dists/resolute/main/r-cran-samr_3.0.1-1.ca2604.1_arm64.deb Size: 3775706 MD5sum: a68ebe66c635cb79a02437e49b030f98 SHA1: 99ca435b627fabe61107127ecf7839f6489d647d SHA256: 5874adc9afcdaf3456087a9d2327407993da9a9d12a930c0930ff6e4e32dcd9b SHA512: a74da88e9f25d96c07902c159aa5b0f0aeadab4518d1a8e3ff9f8c1b463b9878c4675bb630d3bdeddb844e0180c0252293381c52dcbd37c1a2c21da4923143a1 Homepage: https://cran.r-project.org/package=samr Description: CRAN Package 'samr' (SAM: Significance Analysis of Microarrays) Significance Analysis of Microarrays for differential expression analysis, RNAseq data and related problems. Package: r-cran-samsaralight Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 721 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_arm64.deb Size: 502306 MD5sum: 003f0968348687d3540110561ab54366 SHA1: b34caf8bd96606470770af1f1bf9ed7d313a2028 SHA256: a277cba0110bb6ebd87c6b02019a161c3961e7b06973fc7e34f070b47b47ad8f SHA512: 8448cebac6ced69e577bb5ee367e7d0b40e03e600d9b2731042e99e36e2773fab0af1e469d2c6eb384a61f7b2ffe4a38e779f840553044a0fd1097e0a11a9a32 Homepage: https://cran.r-project.org/package=SamsaRaLight Description: CRAN Package 'SamsaRaLight' (Simulate Tree Light Transmission Using Ray-Tracing) Provides tools to simulate light transmission in forest stands using three-dimensional ray-tracing. The package allows users to build virtual stands from tree inventories and to estimate (1) light intercepted by individual trees, (2) light reaching the forest floor, and (3) light at virtual sensors. The package is designed for ecological and forestry applications, including the analysis of light competition, tree growth, and forest regeneration. The implementation builds on the individual-based ray-tracing model SamsaraLight developed by Courbaud et al. (2003) . Package: r-cran-samtool Architecture: arm64 Version: 1.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3488 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-msetool, r-cran-tmb, r-cran-abind, r-cran-dplyr, r-cran-gplots, r-cran-pbapply, r-cran-rmarkdown, r-cran-snowfall, r-cran-vars, r-cran-rcppeigen Suggests: r-cran-caret, r-cran-corpcor, r-cran-covr, r-cran-extradistr, r-cran-ggplot2, r-cran-gmisc, r-cran-knitr, r-cran-mvtnorm, r-cran-numderiv, r-cran-reshape2, r-cran-shiny, r-cran-testthat, r-cran-tmbstan, r-cran-usethis Filename: pool/dists/resolute/main/r-cran-samtool_1.9.1-1.ca2604.1_arm64.deb Size: 2229160 MD5sum: fbc7d0c8cafff6a4b5f04383fbb4f75f SHA1: 2b435d458eb6ace10624a0314b3546c039ea05c3 SHA256: e63cc150a914d8265376d86f4a564c5dd777602f92c3d13764450b0c9d9479f0 SHA512: b55078e149edb99e51b200d6c2eeca738579a44b2f9821ed129966f071e446dd6845f2e0cce97c31a1c7cbd5794743c038b2864042ab142ca02b794895aba88c 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: arm64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 927 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_arm64.deb Size: 512974 MD5sum: 39d2912a3df78d91af0e4495f4494b41 SHA1: b1a1769d3bc2cee7fbe52ac4bafc4614f2e69d2f SHA256: 39afc7214fc9a866ddfd2a01ff3a9ac329a0088a1bf57ab5dbde2904f462d533 SHA512: 383f0660b8aca11c4541887da3ac4c1ad62db04c1a8f982fed4e887bb280a95c9479c044c97349bf770dac34df1dd6ba588afafd94997f4041bd111fdcad53fd Homepage: https://cran.r-project.org/package=sanba Description: CRAN Package 'sanba' (Fitting Shared Atoms Nested Models via MCMC or Variational Bayes) An efficient tool for fitting nested mixture models based on a shared set of atoms via Markov Chain Monte Carlo and variational inference algorithms. Specifically, the package implements the common atoms model (Denti et al., 2023), its finite version (similar to D'Angelo et al., 2023), and a hybrid finite-infinite model (D'Angelo and Denti, 2024). All models implement univariate nested mixtures with Gaussian kernels equipped with a normal-inverse gamma prior distribution on the parameters. Additional functions are provided to help analyze the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) , D’Angelo, Canale, Yu, Guindani (2023) , D’Angelo, Denti (2024) . Package: r-cran-sanic Architecture: arm64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 729 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 279452 MD5sum: a97cef1e5bcfbfc85b0e8a2a9cfe27c7 SHA1: e9b24fe8191d9fedf7c42b98a6b58266022180ae SHA256: 5e8b7dcc45de6f4818dc2f96b13a79e6e44569ac5403d0c99f59f25324a7975b SHA512: 528fe29abea032d0d8c52d89f66d18a8a83d040f040d491cd865c75e214329fbf5d701a91af0c393c2b831d833300c3925a7572e46b6f9fe52ba241f05625f53 Homepage: https://cran.r-project.org/package=sanic Description: CRAN Package 'sanic' (Solving Ax = b Nimbly in C++) Routines for solving large systems of linear equations and eigenproblems in R. Direct and iterative solvers from the Eigen C++ library are made available. Solvers include Cholesky, LU, QR, and Krylov subspace methods (Conjugate Gradient, BiCGSTAB). Dense and sparse problems are supported. Package: r-cran-sanitizers Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: libc6 (>= 2.17), libstdc++6 (>= 4.1.1), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sanitizers_0.1.1-1.ca2604.1_arm64.deb Size: 16278 MD5sum: 5eeba62a14ca9692a76823842d285ac6 SHA1: 0ee8de1bead80c53e7f69950d424773d4277024f SHA256: c5691a1f4a3d93ea5205072847bfcf050b24392d06530615b10ad41015a4cf7c SHA512: f133a89ffbb2fb7e95d75c389a20caca79d72fd000738d621753f6635e2c221f1abd72d41e8c47f60b6027fde8c58602eb971105fc3a3be890f3a82664c3eca8 Homepage: https://cran.r-project.org/package=sanitizers Description: CRAN Package 'sanitizers' (C/C++ Source Code to Trigger Address and Undefined BehaviourSanitizers) Recent gcc and clang compiler versions provide functionality to test for memory violations and other undefined behaviour; this is often referred to as "Address Sanitizer" (or 'ASAN') and "Undefined Behaviour Sanitizer" ('UBSAN'). The Writing R Extension manual describes this in some detail in Section 4.3 title "Checking Memory Access". This feature has to be enabled in the corresponding binary, eg in R, which is somewhat involved as it also required a current compiler toolchain which is not yet widely available, or in the case of Windows, not available at all (via the common Rtools mechanism). As an alternative, pre-built Docker containers such as the Rocker container 'r-devel-san' or the multi-purpose container 'r-debug' can be used. This package then provides a means of testing the compiler setup as the known code failures provides in the sample code here should be detected correctly, whereas a default build of R will let the package pass. The code samples are based on the examples from the Address Sanitizer Wiki at . Package: r-cran-sanple Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 689 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 490646 MD5sum: c22846dbe861095c3ad97c65132978cf SHA1: ad610003268ba4a9c330d1c4f480724709949f41 SHA256: 5290c8764ded8757dc9954712867f3e2d08232683b2ede9c52d3038cab5d9c08 SHA512: c408dd474b3c9c1e35db64d55ecda5a442d03ed186b24936587557fde68ccd63387918709c1cfd2ee4aac72cf8765f27c46974ee8b8b52b134e68c31ad7b4497 Homepage: https://cran.r-project.org/package=SANple Description: CRAN Package 'SANple' (Fitting Shared Atoms Nested Models via Markov Chains Monte Carlo) Estimate Bayesian nested mixture models via Markov Chain Monte Carlo methods. Specifically, the package implements the common atoms model (Denti et al., 2023), and hybrid finite-infinite models. All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyzing the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) , D’Angelo, Denti (2024) . Package: r-cran-santoku Architecture: arm64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 709 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 417296 MD5sum: 7cc16df2d5795a93730f3fbf5c862fcc SHA1: a5bbf23ad1cec15540b2859a100171aa72fa6361 SHA256: c6d46cc816f18aa2d1512e8fa198f441d32915b3b5aef2e352d02f5fbbecbecc SHA512: d6b000b055a6364dd0305152e4ff102489b43005c04b460b426623f4a180afb7ff4c8aa1faff3ae13f6d14edeb97c78e320f81c3f22be90914f6f8d92c743505 Homepage: https://cran.r-project.org/package=santoku Description: CRAN Package 'santoku' (A Versatile Cutting Tool) A tool for cutting data into intervals. Allows singleton intervals. Always includes the whole range of data by default. Flexible labelling. Convenience functions for cutting by quantiles etc. Handles dates, times, units and other vectors. Package: r-cran-sanvi Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 821 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), 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_arm64.deb Size: 573338 MD5sum: df4edfb39d623e31e3044b99091149f4 SHA1: 76e59db14561290f7c5a8e0d60e3b284946b5c57 SHA256: 08c5693cdbd54cdfef7060038acbf709cb6c9021513639cb4ee93ab71123ec45 SHA512: 4ed91a4044e4bd5f65477b0bab28d43a86a39576a7105264e9b889d39a281c5242734076b7ea4bea56853e75d24f9f9e4d8462300e243628b1870c3f22bb7de3 Homepage: https://cran.r-project.org/package=SANvi Description: CRAN Package 'SANvi' (Fitting Shared Atoms Nested Models via Variational Bayes) An efficient tool for fitting the nested common and shared atoms models using variational Bayes approximate inference for fast computation. Specifically, the package implements the common atoms model (Denti et al., 2023), its finite version (D'Angelo et al., 2023), and a hybrid finite-infinite model. All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyze the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) , D’Angelo, Canale, Yu, Guindani (2023) . Package: r-cran-sapp Architecture: arm64 Version: 1.0.9-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 597 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sapp_1.0.9-4-1.ca2604.1_arm64.deb Size: 469064 MD5sum: 7819ac1b0ef6f81fbd727a05e70d4f61 SHA1: 87708cb54d6a9f362db608397db5f5ad46952b73 SHA256: 51fb2253343c7d21d90dec69b29cd684bc158abb8ba3b9f63d642879732cb774 SHA512: ef120837ba009c9edad3472cbe86cc17fdb82a2f98e84b7223dc49f44c56316cbd35203fb9f88b0d1b5a0937aff3049869060b68ef6b41e13ca5c56e14f27051 Homepage: https://cran.r-project.org/package=SAPP Description: CRAN Package 'SAPP' (Statistical Analysis of Point Processes) Functions for statistical analysis of point processes. Package: r-cran-sar Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 559 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_arm64.deb Size: 348722 MD5sum: e66e28817f376d3bd65ae85957dff833 SHA1: 466c4c8a0b87990e53e1f87629ae1c96dd081bb7 SHA256: f98ac4af2b22294603c7a71560525c8791f061286dcba19ed102d2b0df92ab3e SHA512: c0f8b9f5a2adf432e7e91781970a781883ba6ad6ea9cfa3d5fc8e9a5370008acadf3d6ea620e3cea6a80a40955562d44e18f1598d9cc31d1395de7885bb9dfb9 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. 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Package: r-cran-sarima Architecture: arm64 Version: 0.9.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1839 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1426298 MD5sum: 237d62875080ac00072ce0d49d0ba666 SHA1: 05a8e6d9cd245aaa1123f792c439506a60901adf SHA256: 51705a6bc062a85cad192f096513aa08ea551a6d2c64296c3d911fad271aa833 SHA512: 669653beec3f9c2b5a9961a81b8949a1b2788430601468472e5f639c79a34ae4a0212e520c0e84dafd7df3028bc5724710e4eb65b3e9507c6fb7c27ba06d4dd9 Homepage: https://cran.r-project.org/package=sarima Description: CRAN Package 'sarima' (Simulation and Prediction with Seasonal ARIMA Models) Functions, classes and methods for time series modelling with ARIMA and related models. The aim of the package is to provide consistent interface for the user. For example, a single function autocorrelations() computes various kinds of theoretical and sample autocorrelations. This is work in progress, see the documentation and vignettes for the current functionality. Function sarima() fits extended multiplicative seasonal ARIMA models with trends, exogenous variables and arbitrary roots on the unit circle, which can be fixed or estimated (for the algebraic basis for this see , a paper on the methodology is being prepared). Package: r-cran-sarsop Architecture: arm64 Version: 0.6.16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3999 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_arm64.deb Size: 815848 MD5sum: 9dadbf10ed3414fb4d9a065b6fb3b51a SHA1: f7e0f3db8d2b1329d3ed649221f0aac762c91989 SHA256: b4c0b5b626b42b7ce774c8885d03a668277d90dd567ebe180df1ca978d14d79f SHA512: 3481d56129c342b7466bb3264873991a16b2c787c2815c3a109082a884707411a2eebf12450f63d9f2c02a84a70daa6f9a97523b9f4d1124869af52deb5967cd 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: arm64 Version: 0.4.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4610 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_arm64.deb Size: 2168988 MD5sum: 237a9e7446c80f2ce852259c54b4ca34 SHA1: eac57a319b202e58e4e833b4ec30048f70e3da0f SHA256: f42304a7f50a1029c9f00861699e995bd64b5c669f5fa11eedef8669bea12689 SHA512: 03cf6db0c9bb8af5389cb27a0fead7445c0de1b1f62e38e5d572cd051b497ce696662f046f50b3593fe70f6d3ad58f66c333b660bc6526800519a2c9cc294ea5 Homepage: https://cran.r-project.org/package=sass Description: CRAN Package 'sass' (Syntactically Awesome Style Sheets ('Sass')) An 'SCSS' compiler, powered by the 'LibSass' library. With this, R developers can use variables, inheritance, and functions to generate dynamic style sheets. The package uses the 'Sass CSS' extension language, which is stable, powerful, and CSS compatible. Package: r-cran-satdad Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4488 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_arm64.deb Size: 2702846 MD5sum: dbaf1bfb26cf2d45292f3e19bd5077c9 SHA1: 324b54cc4b51eff1c4053ed63c30c1cd3b77df0d SHA256: 5f85f9238196116d32adcf2bc877429dbb850c2ef6dc190631573db13fbb7743 SHA512: 51292d3cf18c9e21cbf5912ff202646d89d8c2041d0377a3a2bccaf5857cae4238aa82d05fb8373e36178e7d88c2d683e69a67a7b03c878530c1dbf5b32ad077 Homepage: https://cran.r-project.org/package=satdad Description: CRAN Package 'satdad' (Sensitivity Analysis Tools for Dependence and AsymptoticDependence) Tools for analyzing tail dependence in any sample or in particular theoretical models. The package uses only theoretical and non parametric methods, without inference. The primary goals of the package are to provide: (a)symmetric multivariate extreme value models in any dimension; theoretical and empirical indices to order tail dependence; theoretical and empirical graphical methods to visualize tail dependence. Package: r-cran-satellite Architecture: arm64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3636 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_arm64.deb Size: 2808202 MD5sum: 8d1dd864b626bd96cd21c8f9b66d71c6 SHA1: 9d304855f8c204ca73a775f18bd492dce94b7a1c SHA256: fda0c4d244f5dab9fbadeaeefc6180bfcd0dcb0492c9227cfd610bedf89dc175 SHA512: 6ae6d0000e940e65b7b986030be399213a9a02cfb14891f347c7dfacef480b89eb6b70bf035e6e30e28b428354e97f27ad44a4c02a249a18fbdbed713879fa1a Homepage: https://cran.r-project.org/package=satellite Description: CRAN Package 'satellite' (Handling and Manipulating Remote Sensing Data) Herein, we provide a broad variety of functions which are useful for handling, manipulating, and visualizing satellite-based remote sensing data. These operations range from mere data import and layer handling (eg subsetting), over Raster* typical data wrangling (eg crop, extend), to more sophisticated (pre-)processing tasks typically applied to satellite imagery (eg atmospheric and topographic correction). This functionality is complemented by a full access to the satellite layers' metadata at any stage and the documentation of performed actions in a separate log file. Currently available sensors include Landsat 4-5 (TM), 7 (ETM+), and 8 (OLI/TIRS Combined), and additional compatibility is ensured for the Landsat Global Land Survey data set. Package: r-cran-sbde Architecture: arm64 Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 174 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 95810 MD5sum: 0e910eb5110cfd1c9580f044c931ee71 SHA1: d91456d36e81de95dab9e614f018a78fe7c030e1 SHA256: 860388aacb7ff92adb44456cf90279bc7cc1181560d1c247d80f3ec42e63ab7f SHA512: aa264585fea91402bd2629f6ccafad8dfc05161b9c8ba9ef6fe98fb08344e3547628ec3fcaf689f7180440ea7e2598ab07f0436c051dc42f1d401b43ef1bfd12 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: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1700 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_arm64.deb Size: 1414958 MD5sum: 9c4fd75ec66e82538ee54b4c515e3f3f SHA1: ad77d814688ca9b53f7789a367ae387044fd1321 SHA256: 277375801c869552616c3adab65ebdaf4408547ca52afb674064e50562461c8c SHA512: d571883b5fb7b1db7a05c2dcbb35f79ecfa452233ec641f10dec996752829cfe437a9531c4bcee4dab775af576b8d81516bd67587d68fcb9772f42412a7cfbe6 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), . Package: r-cran-sbic Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1326 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-polca, r-cran-r.oo, r-cran-r.methodss3, r-cran-mclust, r-cran-igraph, r-cran-rcpp, r-cran-combinat, r-cran-flexmix, r-cran-hash Suggests: r-cran-testthat, r-cran-mvtnorm, r-cran-knitr, r-cran-mass Filename: pool/dists/resolute/main/r-cran-sbic_0.2.0-1.ca2604.1_arm64.deb Size: 1064132 MD5sum: 2bfa98809752fcd9b9534b150f6a14df SHA1: c55084946cc25bee145d8383dca192b3a0e153dd SHA256: cf7d86c0b47731419e9b33d32911b90dd336233fd550ad4586154937eb20cb82 SHA512: 3824228926c64781e2e36607e8fa2438c5dd52b30d650bb107a3b9720c8051c0734b7c50c59b7137291d9f355352a598e81f527965be4c368dace8b901c4a925 Homepage: https://cran.r-project.org/package=sBIC Description: CRAN Package 'sBIC' (Computing the Singular BIC for Multiple Models) Computes the sBIC for various singular model collections including: binomial mixtures, factor analysis models, Gaussian mixtures, latent forests, latent class analyses, and reduced rank regressions. Package: r-cran-sbim Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 444 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_arm64.deb Size: 180556 MD5sum: 0ea4a2416245604f514e4a5bd495adaf SHA1: fb520c23feb1d8aa5d07c6899a7abe8839c77c69 SHA256: 097bb84d327c1a27f56682ab62dcf5957f5a78fd3c6b6f5428107ae29c77893e SHA512: 65f677d86171f20e241668cd5f6b8164b18cb1c605e4c6a499113db3ff870598a96c2f2103735500681c307f3179f93e80253d4b86e7d0d9224597541a4bb0cd 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). Package: r-cran-sbm Architecture: arm64 Version: 0.4.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1685 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-alluvial, r-cran-magrittr, r-cran-dplyr, r-cran-purrr, r-cran-blockmodels, r-cran-r6, r-cran-rcpp, r-cran-igraph, r-cran-ggplot2, r-cran-gremlins, r-cran-stringr, r-cran-rlang, r-cran-reshape2, r-cran-prodlim, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-spelling, r-cran-knitr, r-cran-rmarkdown, r-cran-aricode, r-cran-covr Filename: pool/dists/resolute/main/r-cran-sbm_0.4.7-1.ca2604.1_arm64.deb Size: 1206028 MD5sum: 2327f1e956c26744fd5353389ef130ba SHA1: db5aa65fb66d806afd25aa5e70d34dcd6bce202a SHA256: be0f9765971fc6d89ff4bd3824e645f07c312f07bc552d41ba3e1ce0fc4a190b SHA512: 240bc77e860f958d0b42cbc5fa184322b8256a85e744c200e1ad18c27e3af0c0db6c2c7c3f0888919582329518628d9c9e9b992b8a64e4564d6f428bc6bb2964 Homepage: https://cran.r-project.org/package=sbm Description: CRAN Package 'sbm' (Stochastic Blockmodels) A collection of tools and functions to adjust a variety of stochastic blockmodels (SBM). 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: arm64 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_arm64.deb Size: 74800 MD5sum: fe1da860dbca18e90e65f40999353618 SHA1: aa6b4448acf96ae73f88ae3b13bb8468ef0a2060 SHA256: 0e88a95dc3defdfb84486fc678c186909813604dc0ce1c00c51cd3e4044453b4 SHA512: 746b7d711809742f0df51c0af0f54dc85185747911eaef1354588d2cd2ec75b10107ef8491025408ddf749ad7a9fd37f75fb871de8de69df8481770b7f01b2c0 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: arm64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 59910 MD5sum: bf269893205ba910746a278088acdaa2 SHA1: 3939265cc08d701761083b84efb854ef0e159096 SHA256: cab0ba0e8ed126534b382cd6deec0242227e05914091724a98ee7f267b190d8a SHA512: 27fe62b3421c05607c31d58af271d3319563eaa05ce8ecb1f0e0666a9cf90a49950781976b3842434fea90c6d2dc471808922dbe961f6ceab1a6e5e4f0749ef1 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: arm64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1022 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_arm64.deb Size: 443498 MD5sum: c9eb0022ac68b25d9e69130f09ded3bd SHA1: 67c69bfd88d8c372f26b11addc971a72af34210b SHA256: 162d51fd7b4e609058086d962947c08fe8fa9acb92b74ca59f21bde614085b29 SHA512: 6fcacf7a18b9dae6eb70fc16d02f4cbcab0ce515f269be4f9cb2e44bf461bce9d704a67f1c740788e495af5091855a870c5a3f771c9136375de047a2fdd2447a 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: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1246 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_arm64.deb Size: 1063424 MD5sum: e0a4e8a6c476e6c8e5b4df508b23f167 SHA1: c0e0530b9e94851993b6224b37e91b021fc00e70 SHA256: b5deb294cc1ab868f02d741d67b746018e5e51037e51538464f215b631f0f4e4 SHA512: 4956878813ec5ebd5b81a75d6c5d274f983075a54091974ad9d0ad5da722004762de451f3cf2c2c001275b2ec3c1220a8be6cfffcccc8e528652b4ad5ae747da 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: arm64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 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_arm64.deb Size: 87426 MD5sum: bd045efdaec3172dee483dcac96985ea SHA1: a5f0609269e02e42aebae2d942793f759e300bdd SHA256: 61b3f49eeb2a92fb07c2c97df4148526d0212730bcacb2d9dff9460e868d2099 SHA512: 52318d905c79ecb72bc96c916f84186e809170cd967a84a0a5e463456fc9e96525e1b336cea2f2f931bf9f72dd9ce6e501b618cd0c78ba005c53ede785165e0f 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: arm64 Version: 0.2-1.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), 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_arm64.deb Size: 377440 MD5sum: c8b7220d4d5fe5e0b4d742ad158d259d SHA1: 37bc1034348e015caa5e55117b5eaf1082f5fbbc SHA256: 57e0299000228a4cf0649bf1757ce3dab3803df62fbd4beb7620fef582b0a19c SHA512: ebb1230fd1186f3438ec5a2faac22a41b688653fabd3b333b7d3eb3d2acaec9c7b82c1cf5e04ca6450da9da6c105c7aedf36bccb6113c06d66646d7e5599e5fc 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: arm64 Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 292 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_arm64.deb Size: 139436 MD5sum: 8a974b2e7155993738f6eb6a43651bbc SHA1: c21b0eebce8b8df9bb8b202525deee6b055e2e36 SHA256: 72a0cfc59daf610447e626533a29b16ccccb01cfa5ebb7c3b97b54b9a5d9597c SHA512: a146df755313d257ebd6cf439b31e90ab0a3484dc7a6f64f2e4880f079d47c4bbbc1dee7fe054764f6c726d998aafb10e9ed215189863c1364709af23f579b3a 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: arm64 Version: 1.2-22-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1182 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1071954 MD5sum: ea6af6fadb5719663d0136ce57c0740a SHA1: 334f139690d2a46d0eed87f8edbb956a3bf9255a SHA256: f7f6b7788640508dc9ae17194b562fd92400f2bf44bf33a3b45453023d205aeb SHA512: 42a07cc9b898445e81f33d622793dc0abb46b62fc2f3fd165fe969569bf9459b62bd832047ee1543bfeff6694761e28e0e9a1e23c6e218ee5982c795165f89ad 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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Focuses on prospective surveillance of data streams, scanning for clusters with ongoing anomalies. Hypothesis testing is made possible by Monte Carlo simulation. Allévius (2018) . Package: r-cran-scar Architecture: arm64 Version: 0.2-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-gam, r-cran-mgcv, r-cran-scam Filename: pool/dists/resolute/main/r-cran-scar_0.2-2-1.ca2604.1_arm64.deb Size: 143146 MD5sum: a6913a2c155264cf4691c028be4b9598 SHA1: 82ffff157cf402d8e64673b29a48458e2c9b76c1 SHA256: 6f3ffa64c3c171378abdc9dc2e51b02211a0f9a87e72c19936a6e0bcb91affa2 SHA512: ed521b776755038e8319579e3c02f4a86e3a837380c7130ed40a31a8af47d57c2361e71ecbe4141aa29719f23568f9732f0d8817832eb7dace47921f171001fa Homepage: https://cran.r-project.org/package=scar Description: CRAN Package 'scar' (Shape-Constrained Additive Regression: a Maximum LikelihoodApproach) Computes the maximum likelihood estimator of the generalised additive and index regression with shape constraints. Each additive component function is assumed to obey one of the nine possible shape restrictions: linear, increasing, decreasing, convex, convex increasing, convex decreasing, concave, concave increasing, or concave decreasing. For details, see Chen and Samworth (2016) . Package: r-cran-scatterdensity Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 402 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppparallel, r-cran-pracma, r-cran-rcpparmadillo Suggests: r-cran-datavisualizations, r-cran-ggplot2, r-cran-ggextra, r-cran-plotly, r-cran-fcps, r-cran-paralleldist, r-cran-secr, r-cran-clusterr, r-cran-geometry Filename: pool/dists/resolute/main/r-cran-scatterdensity_0.1.1-1.ca2604.1_arm64.deb Size: 193376 MD5sum: 4f42422fdc6b6871e5c586cb86999ead SHA1: 8edb138ad0122335d2bcfee0492d75c23bb6a3ed SHA256: 262a39e83a86a19726a9c82c649d6718518b0119ff38b98612c86b723be1c6e2 SHA512: 10fb2b7723bd2050cbb14ebd76fa5aafb76455f03a7fa6d38004fc6d651051b5791c4ee4cc553161fa4944d712e84da309e706ec6f0c482d18f085041f3dde55 Homepage: https://cran.r-project.org/package=ScatterDensity Description: CRAN Package 'ScatterDensity' (Density Estimation and Visualization of 2D Scatter Plots) The user has the option to utilize the two-dimensional density estimation techniques called smoothed density published by Eilers and Goeman (2004) , and pareto density which was evaluated for univariate data by Thrun, Gehlert and Ultsch, 2020 . Moreover, it provides visualizations of the density estimation in the form of two-dimensional scatter plots in which the points are color-coded based on increasing density. Colors are defined by the one-dimensional clustering technique called 1D distribution cluster algorithm (DDCAL) published by Lux and Rinderle-Ma (2023) . Package: r-cran-scattermore Architecture: arm64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 577 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 332572 MD5sum: c1e45f2c0d18ce8224eee0463d22a416 SHA1: d47af86bd2879df32b87374c9fea7625c58af87b SHA256: 707444ec3870eff5e4e491564c7047c2c2a58bd817f432e301e5b3c91c81ddac SHA512: 6213fb809025b4d8a6c4ba4b4d0b2dcb7e2ecea2b5831df08ecf909161f800099ebdc2dd2b4af7db4f49c5160c57fcb0f38bd373af6f283e25ae64515ee8c0ed Homepage: https://cran.r-project.org/package=scattermore Description: CRAN Package 'scattermore' (Scatterplots with More Points) C-based conversion of large scatterplot data to rasters plus other operations such as data blurring or data alpha blending. Speeds up plotting of data with millions of points. Package: r-cran-scci Architecture: arm64 Version: 1.2-1.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 (>= 4.5.0), r-api-4.0, r-cran-rcpp Suggests: r-cran-pcalg, r-bioc-rgraphviz Filename: pool/dists/resolute/main/r-cran-scci_1.2-1.ca2604.1_arm64.deb Size: 58752 MD5sum: 59428077c1dc9db4fda925dce76f431c SHA1: 510f3c4779eac1a7f15280076ef30eff2a838e97 SHA256: 5b1db7d02db4ff20d84782fbf813f87dc032318d34138dc84b3d90c7b00be666 SHA512: 3a892457ab2863fca33a87d8225fe67548c752a7a124337100f4afdf15ca8e4e5f4563934c7d6bbe03f496928dd97f91427ff5751065928fd253c3c90e23d28f Homepage: https://cran.r-project.org/package=SCCI Description: CRAN Package 'SCCI' (Stochastic Complexity-Based Conditional Independence Test forDiscrete Data) An efficient implementation of SCCI using 'Rcpp'. SCCI is short for the Stochastic Complexity-based Conditional Independence criterium (Marx and Vreeken, 2019). SCCI is an asymptotically unbiased and L2 consistent estimator of (conditional) mutual information for discrete data. Package: r-cran-scclust Architecture: arm64 Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-distances Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-scclust_0.2.5-1.ca2604.1_arm64.deb Size: 100212 MD5sum: 5d929eed093c3881331793e159fdc942 SHA1: 408f22e7404857419b3c8ff1f90767b2d0d7ae26 SHA256: 451943e8537ae2e9bc449d2fb6c74f81c1a88c98bfb5e4d067f780588bf66171 SHA512: 46bc786b784f7ce79707f666b3a83493de31d6d58fdf6f56faebb34d2b700b2c9c079467b0f1ddaa0c878d6c3cc2160bd619cebb95c946ab49a272937bb19fe6 Homepage: https://cran.r-project.org/package=scclust Description: CRAN Package 'scclust' (Size-Constrained Clustering) Provides wrappers for 'scclust', a C library for computationally efficient size-constrained clustering with near-optimal performance. See for more information. Package: r-cran-sccore Architecture: arm64 Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1076 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-ggrepel, r-cran-igraph, r-cran-irlba, r-cran-magrittr, r-cran-matrix, r-cran-pbmcapply, r-cran-proc, r-cran-rcpp, r-cran-rlang, r-cran-scales, r-cran-tibble, r-cran-uwot, r-cran-withr, r-cran-rcpparmadillo, r-cran-rcppprogress, r-cran-rcppeigen Suggests: r-cran-ggrastr, r-cran-jsonlite, r-cran-philentropy, r-cran-rmumps, r-cran-seurat, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sccore_1.0.7-1.ca2604.1_arm64.deb Size: 842722 MD5sum: 57dc94a23d520656c73e6df24ff853fc SHA1: 426ce79b9107ddeac7c5e54a2e2f3d01e9f8923f SHA256: 62b3c897e57ef0eb8a9396768f508e53fe245146863f9a681c2e89e876af2fd9 SHA512: 0a43270442534823460b1664e40815af5d479920c79cafdcd11ed8c591fe5e8083a7f1c4755582bddf5756f8a9daae96886c1670c52ffe47005c26c2a5af963e Homepage: https://cran.r-project.org/package=sccore Description: CRAN Package 'sccore' (Core Utilities for Single-Cell RNA-Seq) Core utilities for single-cell RNA-seq data analysis. Contained within are utility functions for working with differential expression (DE) matrices and count matrices, a collection of functions for manipulating and plotting data via 'ggplot2', and functions to work with cell graphs and cell embeddings. Graph-based methods include embedding kNN cell graphs into a UMAP , collapsing vertices of each cluster in the graph, and propagating graph labels. Package: r-cran-scdha Architecture: arm64 Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3581 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_arm64.deb Size: 3417204 MD5sum: 55495e66a56a04cfbdaf0399b824d170 SHA1: a077460711ac793154d619a22ece73c94b7eebb5 SHA256: a04e9c7b7f4db28be07efd6b40c29d6e2831e35fd4ebf2831767a5093908a8ab SHA512: 36de58e5d90119a6fa9dcec5d6db40821501787c608ad5918b98b758bdb949f0b7843cfb401b34d0485d10ff2057246ece464c23db2218137f6f70f859aa7b19 Homepage: https://cran.r-project.org/package=scDHA Description: CRAN Package 'scDHA' (Single-Cell Decomposition using Hierarchical Autoencoder) Provides a fast and accurate pipeline for single-cell analyses. The 'scDHA' software package can perform clustering, dimension reduction and visualization, classification, and time-trajectory inference on single-cell data (Tran et.al. (2021) ). Package: r-cran-scellpam Architecture: arm64 Version: 1.4.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2338 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_arm64.deb Size: 554644 MD5sum: 1b1b3a810bc44621115a85a44f5ab3ef SHA1: 0ae55922d3e11bc5d5ed5805a265035bb46e71d2 SHA256: 008a4843ddd61cc9c432fb36f74e6400af0ba4c160c0c4a21404c6e42ce82ea0 SHA512: 71c37e4cbba5f234293852687bb453211118ea03ae0f47a3353ae2e7c7fc4c3e3c1b8aef4763cab4b5f2aa8bf74e5408d71b4e0fdf969277e34ae44f57d5dae7 Homepage: https://cran.r-project.org/package=scellpam Description: CRAN Package 'scellpam' (Applying Partitioning Around Medoids to Single Cell Data withHigh Number of Cells) PAM (Partitioning Around Medoids) algorithm application to samples of single cell sequencing techniques with a high number of cells (as many as the computer memory allows). The package uses a binary format to store matrices (either full, sparse or symmetric) in files written in the disk that can contain any data type (not just double) which allows its manipulation when memory is sufficient to load them as int or float, but not as double. The PAM implementation is done in parallel, using several/all the cores of the machine, if it has them. This package shares a great part of its code with packages 'jmatrix' and 'parallelpam' but their functionality is included here so there is no need to install them. Package: r-cran-scepter Architecture: arm64 Version: 0.2-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3992 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 3984870 MD5sum: 767234b770b40abc2ebc8f6581958daf SHA1: b3fc4e6990a1dd1b7418cb69ee28217330b78330 SHA256: 9e7ffa80690711e0ada4a4a4793ec145b1930b4a6245d050f5faf66223b4ded2 SHA512: 42e3450c0dc6444b6760c19fd9b13f18f94eab30f4dfe9d910514b442f87fc23dd35993e06cdd07d802e33916e8ab10750f2591faac69cb468dc0e0dfc40162f 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: arm64 Version: 0.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 39270 MD5sum: ec7cb54b10513d0be1994b64e5de37f4 SHA1: a9674854a6beca97c747458d715067932100778d SHA256: 82bd3a971e666d048b103596cff7366f925bc94d35b2d0e30b622bc3eb555a49 SHA512: b2e0b88b78047c19284b56862ebfed5524fd06831af6bec0d2cdf4995045468ffc43a7c71294ff36a65401174460127d7024c4bea8399db26ffb3bf352a30b8d 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: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 306 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_arm64.deb Size: 141972 MD5sum: 849998a6a8cc94b9eaa7cdb9b23043f9 SHA1: 4648bd41d4c3c38dba1b95cff4548a7b50784d8c SHA256: 7206da5146b0931ab8845d013263d5fbc021fbbceeb8104f7b644b046464a866 SHA512: 25a79ed4e981d83c1391bfd4f76bb2510889696b028a2dabc4b44fe7a74f9af3e3a8f7d2a4c550bdde293a7d6c71cbe780ab78b29ae581190d4d8daa034de5d5 Homepage: https://cran.r-project.org/package=SChangeBlock Description: CRAN Package 'SChangeBlock' (Spatial Structural Change Detection by an Analysis ofVariability Between Blocks of Observations) Provides methods to detect structural changes in time series or random fields (spatial data). Focus is on the detection of abrupt changes or trends in independent data, but the package also provides a function to de-correlate data with dependence. The functions are based on the test suggested in Schmidt (2024) and the work in Görz and Fried (2025) . Package: r-cran-scinsight Architecture: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 337 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 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_arm64.deb Size: 142964 MD5sum: e03bc2d42a0a9a53d33078f1358c37ca SHA1: 4004493bd6d4a121223a066193092fe883a9cd80 SHA256: cf3ccdf1b5612523407beba0a54a1f93a6c3f86707f83cf91b56c48bc5f3f4ed SHA512: ffa642079d920ba1699b0f41084ec6d5bc1358c753c5681840491203406c92abbdabd2eb9835ef27e883e142bbca22c92b4315480a5c7604714ef633816c6d19 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: arm64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 412 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_arm64.deb Size: 245380 MD5sum: 159333cd20a37636ae227cf69f4ec86f SHA1: 14c83e99206f697016ad00a621a93c582a03336d SHA256: cb9d2b33c2937e2f0352cd281ee94ec7de7786fc98d4aaa9d374af86c0a89cc8 SHA512: afc24349d3ae9e3d7d6fa240ac580fc8719a05492700381ce4a9f9d78ce76693c9bffc43d8118260c896954f67bb7f1cd7aa8e89a1a9d9133fa6db2724b72ecf Homepage: https://cran.r-project.org/package=scistreer Description: CRAN Package 'scistreer' (Maximum-Likelihood Perfect Phylogeny Inference at Scale) Fast maximum-likelihood phylogeny inference from noisy single-cell data using the 'ScisTree' algorithm by Yufeng Wu (2019) . 'scistreer' provides an 'R' interface and improves speed via 'Rcpp' and 'RcppParallel', making the method applicable to massive single-cell datasets (>10,000 cells). Package: r-cran-scitd Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1251 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1067002 MD5sum: 477a8aa2400734f630c25e277d007629 SHA1: 0429801cddbd3cf4d748bf8633fbd777651c1b71 SHA256: 2686c208da2b393eb5605f6eaeb6db587fee7afe1ac3b46a9a54654550079682 SHA512: 63202a17a8739dee00c3b413fddd72eb1f869aee94944a43730784683160a2b1da9f7b27421c4122abf2c07404a6b9bfcd7620923834b0abde30e9256f07703d 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: arm64 Version: 1.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 805 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 595790 MD5sum: 682de8783fbcc838ec3d3da07d0f2675 SHA1: 5c37b6df961300728c7072508e85bb1a72b45541 SHA256: e99618a5dfc4d235f04c5824f0041d3bed83bb30cdd4b5886d629365ea315270 SHA512: 6b96decf225af7d4b53f300921a6a24acfbe30677f6940a8718747674dc0f8b67e6fb080f3256f46585d0717c1ce103657702e713f77d2f6fa65416587672bf4 Homepage: https://cran.r-project.org/package=scoper Description: CRAN Package 'scoper' (Spectral Clustering-Based Method for Identifying B Cell Clones) Provides a computational framework for identification of B cell clones from Adaptive Immune Receptor Repertoire sequencing (AIRR-Seq) data. Three main functions are included (identicalClones, hierarchicalClones, and spectralClones) that perform clustering among sequences of BCRs/IGs (B cell receptors/immunoglobulins) which share the same V gene, J gene and junction length. Nouri N and Kleinstein SH (2018) . Nouri N and Kleinstein SH (2019) . Gupta NT, et al. (2017) . Package: r-cran-scoredec Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 352 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 155716 MD5sum: 66d3acfd2e96e3525e6e62c86cbfa432 SHA1: 5b58ca19480b67bc5cb1f5c57d7827bd4770b902 SHA256: a34bfb1be8cf0bd1cecb3ce96a4d45c9f0afdb35b86cecb8868893b9546f9afb SHA512: 29e100fba81f776975e104461e1c9fff606011b9df1fd23f2432cae4d905b1332e909e999d75fb63dfde598dc895cafc1089e134807dfb43df7b0e1e1d47d202 Homepage: https://cran.r-project.org/package=scoredec Description: CRAN Package 'scoredec' (S-Core Graph Decomposition) S-Core Graph Decomposition algorithm for graphs. This is a method for decomposition of a weighted graph, as proposed by Eidsaa and Almaas (2013) . The high speed and the low memory usage make it suitable for large graphs. Package: r-cran-scoreeb Architecture: arm64 Version: 0.1.1-1.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_arm64.deb Size: 58588 MD5sum: c3f0851300b7756fa579d2175514c4d8 SHA1: a1b1f77e021015b985c1a791de66e38668316d7b SHA256: 9a08f2a1dde1b1abc3a33f665823bfc27ad7078917d3bcc9f2dd08c4100ed10a SHA512: 01c8312e613e247c7f3e7ef241c9307adabead3c5b7eb04cc8edd055b403b23fc0dbda033b6f30dd0090e2796b98b939b3e011a408ecc8b2cfbcea07e319c13c Homepage: https://cran.r-project.org/package=ScoreEB Description: CRAN Package 'ScoreEB' (Score Test Integrated with Empirical Bayes for Association Study) Perform association test within linear mixed model framework using score test integrated with Empirical Bayes for genome-wide association study. Firstly, score test was conducted for each marker under linear mixed model framework, taking into account the genetic relatedness and population structure. And then all the potentially associated markers were selected with a less stringent criterion. Finally, all the selected markers were placed into a multi-locus model to identify the true quantitative trait nucleotide. Package: r-cran-scorematchingad Architecture: arm64 Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13190 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_arm64.deb Size: 1722904 MD5sum: f21173a61eca86443e9a404f5c945f67 SHA1: c67b1bcf9d18c7e218ee14e55c70822491dcf039 SHA256: 6b70770b3ef57d1395a0201f8181a57369fcdb2d2f7256d6f078e99705fc933a SHA512: e69eb964e328a5b2c5450996f0b529dc984efc9402146f9669d930c6e2c38b018ca3bae807475e008dae4022b4db9746fb79f36e3632f2d1d63bc50de85468ff Homepage: https://cran.r-project.org/package=scorematchingad Description: CRAN Package 'scorematchingad' (Score Matching Estimation by Automatic Differentiation) Hyvärinen's score matching (Hyvärinen, 2005) is a useful estimation technique when the normalising constant for a probability distribution is difficult to compute. This package implements score matching estimators using automatic differentiation in the 'CppAD' library and is designed for quickly implementing score matching estimators for new models. Also available is general robustification (Windham, 1995) . Already in the package are estimators for directional distributions (Mardia, Kent and Laha, 2016) and the flexible Polynomially-Tilted Pairwise Interaction model for compositional data. The latter estimators perform well when there are zeros in the compositions (Scealy and Wood, 2023) , even many zeros (Scealy, Hingee, Kent, and Wood, 2024) . A partial interface to CppAD's ADFun objects is also available. Package: r-cran-scorepeak Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 575 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 180192 MD5sum: 53bbca12462cfe3051ab8d34110eb492 SHA1: 53e821bd347878d44dffce55c5d3135b1c385c4b SHA256: 264b2af2a007b8a1a1f6a57f0c307afc08691544d2907dabc738ba207de29ad7 SHA512: d8238eb4bc84af7b236ac8125e71f8e09729c41e1fdacc00548c5f9af45c232b5985f1f8d43de5b625debee72c1055d831f6c1205b7da104b70be8032141ce50 Homepage: https://cran.r-project.org/package=scorepeak Description: CRAN Package 'scorepeak' (Peak Functions for Peak Detection in Univariate Time Series) Provides peak functions, which enable us to detect peaks in time series. The methods implemented in this package are based on Girish Keshav Palshikar (2009) . Package: r-cran-scoringrules Architecture: arm64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2461 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 2054112 MD5sum: 69769ea5b09360bbb5e85619cff07951 SHA1: ec0545ca4ab6449f20a2592248cc43a0374d556a SHA256: c89ae5b5a76b9c366bdd95edaca80ab41b8fd1f1a42ebf9db94cd7017b9b0f12 SHA512: 16d24ffc1330091716b0eea5ae80fbba24082139e96c0045e78883ca6009a667e222eff876d4fdddd8dd05749dc18121c907d21c2d61b3ffb385ed89d0ac95f5 Homepage: https://cran.r-project.org/package=scoringRules Description: CRAN Package 'scoringRules' (Scoring Rules for Parametric and Simulated DistributionForecasts) Dictionary-like reference for computing scoring rules in a wide range of situations. Covers both parametric forecast distributions (such as mixtures of Gaussians) and distributions generated via simulation. Further details can be found in the package vignettes , . Package: r-cran-scornet Architecture: arm64 Version: 0.1.1-1.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_arm64.deb Size: 72192 MD5sum: 3e6fb97655621d0cb81bf7cf4070d8ae SHA1: a4239b159c703c6a8887351c4e2770af520cb79d SHA256: cd7711e3e4297b80b4ba76f202e381d838542e24d95d97d00c6df3234e41a357 SHA512: d2d609accc1a6810a1d03bee42741e6dce199d638b13a76f76437264f26bc1dd7469b5ef28e5990227790b0734e0d5ec2e33d7874b2f53197896d9ca2cdb3345 Homepage: https://cran.r-project.org/package=SCORNET Description: CRAN Package 'SCORNET' (Semi-Supervised Calibration of Risk with Noisy Event Times) A consistent, semi-supervised, non-parametric survival curve estimator optimized for efficient use of Electronic Health Record (EHR) data with a limited number of current status labels. See van der Laan and Robins (1997) . Package: r-cran-scout Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-glasso Suggests: r-cran-lars Filename: pool/dists/resolute/main/r-cran-scout_1.0.4-1.ca2604.1_arm64.deb Size: 71332 MD5sum: bb4b5f3acc2341eecab9bba519e2d505 SHA1: 359b9c787a8724ed80f145169d0e05535a513ab2 SHA256: 0fc37b1ef6352a6622475a98592c38bf2a37d2cfb44528b2d4ef33e85c2e823d SHA512: b1f35c1763947fcfe668a0a74065bbb38dc18af4d39acfec7818eccaeee3c034d6399be6bfcbf8e7457e1bf976c83bb2c25fa96a04632f4476ffc5f99213bf6c Homepage: https://cran.r-project.org/package=scout Description: CRAN Package 'scout' (Implements the Scout Method for Covariance-RegularizedRegression) Implements the Scout method for regression, described in "Covariance-regularized regression and classification for high-dimensional problems", by Witten and Tibshirani (2008), Journal of the Royal Statistical Society, Series B 71(3): 615-636. Package: r-cran-scquantum Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-scquantum_1.0.0-1.ca2604.1_arm64.deb Size: 37552 MD5sum: c39d818ffa08139a4b3106cd1e96babe SHA1: 864907447882fd4c5c613702d31e65d26c035710 SHA256: 1203cf60acf93e16b9c0fa4b0bdb14951429b5d9cf2a7f4aa787c2a7e4b46475 SHA512: 8f86e17d8fb51e0496861805398a05e81290ab92ab6f5f4cd2e26c738465b1e0b14e79ea34a4d8816682672b361e792f7fad0267352e96e1f6e21a7718519522 Homepage: https://cran.r-project.org/package=scquantum Description: CRAN Package 'scquantum' (Estimate Ploidy and Absolute Copy Number from Single CellSequencing) Given bincount data from single-cell copy number profiling (segmented or unsegmented), estimates ploidy, and uses the ploidy estimate to scale the data to absolute copy numbers. Uses the modular quantogram proposed by Kendall (1986) , modified by weighting segments according to confidence, and quantifying confidence in the estimate using a theoretical quantogram. Includes optional fused-lasso segmentation with the algorithm in Johnson (2013) , using the implementation from glmgen by Arnold, Sadhanala, and Tibshirani. Package: r-cran-scregclust Architecture: arm64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 727 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 507292 MD5sum: b7027af66971d5b8c618965be5725665 SHA1: 0294cb2de5071c8d0645180552bea5c81ea594ab SHA256: 69ae3b6cc4ea67b27c9f76f278a7b4cd325bbce8d331d2259491b60558305850 SHA512: 03583362a73400fc6898ee9768bf21438b88027b68b5f7c72904020ce613b4611ed267c3d1f047e0966054aa74e0579e5c5322d872afb56d2a43ce813d6fd05e Homepage: https://cran.r-project.org/package=scregclust Description: CRAN Package 'scregclust' (Reconstructing the Regulatory Programs of Target Genes inscRNA-Seq Data) Implementation of the scregclust algorithm described in Larsson, Held, et al. (2024) which reconstructs regulatory programs of target genes in scRNA-seq data. Target genes are clustered into modules and each module is associated with a linear model describing the regulatory program. Package: r-cran-scrm Architecture: arm64 Version: 1.7.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 440 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_arm64.deb Size: 150090 MD5sum: 2fc6a56ab9072a177f4a4346d9532173 SHA1: 27e281e0334f570dfdb2b3fc15603131140dba67 SHA256: 356f81508b4cf01df0d23f679cb7e90d87eca3bdfebab5ac00dd5b447c050fc7 SHA512: 005b5ab13ebb3b01eb3221837779527cb3d27d4b4a01b5c9de31f1ceab9c3a1cc8a68a41978d542c09eef98b94d87f2913682c01eabc61a5ba0cf241721322c6 Homepage: https://cran.r-project.org/package=scrm Description: CRAN Package 'scrm' (Simulating the Evolution of Biological Sequences) A coalescent simulator that allows the rapid simulation of biological sequences under neutral models of evolution, see Staab et al. (2015) . Different to other coalescent based simulations, it has an optional approximation parameter that allows for high accuracy while maintaining a linear run time cost for long sequences. It is optimized for simulating massive data sets as produced by Next- Generation Sequencing technologies for up to several thousand sequences. Package: r-cran-scrypt Architecture: arm64 Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 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_arm64.deb Size: 50016 MD5sum: cb664ab53bc25fedc4dfc7fcd27bed58 SHA1: c32911b881614fd15d6042f4594224ee2c9162f8 SHA256: 6e291749febbea6c955d409d476b167def49315dcedb128ea2284eeb741891b6 SHA512: 6b24b956509a483655c4e9be34eb9803bad0170dd594ce9df4159b4a1187dd65ebd4544d40ec841579aa2fdb57c943191a96ca18f63b76d22579dddf23086c33 Homepage: https://cran.r-project.org/package=scrypt Description: CRAN Package 'scrypt' (Key Derivation Functions for R Based on Scrypt) Functions for working with the scrypt key derivation functions originally described by Colin Percival and in Percival and Josefsson (2016) . Scrypt is a password-based key derivation function created by Colin Percival. The algorithm was specifically designed to make it costly to perform large-scale custom hardware attacks by requiring large amounts of memory. Package: r-cran-scs Architecture: arm64 Version: 3.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1574 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), 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_arm64.deb Size: 1282852 MD5sum: 81630d44f8c365ffefe84bc29ced09c0 SHA1: 88aeb3825379246ad07ff4a1a8d94a9129caa56f SHA256: 703d8b32044f83a621a3ad09984508a3048f77952a7fdf29dcd8f616fb794c13 SHA512: 8c2bd806a00fcf4d585e690e2efae68c28875526b07ec2192d0229139d6256f3219b961d619f2483e644adb20fe76e14ad5727a5f1c81cbf4c1c79c6bc2fda50 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' (). Package: r-cran-sctransform Architecture: arm64 Version: 0.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 776 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-mass, r-cran-matrix, r-cran-future.apply, r-cran-future, r-cran-parallelly, r-cran-ggplot2, r-cran-reshape2, r-cran-rlang, r-cran-gridextra, r-cran-matrixstats, r-cran-rcpparmadillo, r-cran-rcpp Suggests: r-cran-irlba, r-cran-testthat, r-cran-knitr Filename: pool/dists/resolute/main/r-cran-sctransform_0.4.3-1.ca2604.1_arm64.deb Size: 501852 MD5sum: 79e7827cb2d24ed166ff89f26c9a2493 SHA1: 7bb9b9d75f254715a23faa00ba1c0e7629cb8825 SHA256: 31b1a8972a2ad23d51dbd8d7d5b09b2ad673a26786639d4467264095179eeaa5 SHA512: 1b80d439b479c07c16e975aaf44664a0ed7d91922f4679ba446d481864c0a07253250c9270a524c092feef92c20ebd6b84b6db113da4de777118fef69facd329 Homepage: https://cran.r-project.org/package=sctransform Description: CRAN Package 'sctransform' (Variance Stabilizing Transformations for Single Cell UMI Data) A normalization method for single-cell UMI count data using a variance stabilizing transformation. The transformation is based on a negative binomial regression model with regularized parameters. As part of the same regression framework, this package also provides functions for batch correction, and data correction. See Hafemeister and Satija (2019) , and Choudhary and Satija (2022) for more details. Package: r-cran-scuba Architecture: arm64 Version: 1.11-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1086 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_arm64.deb Size: 682776 MD5sum: 510d4c36cd9ebc635c5c594050839001 SHA1: 895d9f02635060e1516546f22cdc708e061c3204 SHA256: 3f11688f5d0f6535c443f20354edf8d7242fb08a07c36d0644def8eb7357e7d5 SHA512: 7c95b4d63e85f8c3bb76f950e106297ec5ad390eff671bd155bdb23085d8cb41ed3dcebd0c287a42a716b17dacfc465e66f6e1304605cdc893df774092228341 Homepage: https://cran.r-project.org/package=scuba Description: CRAN Package 'scuba' (Diving Calculations and Decompression Models) Code for describing and manipulating scuba diving profiles (depth-time curves) and decompression models, for calculating the predictions of decompression models, for calculating maximum no-decompression time and decompression tables, and for performing mixed gas calculations. Package: r-cran-sd2r Architecture: arm64 Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 25811 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_arm64.deb Size: 6632058 MD5sum: baa22e7b5430d021a1e70e2c06fdc3aa SHA1: 164c3d580b59700edb05256f2c84b1603c83ea1e SHA256: 1c957516eb21b457b67f8c9dcdd9a973d046c61e89a35c6a0b524b850aa3d5f6 SHA512: 56801abd9e24e81de8bf65220635202c5a6e2490550d72f64989a6168937dabdd357a049e03ffd79d26b712c4b80a147d85a60ed7a15cce2f4eb57ea0e397383 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. Package: r-cran-sdchierarchies Architecture: arm64 Version: 0.23.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 553 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-shinythemes, r-cran-shiny, r-cran-shinyjs, r-cran-shinytree, r-cran-jsonlite, r-cran-rlang, r-cran-data.table, r-cran-cli, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sdchierarchies_0.23.1-1.ca2604.1_arm64.deb Size: 236394 MD5sum: 4e148f9e9c7b6a730a711880816a9580 SHA1: f2d5484547705e71519241f05f790e4f17ceab2e SHA256: 07122aef64a3fd1eef36a08ee3f700a8b8ac85ea9958706b70a901c53cb88bae SHA512: a2d220f5a2ebbc2b18cecb81633b62f3599f89031c2c41ca9501c09cfc7610edd0b690b928c86f3d76e892690a1f3b56bdca8df3c2e7d3ff39167d62f124685f Homepage: https://cran.r-project.org/package=sdcHierarchies Description: CRAN Package 'sdcHierarchies' (Create and (Interactively) Modify Nested Hierarchies) Provides functionality to generate, (interactively) modify (by adding, removing and renaming nodes) and convert nested hierarchies between different formats. These tree like structures can be used to define for example complex hierarchical tables used for statistical disclosure control. Package: r-cran-sdcmicro Architecture: arm64 Version: 5.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3347 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_arm64.deb Size: 1653578 MD5sum: f9ea1f4315f1398101d9bda0ff43a39f SHA1: 50defa1e0c8c2188805731497d2022c5e5505a9e SHA256: e30308c2564133a3ed0e19b102a80ddb1c0e6d5d5718295e86458f85208ef7d1 SHA512: a8bfd3b66deb577b0de9972b0df1620ec2e13e3bcd51bae8848c21b51148b855f423e73b2109eaf3654ce6b3e4f90ec5b3d63852dfbef595f95ecd7bd236f4d9 Homepage: https://cran.r-project.org/package=sdcMicro Description: CRAN Package 'sdcMicro' (Statistical Disclosure Control Methods for Anonymization of Dataand Risk Estimation) Data from statistical agencies and other institutions are mostly confidential. This package, introduced in Templ, Kowarik and Meindl (2017) , can be used for the generation of anonymized (micro)data, i.e. for the creation of public- and scientific-use files. The theoretical basis for the methods implemented can be found in Templ (2017) . Various risk estimation and anonymization methods are included. Note that the package includes a graphical user interface published in Meindl and Templ (2019) that allows to use various methods of this package. Package: r-cran-sdcspatial Architecture: arm64 Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2161 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-raster Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-sp, r-cran-sf, r-cran-fnn Filename: pool/dists/resolute/main/r-cran-sdcspatial_0.6.1-1.ca2604.1_arm64.deb Size: 1966488 MD5sum: 275143a4837b62cfc091f62ec9cfa49a SHA1: 77c59b021ca65aec93e1567cbe28345cfbc0c90c SHA256: 081988f69bf3ef451e747b9daf545426f5adee0aa5085b98cc9a2baa02a2a674 SHA512: 951092bfe29ed220a27a8bb4de9880ad8af1f743879b24937ec9a6b288a7019896a4b2d4f60946d7e3a0a31b7eea506e5619902f2f5a93e41805142dfc643574 Homepage: https://cran.r-project.org/package=sdcSpatial Description: CRAN Package 'sdcSpatial' (Statistical Disclosure Control for Spatial Data) Privacy protected raster maps can be created from spatial point data. Protection methods include smoothing of dichotomous variables by de Jonge and de Wolf (2016) , continuous variables by de Wolf and de Jonge (2018) , suppressing revealing values and a generalization of the quad tree method by Suñé, Rovira, Ibáñez and Farré (2017) . 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(2012) are covered in this package. Package: r-cran-sde Architecture: arm64 Version: 2.0.21-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 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_arm64.deb Size: 457468 MD5sum: 74f7e0aeca927a4bd9e3514ea0fe1b8d SHA1: ece163be8014ebc8a5207aa037e281e6190bdfd8 SHA256: 0a4fafc744d556ecca2caa63fafc5e7b125e631ce134375a77c1f99b1e475384 SHA512: 0d6338918e5ba3386eec6be33c7d61c05640a4afebcbebcf859c20b16be12591a1cd0085508e81ff6002b8ee2ddc74a14b4430afc6476d12b8e7b178da5427ac Homepage: https://cran.r-project.org/package=sde Description: CRAN Package 'sde' (Simulation and Inference for Stochastic Differential Equations) Description: Provides functions for simulation and inference for stochastic differential equations (SDEs). It accompanies the book "Simulation and Inference for Stochastic Differential Equations: With R Examples" (Iacus, 2008, Springer; ISBN: 978-0-387-75838-1). Package: r-cran-sdetorus Architecture: arm64 Version: 0.1.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1847 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1324396 MD5sum: 861b035f17559899e5b275ac1d15bfb4 SHA1: a4a57010df9db4b7b7d630d2f0eeece15bc8d24e SHA256: 9f818b1a998b0c3dfbdf794f5f32d427b233230b72b9d7ae787211d6d149c014 SHA512: 1284a5ead1dca0099fa6dd86324c2101ee44b204386339a93172a49464bcd77c083e4fffde29cb6da230bb8b7c21fcb6aaae9aa8cf0c570c87ca517a3aa5fbe9 Homepage: https://cran.r-project.org/package=sdetorus Description: CRAN Package 'sdetorus' (Statistical Tools for Toroidal Diffusions) Implementation of statistical methods for the estimation of toroidal diffusions. Several diffusive models are provided, most of them belonging to the Langevin family of diffusions on the torus. Specifically, the wrapped normal and von Mises processes are included, which can be seen as toroidal analogues of the Ornstein-Uhlenbeck diffusion. A collection of methods for approximate maximum likelihood estimation, organized in four blocks, is given: (i) based on the exact transition probability density, obtained as the numerical solution to the Fokker-Plank equation; (ii) based on wrapped pseudo-likelihoods; (iii) based on specific analytic approximations by wrapped processes; (iv) based on maximum likelihood of the stationary densities. The package allows the replicability of the results in García-Portugués et al. (2019) . Package: r-cran-sdmtmb Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5611 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_arm64.deb Size: 2146136 MD5sum: a84c7adf78f6db510df704255d1c00ba SHA1: f4e12714b5caa70b74deab13bbf864680efa46b0 SHA256: a026da8f5996b1fc03e8fbdf44e4575e9b83bb79ce1c8216eb2d34c63817df81 SHA512: 1228a60a9251bba73603f72e0ca78bbe385c034a12614d791ee19bc39f50e713fd619848e2f5a3d27fe152d06a9b9f05312db71b9804b69d83ea1d7e80b5a2a6 Homepage: https://cran.r-project.org/package=sdmTMB Description: CRAN Package 'sdmTMB' (Spatial and Spatiotemporal SPDE-Based GLMMs with 'TMB') Implements spatial and spatiotemporal GLMMs (Generalized Linear Mixed Effect Models) using 'TMB', 'fmesher', and the SPDE (Stochastic Partial Differential Equation) Gaussian Markov random field approximation to Gaussian random fields. One common application is for spatially explicit species distribution models (SDMs). See Anderson et al. (2025) . Package: r-cran-sdmtune Architecture: arm64 Version: 1.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2869 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1726364 MD5sum: 9f3f4a4695d95d975f9a9a9b0fdb731a SHA1: 4cacb72fe2311a8244ce0b3ad435e3ab25699d4b SHA256: 88c2b5212aeedab3034a46e163bece8a1fefebb6bf5a855867125d2c54178a2b SHA512: 2d7323969a786413d99c7c44b91bff93b3ea65b7fa207821c540c52656ccc6eaeaa83aaa9d6f6de1a844a4d33cedbaa1cd44d09cb00845a4cdb4acf6098d5845 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: arm64 Version: 1.1-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 193 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 97222 MD5sum: 70c0041fef60bc1d6c5d11ce0fff432e SHA1: e7f3e584d302356498b91ef251c0cb9e06e64971 SHA256: f95c692e1545d1ed82327cb2027e2d88e7397ef32883daaeee0d4c9c825d8ff7 SHA512: fc9335cd77256c2ec4134eeb807f0f07e76c892a6086c16fd89bcf8e4e2066988bb36f85776b6e81239de3677987490f57ff9c7e72225b7500cd43e38e4401a2 Homepage: https://cran.r-project.org/package=sdprisk Description: CRAN Package 'sdprisk' (Measures of Risk for the Compound Poisson Risk Process withDiffusion) Based on the compound Poisson risk process that is perturbed by a Brownian motion, saddlepoint approximations to some measures of risk are provided. Various approximation methods for the probability of ruin are also included. Furthermore, exact values of both the risk measures as well as the probability of ruin are available if the individual claims follow a hypo-exponential distribution (i. e., if it can be represented as a sum of independent exponentially distributed random variables with different rate parameters). For more details see Gatto and Baumgartner (2014) . Package: r-cran-sdrt Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, 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_arm64.deb Size: 65278 MD5sum: d66335b6fb631a2264f1eef828f85a1a SHA1: d551d09cd7074676bd1a2540d0279016eb45b857 SHA256: 25a8647082551a09cfcc6dc42dbff3191b72e30750d2a3e51d61b612ed5875f3 SHA512: e423a35a176fccfc367607880b3edd69e823d4199021a97126af7b1acc239616432135ec14f7d252b982f24ff48356b2b9863fccff9498c145c989407e025730 Homepage: https://cran.r-project.org/package=sdrt Description: CRAN Package 'sdrt' (Estimating the Sufficient Dimension Reduction Subspaces in TimeSeries) The sdrt() function is designed for estimating subspaces for Sufficient Dimension Reduction (SDR) in time series, with a specific focus on the Time Series Central Mean subspace (TS-CMS). 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Package: r-cran-sdsfun Architecture: arm64 Version: 0.8.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 938 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 378514 MD5sum: d7ca3df6b0689ac4f2ec336dfb9b74d3 SHA1: d654ce877f44a3af7af9e4b9a32e127aa4dcfb6a SHA256: a613d36f5022244aefb609e63226d9e932b519c6ea1710699734f2c95d5d621d SHA512: a447924c4bf9571ad83d73099764eb16f5095e4ede4cdb5e5423133f88dfb81ce44fcef59adcc466ef42f2b638c442c5b4987bab1720d322e513a57acd28d53e 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. 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This package also has graphics capabilities for representing seasonal data, including boxplots for seasonal parameters, and bars for summed normals. There are many specific functions related to climatology, including precipitation normals, temperature normals, cumulative precipitation departures and precipitation interarrivals. However, this package is designed to represent any time-varying parameter with a discernible seasonal signal, such as found in hydrology and ecology. Package: r-cran-secr Architecture: arm64 Version: 5.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4169 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 3386108 MD5sum: d70ca0a088e60dbaa7f868f604ed06ea SHA1: 3bbfb8778b43289e5dc6a5f9b04c42294c9dd2ae SHA256: 00ab4bd1d70124eca14897d9d24a2a745202c9d8f73b59e8cd4252ae85eddce0 SHA512: bce2dca72adec816b1f7da4323b9491e73e2186b4826245779216a941c91e367e1d4544fc367b79863fe40c9b1d57d2994d6c2834c4e65bada35c96f9b081601 Homepage: https://cran.r-project.org/package=secr Description: CRAN Package 'secr' (Spatially Explicit Capture-Recapture) Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection. Package: r-cran-secrdesign Architecture: arm64 Version: 2.10.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 621 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), libstdc++6 (>= 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_arm64.deb Size: 449078 MD5sum: 3be6b69bc80e11c3c4ef10b8d68123ae SHA1: 8410774044c59fc2496e1fd0a70bda8e489e2e57 SHA256: fce0b2faa85c38d5934ce66253316741e6a2a733a8e6ccc3a12a26bd453ef13d SHA512: e0f3a2ba3d8b8dd8c81ba558487e2dbd81120d8b426a9af86b1513e4d9720d8a70feefd8df02d8fa5b8cd9b78d103b7ea6f6cef5c9617a8cd7cee25fb6396d88 Homepage: https://cran.r-project.org/package=secrdesign Description: CRAN Package 'secrdesign' (Sampling Design for Spatially Explicit Capture-Recapture) Tools for designing spatially explicit capture-recapture studies of animal populations. This is primarily a simulation manager for package 'secr'. Extensions in version 2.5.0 include costing and evaluation of detector spacing. Package: r-cran-secretbase Architecture: arm64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 162 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-secretbase_1.2.2-1.ca2604.1_arm64.deb Size: 74894 MD5sum: 614421d3aefe0730b3fcaed206a97241 SHA1: c2b68f6bdd9e552a29b366f6ca49a7b56f711d3b SHA256: 99fffb41be40308cb6e40ee015251eef4fe7862ac789566ac4143b55e7b8614e SHA512: fd14e7b68e593c00b00b68e946df94c3f7cf60baa988e06414217cb98eb811ffc0371454e11be260804a8ba9c48e25cf988d96262ee9c2f3e97243bfc1df41f8 Homepage: https://cran.r-project.org/package=secretbase Description: CRAN Package 'secretbase' (Cryptographic Hash Functions and Data Encoding) Fast and memory-efficient streaming hash functions, binary/text encoding and serialization. Hashes strings and raw vectors directly. Stream hashes files which can be larger than memory, as well as in-memory objects through R's serialization mechanism. Implements the SHA-256, SHA-3 and 'Keccak' cryptographic hash functions, SHAKE256 extendable-output function (XOF), 'SipHash' pseudo-random function, base64 and base58 encoding, 'CBOR' and 'JSON' serialization. Package: r-cran-secrfunc Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 576 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 154274 MD5sum: 618aee4594c7bc1a64fb538a4444427d SHA1: 26a14ae305f1015e7d431fad51e392f2661dcd58 SHA256: 200f3f36e00ecc1252bbd9096ec9390e0c934330d2fdeb9323f7622b3d440af7 SHA512: 392ffe587efffdea21e214d4eafab43fe0b1272371cb531e9ec7ebd2616d814cca6edfb94172284859a9c1353107c3ed91ed9db885583f5a57e004ce5fcc5be7 Homepage: https://cran.r-project.org/package=secrfunc Description: CRAN Package 'secrfunc' (Helper Functions for Package 'secr') Functions are provided for internal use by the spatial capture-recapture package 'secr' (from version 5.4.0). The idea is to speed up the installation of 'secr', and possibly reduce its size. Initially the functions are those for area and transect search that use numerical integration code from 'RcppNumerical' and 'RcppEigen'. The functions are not intended to be user-friendly and require considerable preprocessing of data. Package: r-cran-secsse Architecture: arm64 Version: 3.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1430 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-ddd, r-cran-ape, r-cran-geiger, r-cran-rcpp, r-cran-rcppparallel, r-cran-ggplot2, r-cran-tibble, r-cran-rlang, r-cran-treestats, r-cran-pracma, r-cran-bh Suggests: r-cran-diversitree, r-cran-phytools, r-cran-testthat, r-cran-subplex, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-secsse_3.7.0-1.ca2604.1_arm64.deb Size: 516482 MD5sum: f6ee73596aa6079dfc7a85b13324ccba SHA1: 7cb2201654048dc0f8bbc46b45d6f8ce378adf38 SHA256: f56d7d4f1a97dc0e6c0343dcb316bb2d8be813251034c581158a40a51d3d307f SHA512: 69f528fa0f95da5848d99eed985ec139747b70707be87e841ce99819f8b4783b7d2d42b588580ae36e4c2a313cf2e8914054272f23928e25577d91ba49be73e8 Homepage: https://cran.r-project.org/package=secsse Description: CRAN Package 'secsse' (Several Examined and Concealed States-Dependent Speciation andExtinction) Simultaneously infers state-dependent diversification across two or more states of a single or multiple traits while accounting for the role of a possible concealed trait. See Herrera-Alsina et al. (2019) . Package: r-cran-seededlda Architecture: arm64 Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3791 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_arm64.deb Size: 3345214 MD5sum: b5a2f772e421ed0cd09b49510b05706a SHA1: fcaf154389f0148c9380d5adb81998dd68491ca5 SHA256: 5eac13562b1ea347e002407e2c5bef51ee402ad5d0010aa2e9ae112d39eb9dda SHA512: 88c2265b6d8e506dec0658b84bab062f62edd572d32856658b61da749f4f482547a8c0676641adeb099a26558450572c60dad0a0103038507527935054572c44 Homepage: https://cran.r-project.org/package=seededlda Description: CRAN Package 'seededlda' (Seeded Sequential LDA for Topic Modeling) Seeded Sequential LDA can classify sentences of texts into pre-define topics with a small number of seed words (Watanabe & Baturo, 2023) . Implements Seeded LDA (Lu et al., 2010) and Sequential LDA (Du et al., 2012) with the distributed LDA algorithm (Newman, et al., 2009) for parallel computing. Package: r-cran-seerabomb Architecture: arm64 Version: 2019.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1223 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 805948 MD5sum: 10784d15f52fa567ce50c8e6391d5870 SHA1: 7d3a1fd1b08a395df3e8652e0a1df8568b8de5e2 SHA256: 7f26ad5575ca0833254440ac94945e9144f5ab846a39720cd7ba36df04b9d15d SHA512: 8533435c666457d49ea1cfb231e598265d1d4dcd88bf4d217615bddcd0d7fb1f69a4a3cc371ace327d7041374ba5535899d51e4d2217a3aed9745cad5ab7186e Homepage: https://cran.r-project.org/package=SEERaBomb Description: CRAN Package 'SEERaBomb' (SEER and Atomic Bomb Survivor Data Analysis Tools) Creates SEER (Surveillance, Epidemiology and End Results) and A-bomb data binaries from ASCII sources and provides tools for estimating SEER second cancer risks. Methods are described in . Package: r-cran-segclust2d Architecture: arm64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1534 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_arm64.deb Size: 899244 MD5sum: 47856348435ef5535d1742700d4d032d SHA1: 0e85c053afb222400968d9a26e59ff60c0fb2dd5 SHA256: 3c41c008168998fffc2f39c36900f018023f4ca761437246edb84cb657b8b242 SHA512: be4f684568b29b8268a02d575c60393a6d41a7742bdf3af2ac80a3a932d5ff2ad5ebbb82528511e7aa5944ba957ec58d6d20c590f5513b02114781e17e4e3fa0 Homepage: https://cran.r-project.org/package=segclust2d Description: CRAN Package 'segclust2d' (Bivariate Segmentation/Clustering Methods and Tools) Provides two methods for segmentation and joint segmentation/clustering of bivariate time-series. Originally intended for ecological segmentation (home-range and behavioural modes) but easily applied on other series, the package also provides tools for analysing outputs from R packages 'moveHMM' and 'marcher'. The segmentation method is a bivariate extension of Lavielle's method available in 'adehabitatLT' (Lavielle, 1999 and 2005 ). This method rely on dynamic programming for efficient segmentation. The segmentation/clustering method alternates steps of dynamic programming with an Expectation-Maximization algorithm. This is an extension of Picard et al (2007) method (formerly available in 'cghseg' package) to the bivariate case. The method is fully described in Patin et al (2018) . Package: r-cran-segmag Architecture: arm64 Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 71124 MD5sum: 506059a8f13ff2c95447907fb963d0d7 SHA1: e80be14008a9badbad6486e924a657b0b32b55ed SHA256: fbe4e9f4a867df67d0d27a2ba21395969df45abf228b4d69f3bde4301fd11a91 SHA512: d3fc5fb8b51a6482c0f8d5b79cd1039f026d54d06ad6d2f580224c6b351557c3bd5f772190b4cb63ed62dbd06c320f1e9114067f80aba3f82fd6b3edb88c4bb1 Homepage: https://cran.r-project.org/package=segmag Description: CRAN Package 'segmag' (Determine Event Boundaries in Event Segmentation Experiments) Contains functions that help to determine event boundaries in event segmentation experiments by bootstrapping a critical segmentation magnitude under the null hypothesis that all key presses were randomly distributed across the experiment. Segmentation magnitude is defined as the sum of Gaussians centered at the times of the segmentation key presses performed by the participants. Within a participant, the maximum of the overlaid Gaussians is used to prevent an excessive influence of a single participant on the overall outcome (e.g. if a participant is pressing the key multiple times in succession). Further functions are included, such as plotting the results. Package: r-cran-segmentier Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 898 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 645534 MD5sum: 2825c7cb21db4837842eb0985df3ecb2 SHA1: 62ec843b430adc44c4722bae179aaf156deee82a SHA256: 22c370e801c45ab6f1a1b5dab3a11b79d6ed766997e998b7400b60b2377e0ea6 SHA512: 946de66b2f185c8c02b59972956bd8b5f45fa8d095bed77723f7f07f4db486b988ac6a989ecec7eb8b993a30a8333bea7144c0bd0733e9e20cb0c83f2b32dc7b Homepage: https://cran.r-project.org/package=segmenTier Description: CRAN Package 'segmenTier' (Similarity-Based Segmentation of Multidimensional Signals) A dynamic programming solution to segmentation based on maximization of arbitrary similarity measures within segments. The general idea, theory and this implementation are described in Machne, Murray & Stadler (2017) . In addition to the core algorithm, the package provides time-series processing and clustering functions as described in the publication. These are generally applicable where a `k-means` clustering yields meaningful results, and have been specifically developed for clustering of the Discrete Fourier Transform of periodic gene expression data (`circadian' or `yeast metabolic oscillations'). This clustering approach is outlined in the supplemental material of Machne & Murray (2012) ), and here is used as a basis of segment similarity measures. Notably, the time-series processing and clustering functions can also be used as stand-alone tools, independent of segmentation, e.g., for transcriptome data already mapped to genes. Package: r-cran-segmentr Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 541 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_arm64.deb Size: 265282 MD5sum: c91ce4542de3c7c05df0154d7dbd3191 SHA1: 35e66ad4c1e6446b48de4ca24b9757d4742acab4 SHA256: 8e3203f1ef161e25d8ed8ff1dae08c3aaf2edc34213734a13796f751dc984427 SHA512: fcb9e055cbac8f09d59a632d0c517b0a748ed17bae41c18ead9bf37cdd94c7d6ebc39ab65875fff5a4e8c3f4ef407a11840f5051e6950efbe5ca9865c1614d09 Homepage: https://cran.r-project.org/package=segmentr Description: CRAN Package 'segmentr' (Segment Data With Maximum Likelihood) Given a likelihood provided by the user, this package applies it to a given matrix dataset in order to find change points in the data that maximize the sum of the likelihoods of all the segments. This package provides a handful of algorithms with different time complexities and assumption compromises so the user is able to choose the best one for the problem at hand. The implementation of the segmentation algorithms in this package are based on the paper by Bruno M. de Castro, Florencia Leonardi (2018) . The Berlin weather sample dataset was provided by Deutscher Wetterdienst . You can find all the references in the Acknowledgments section of this package's repository via the URL below. Package: r-cran-segmgarch Architecture: arm64 Version: 1.3-1.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-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_arm64.deb Size: 150504 MD5sum: 2c0d7f0f66c60664ac80764586dee6ad SHA1: f55a0ada85599107e3d6cff7e8bd27fc6b49d688 SHA256: be03321c3a9f550e5db397148274c163f88c4ce650d1e9c05cf1c881bc689b1f SHA512: 0f31477313741956b36033cab32d1272de37a50393345de283257580e6ba7a3a63a9c09de47177b6659fac2a88def55177177caacbb31db24f59d852f6017fc2 Homepage: https://cran.r-project.org/package=segMGarch Description: CRAN Package 'segMGarch' (Multiple Change-Point Detection for High-Dimensional GARCHProcesses) Implements a segmentation algorithm for multiple change-point detection in high-dimensional GARCH processes. It simultaneously segments GARCH processes by identifying 'common' change-points, each of which can be shared by a subset or all of the component time series as a change-point in their within-series and/or cross-sectional correlation structure. Package: r-cran-segregation Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1093 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_arm64.deb Size: 641238 MD5sum: 703b76f00a99f705929ed8890debcb4f SHA1: e013431fec9fb5bb1f34aec474cb6801c0090832 SHA256: 94a4f11360035fd3b5b73efc7707f5c58ab22e79356034a570bdd4370a901efc SHA512: ecc2ad7dae8bfff9a2ab6bf72e25afa2554bd9df1919d66d172004925b7d3c08f24d438884574280bbc769a52a266f816a1d014f93bd7eb555e26d12987872f8 Homepage: https://cran.r-project.org/package=segregation Description: CRAN Package 'segregation' (Entropy-Based Segregation Indices) Computes segregation indices, including the Index of Dissimilarity, as well as the information-theoretic indices developed by Theil (1971) , namely the Mutual Information Index (M) and Theil's Information Index (H). The M, further described by Mora and Ruiz-Castillo (2011) and Frankel and Volij (2011) , is a measure of segregation that is highly decomposable. The package provides tools to decompose the index by units and groups (local segregation), and by within and between terms. The package also provides a method to decompose differences in segregation as described by Elbers (2021) . The package includes standard error estimation by bootstrapping, which also corrects for small sample bias. The package also contains functions for visualizing segregation patterns. Package: r-cran-segtest Architecture: arm64 Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1222 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_arm64.deb Size: 969102 MD5sum: 9c979fd16d8a9f558a73ada5202a65c8 SHA1: 737e22bf1d73a5be55732cfdeaa6d1eb9c9407a1 SHA256: 8d21099e1ab0f080097f50a33a3bd8e75f3965510eb7e89d895acb3dc16283ec SHA512: 41f63f5133eaaeb6c6dfaf1a1327df46ae6c5ed993b46ba5c74968a8f1bbb75fc6367394e61e6dbadce1a5a8f828668e0764b54a7b8dcbfc2432cd16445489e6 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. 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See the 'RcppRoll' package for alternative versions of basic statistical functions such as rolling mean, median, etc. Package: r-cran-sel Architecture: arm64 Version: 1.0-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 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_arm64.deb Size: 84658 MD5sum: 356863819d1be579f4865323f92c0b08 SHA1: 4bfd7a3f98c30447c7562d3b7a36c3a74ec75fe4 SHA256: 9148f71e75ad7d00b38d760dd7696c2f1a99972723ed5c05fba009adc626ab88 SHA512: 19eea133307b7730a1d0136ae5b20583f7531d492bce2fffd6da4f1cadd477544cff41efda0a7e5ef6ce5ce07f4c0b372bf1f5e50d6e0213dce33cb6a84467af Homepage: https://cran.r-project.org/package=SEL Description: CRAN Package 'SEL' (Semiparametric Elicitation) Implements a method for fitting a bounded probability distribution to quantiles (for example stated by an expert), see Bornkamp and Ickstadt (2009) for details. 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Package: r-cran-selectboost.beta Architecture: arm64 Version: 0.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1301 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_arm64.deb Size: 780412 MD5sum: 596a5bf8ad67882b80f7cf2f54e38b9b SHA1: d2bf3b95831b63291fc23b2553b98a6a34f1bc31 SHA256: f77cf33ca632052875cb2741085b56249ba0761fcdf7dcbcb835002dbce741d8 SHA512: bb3867041dbc614042340adce50df6ce6d617b4b0366b05aca7f2831243e2efee0f13a830c2d16cf909dbeb5e08c16778980def8953906cd6565763fd7884444 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. 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Package: r-cran-selectiveinference Architecture: arm64 Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 601 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_arm64.deb Size: 422632 MD5sum: e55dc58f084b5e7e000bd40a720628ec SHA1: 88e539818810952dc9cd3ae3d75723f9e334b25d SHA256: 68718c4311b6e2ca0b2b2fa2d4b5c335b94ce009542a3f2a401cfb19250de81f SHA512: 8be2771e113df4cdb20287d9eabe090b0ecba2058431099df5550f90fcc1866af15c45992e3cc13eb11a11083d07fba93f094cab0da05e2b018b889f6a906381 Homepage: https://cran.r-project.org/package=selectiveInference Description: CRAN Package 'selectiveInference' (Tools for Post-Selection Inference) New tools for post-selection inference, for use with forward stepwise regression, least angle regression, the lasso, and the many means problem. 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Package: r-cran-sensitivity Architecture: arm64 Version: 1.31.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2953 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_arm64.deb Size: 2585242 MD5sum: 68c499ea2bb0dde9fb9f4b41df2b51a3 SHA1: 85cae2a28089c0dc1963b5465b3abc569c63d454 SHA256: 3c7dc61834f6732025fdf9d15671ec6562173ff77d661c3599a1262d06374b24 SHA512: c0cd72ba48b79b2ea305aa284ae8db50a83b548b87693d0b911f2149639cc3a8c851ac9bc5bea931e022300a4d8f0cfae777986d0467ceabd074bc9ba3f65bee 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: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 427 Depends: libc6 (>= 2.29), libgcc-s1 (>= 4.2), 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_arm64.deb Size: 228682 MD5sum: e254cae617c227982bcc43c5f748311d SHA1: 14c2206a8763a214547c5138065fabc9e132d23e SHA256: 9164dbf42ece3b5566517f8f2428319953478c0ff60d9dada44125092600682a SHA512: 3bf218d6fe6f7dd0f086e501e87abfbb7c403956fa21f0d4aaa77e3b64f85156f9462a529d7317fd034caaa7a5b65b9392f9013323b7088400f1d5b5e853e7ba Homepage: https://cran.r-project.org/package=sensitivityIxJ Description: CRAN Package 'sensitivityIxJ' (Exact Nonparametric Sensitivity Analysis for I by J ContingencyTables) Implements exact, normally approximated, and sampling-based sensitivity analysis for observational studies with contingency tables. Includes exact (kernel-based), normal approximation, and sequential importance sampling (SIS) methods using 'Rcpp' for computational efficiency. The methods build upon the framework introduced in Rosenbaum (2002) and the generalized design sensitivity framework developed by Chiu (2025) . Package: r-cran-sensobol Architecture: arm64 Version: 1.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1619 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-boot, r-cran-data.table, r-cran-ggplot2, r-cran-lhs, r-cran-magrittr, r-cran-matrixstats, r-cran-randtoolbox, r-cran-desolve, r-cran-rdpack, r-cran-rfast, r-cran-rlang, r-cran-scales, r-cran-stringr, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-sensobol_1.1.9-1.ca2604.1_arm64.deb Size: 1495156 MD5sum: 68cc47e5cba3cbb5ffe3435716093d22 SHA1: 9d677b7fa3488632bbbc06c2200be942bffb308e SHA256: 7a6af9326e8e940dd176b4f47016b9eb3211eef259fcfcc8dc8289d03b9c8e0e SHA512: 843297f20204a490514c7684d9cfab351a92dc4baf270bf27e21ba9e3493abdd419c1050df15d7cace22619dd467995280dc0537f5431bb02030da00bf064fd6 Homepage: https://cran.r-project.org/package=sensobol Description: CRAN Package 'sensobol' (Computation of Variance-Based Sensitivity Indices) It allows to rapidly compute, bootstrap and plot up to fourth-order Sobol'-based sensitivity indices using several state-of-the-art first and total-order estimators. Sobol' indices can be computed either for models that yield a scalar as a model output or for systems of differential equations. The package also provides a suit of benchmark tests functions and several options to obtain publication-ready figures of the model output uncertainty and sensitivity-related analysis. An overview of the package can be found in Puy et al. (2022) . Package: r-cran-senspe Architecture: arm64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 178 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-senspe_1.3-1.ca2604.1_arm64.deb Size: 49924 MD5sum: 796a8fcc898d0aca2f1094421c72f73c SHA1: 1c1a4e29037de2d93d0916b6267f6836e347ce58 SHA256: b3d3416fd311909dc8d01534ffc898c88e21a8275b351266b2fe10bde8bc6098 SHA512: fbcee4181a12e885834fff34bcdb4a6a5fc39b0c3a00c9f2a46236daa3c53b2f146a7f3d71a0abc7a794793758eecacd73309f858578737f859bdc2bac6db3f3 Homepage: https://cran.r-project.org/package=SenSpe Description: CRAN Package 'SenSpe' (Estimating Specificity at Controlled Sensitivity, or Vice Versa) Perform biomarker evaluation and comparison in terms of specificity at a controlled sensitivity level, or sensitivity at a controlled specificity level. Point estimation and exact bootstrap of Huang, Parakati, Patil, and Sanda (2023) for the one- and two-biomarker problems are implemented. Package: r-cran-sentencepiece Architecture: arm64 Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4275 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.2), 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_arm64.deb Size: 1380726 MD5sum: da5da173936201f1e8faf3a67907f010 SHA1: 6960fcbb99dcee945c3476e517c5e8fc5ea92f90 SHA256: f930e17d9384d51488d982721ecccaf05369f50b62df9850f38a6c2df3abb5f9 SHA512: b56b87ed24fe4e5e345446e6bc9a73920f43f8a724075d2e5906a2d76f59d96c9cc9faad525184ae81f2089f570bb51d5f1cdce17c58d0e778bc3d4eba372e91 Homepage: https://cran.r-project.org/package=sentencepiece Description: CRAN Package 'sentencepiece' (Text Tokenization using Byte Pair Encoding and Unigram Modelling) Unsupervised text tokenizer allowing to perform byte pair encoding and unigram modelling. Wraps the 'sentencepiece' library which provides a language independent tokenizer to split text in words and smaller subword units. The techniques are explained in the paper "SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing" by Taku Kudo and John Richardson (2018) . Provides as well straightforward access to pretrained byte pair encoding models and subword embeddings trained on Wikipedia using 'word2vec', as described in "BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages" by Benjamin Heinzerling and Michael Strube (2018) . Package: r-cran-sentometrics Architecture: arm64 Version: 1.0.1-1.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_arm64.deb Size: 3506830 MD5sum: 62e49831322bca72fdf28db16d6d5e76 SHA1: d4804f51c57949ef95282eb56dd9e6f7ed3ae2e9 SHA256: ace53596cbe269a269293baf04ab04768a30f7816055eba0dada1d4a54778ecf SHA512: 770801ea42bdd6a9bf572da24a03c913542f0b1bb8589e3c1c4805b62bab967bc17fd49e96e5b9432a10d78a40dfb93385a2453732104135f74b887039d0e8d5 Homepage: https://cran.r-project.org/package=sentometrics Description: CRAN Package 'sentometrics' (An Integrated Framework for Textual Sentiment Time SeriesAggregation and Prediction) Optimized prediction based on textual sentiment, accounting for the intrinsic challenge that sentiment can be computed and pooled across texts and time in various ways. See Ardia et al. (2021) . Package: r-cran-sentopics Architecture: arm64 Version: 0.7.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2619 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 2067646 MD5sum: 61aca29fb244fa3feb6d8ddcc5cb0a96 SHA1: d343c5eb863e425682e9e2198333f94006b845c5 SHA256: 6b792f0e96e82f0d5b0a6803b153b06349f7e30450a46d876af2c0e8217be437 SHA512: aca51f9b2bf100579aa090a717dd70243858ac3fea8be40f3a8d3ca7db37ab1d1dd01e1dd41358d4d1e16aedbe53d170bbdbdaa137ed1d7780e18d34faf2d07c Homepage: https://cran.r-project.org/package=sentopics Description: CRAN Package 'sentopics' (Tools for Joint Sentiment and Topic Analysis of Textual Data) A framework that joins topic modeling and sentiment analysis of textual data. The package implements a fast Gibbs sampling estimation of Latent Dirichlet Allocation (Griffiths and Steyvers (2004) ) and Joint Sentiment/Topic Model (Lin, He, Everson and Ruger (2012) ). It offers a variety of helpers and visualizations to analyze the result of topic modeling. The framework also allows enriching topic models with dates and externally computed sentiment measures. A flexible aggregation scheme enables the creation of time series of sentiment or topical proportions from the enriched topic models. Moreover, a novel method jointly aggregates topic proportions and sentiment measures to derive time series of topical sentiment. Package: r-cran-seq2r Architecture: arm64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 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_arm64.deb Size: 94372 MD5sum: 92bce79cff65e4493490d0a5fa07f96c SHA1: 52a7d73338741170db7b4d73c6bdf3464d3f97b5 SHA256: ba20e236e9d97c73ce7405edea5e7b5406e13b665dadb2bc09b45d6dd1a43bb5 SHA512: 4d91e7bd98a35533cc638a4d84401a331970e442ae15c459a27186abb3d7ef15d1a009122448755169911e319fea96cc2449597064e9ca7c5ea34f1da5c90ce4 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: arm64 Version: 2.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3859 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_arm64.deb Size: 2614828 MD5sum: 25526f8fae8b4920f9b976b11ed45ae1 SHA1: a226d7eead4e91b29bca1ebfcb9ab73a63a0dd36 SHA256: ba838bf255daa0277fc755b1a4e47fb4105f25bedfcdca011af418ceed91f71c SHA512: d50f264ce875b0af6e854c170d02d657872abb0c9026f8ba54d4bebed75597b5b2f1bef9ecb64d1771f5ad43bdfc13304f99b2bd445410acc768b24ed1ebec44 Homepage: https://cran.r-project.org/package=seqHMM Description: CRAN Package 'seqHMM' (Mixture Hidden Markov Models for Social Sequence Data and OtherMultivariate, Multichannel Categorical Time Series) Designed for estimating variants of hidden (latent) Markov models (HMMs), mixture HMMs, and non-homogeneous HMMs (NHMMs) for social sequence data and other categorical time series. Special cases include feedback-augmented NHMMs, Markov models without latent layer, mixture Markov models, and latent class models. The package supports models for one or multiple subjects with one or multiple parallel sequences (channels). External covariates can be added to explain cluster membership in mixture models as well as initial, transition and emission probabilities in NHMMs. The package provides functions for evaluating and comparing models, as well as functions for visualizing of multichannel sequence data and HMMs. For NHMMs, methods for computing average causal effects and marginal state and emission probabilities are available. Models are estimated using maximum likelihood via the EM algorithm or direct numerical maximization with analytical gradients. Documentation is available via several vignettes, and Helske and Helske (2019, ). For methodology behind the NHMMs, see Helske (2025, ). Package: r-cran-seqinr Architecture: arm64 Version: 4.2-44-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5322 Depends: libc6 (>= 2.38), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ade4, r-cran-segmented Filename: pool/dists/resolute/main/r-cran-seqinr_4.2-44-1.ca2604.1_arm64.deb Size: 4072492 MD5sum: b065bb74055c412a80246c4cae869dc8 SHA1: 174dd7ecda9a14efd39dc9ecc40e49745f12f590 SHA256: 3c01bfeeebec39fb5da7685cb98b5f3e6c8e73e36663fd9dbcd9e03be2f53a29 SHA512: 9e7f5da7c16eeb6f502bdaa0a7b36632c7362d8abb6158c868d02dc86a4aeacf1b00d8129f1367485365cda5e1b7c7d5a6f53c7854246a499d0099f331879703 Homepage: https://cran.r-project.org/package=seqinr Description: CRAN Package 'seqinr' (Biological Sequences Retrieval and Analysis) Exploratory data analysis and data visualization for biological sequence (DNA and protein) data. Seqinr includes utilities for sequence data management under the ACNUC system described in Gouy, M. et al. (1984) Nucleic Acids Res. 12:121-127 . Package: r-cran-seqkat Architecture: arm64 Version: 0.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2546 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 886058 MD5sum: 44ebbd58d6b07d5f98f8a51589ea9214 SHA1: 18abbfbccc421a80bbe65d2d6e9a6e5657358c30 SHA256: 1d31deb2a165ba4dc957ab47ced8e7c563ed9dc95fa517b5893b7922a1303477 SHA512: 23e581991db1fe02ef2eb2a89c4b81c505b9068375b0420cb60b92a0cc253c9ce528fd9b600fe8de974b143051bd1a3566de74919a1fb2275a931c3470e3d283 Homepage: https://cran.r-project.org/package=SeqKat Description: CRAN Package 'SeqKat' (Detection of Kataegis) Kataegis is a localized hypermutation occurring when a region is enriched in somatic SNVs. Kataegis can result from multiple cytosine deaminations catalyzed by the AID/APOBEC family of proteins. This package contains functions to detect kataegis from SNVs in BED format. This package reports two scores per kataegic event, a hypermutation score and an APOBEC mediated kataegic score. Yousif, F. et al.; The Origins and Consequences of Localized and Global Somatic Hypermutation; Biorxiv 2018 . Package: r-cran-seqminer Architecture: arm64 Version: 9.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3544 Depends: libbz2-1.0, libc6 (>= 2.38), libgcc-s1 (>= 4.2), libstdc++6 (>= 13.1), libzstd1 (>= 1.5.5), zlib1g (>= 1:1.1.4), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat, r-cran-skat Filename: pool/dists/resolute/main/r-cran-seqminer_9.9-1.ca2604.1_arm64.deb Size: 2208282 MD5sum: f182b3d2235d8089ed09f7b3d9a97489 SHA1: 3f6dc198f4d57e2ec411370b1b1fcdd36312b088 SHA256: 71e3971db7520b02c3ca9fbb3967f03e3461c86bd0478f665ca2fade234875a2 SHA512: 6b10d9258617f7e41c4e2109a20a623ec3f8406bf676fddd0b56d50d19a3057a04495d5b119fc7724fd0b9217bd8a39f76513a6567f5ac7424cbd90be9278c0a 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: arm64 Version: 0.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1885 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_arm64.deb Size: 1418220 MD5sum: 033189d937d90c90b28b91c3b5cdb3b4 SHA1: fe57aa4b35b61942f31f1a7878dd87e1e707051c SHA256: eab7eb595bc2748b86dde55aa16f2bccaa4098471fad19f0aae29c8c25b01f3e SHA512: 593aa99661254ff3cda0d3bd5cf2305be13adb5dfe336150fa4460245a8502b14fff10ce8f16b177e033853571590787367c9e67d3ae8071f4c0fdde4a94d574 Homepage: https://cran.r-project.org/package=seqtrie Description: CRAN Package 'seqtrie' (Radix Tree and Trie-Based String Distances) A collection of Radix Tree and Trie algorithms for finding similar sequences and calculating sequence distances (Levenshtein and other distance metrics). This work was inspired by a trie implementation in Python: "Fast and Easy Levenshtein distance using a Trie." Hanov (2011) . Package: r-cran-sequencespikeslab Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 258 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_arm64.deb Size: 97826 MD5sum: c6a057649f44facea3a91bed07446ee6 SHA1: fb3763a5ca327185eb55a51139a42472bb7a5bc6 SHA256: 39608a2c86ee5c36d35ce2c27ade28fb2b8c085b0e6be549ca5adaf6f8b1cfc7 SHA512: 0c03c4cc9a53707411f18acf54328f47a946e5d7278ac9b0f31c0a27b48255053969a4857e3082a3850a20cbd6fd37f06792225b8376e0328be39f2d34b0e462 Homepage: https://cran.r-project.org/package=SequenceSpikeSlab Description: CRAN Package 'SequenceSpikeSlab' (Exact Bayesian Model Selection Methods for the Sparse NormalSequence Model) Contains fast functions to calculate the exact Bayes posterior for the Sparse Normal Sequence Model, implementing the algorithms described in Van Erven and Szabo (2021, ). For general hierarchical priors, sample sizes up to 10,000 are feasible within half an hour on a standard laptop. For beta-binomial spike-and-slab priors, a faster algorithm is provided, which can handle sample sizes of 100,000 in half an hour. In the implementation, special care has been taken to assure numerical stability of the methods even for such large sample sizes. Package: r-cran-sequoia Architecture: arm64 Version: 3.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3292 Depends: libc6 (>= 2.39), 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_arm64.deb Size: 2637686 MD5sum: 84bf8f37edb65944227af5d9d175cfbb SHA1: f0849df88fa1d9f1a6c13e258a9040b6beda970e SHA256: 5675c8d229bd67cf231354de6ab5f3756c65ce2fb03aaf02f8aec7767df745bd SHA512: f45e84e70ef17cf48107bd0301f161e42cb39c40f4153a56e059d86d99488d4ccfba0d4318d0789b37fbc30812ec73dc5a0435090e2bf4552f4130fd0e7c05f2 Homepage: https://cran.r-project.org/package=sequoia Description: CRAN Package 'sequoia' (Pedigree Inference from SNPs) Multi-generational pedigree inference from incomplete data on hundreds of SNPs, including parentage assignment and sibship clustering. See Huisman (2017) () for more information. Package: r-cran-seriation Architecture: arm64 Version: 1.5.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1558 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ca, r-cran-cluster, r-cran-colorspace, r-cran-foreach, r-cran-gclus, r-cran-mass, r-cran-qap, r-cran-registry, r-cran-tsp, r-cran-vegan Suggests: r-cran-dbscan, r-cran-dendser, r-cran-dendextend, r-cran-doparallel, r-cran-ga, r-cran-ggplot2, r-cran-keras, r-cran-rtsne, r-cran-scales, r-cran-smacof, r-cran-tensorflow, r-cran-testthat, r-cran-umap Filename: pool/dists/resolute/main/r-cran-seriation_1.5.8-1.ca2604.1_arm64.deb Size: 1346444 MD5sum: c51d61711673cce71377018dccb2649c SHA1: 9116c10221ef8f7c9cffc440930118ce0928c15e SHA256: 5eec63779711a32af7cb537fc1d2a8db7d677234020d12a30ac776db67b5fe33 SHA512: efd6ccd2ef16e68e3b5a458c4ba2a0da8511d2fd776c5f242f53504637fb306956c07008eeff452f696108455352671ce05126f3f3d5168f8393dc849034916f Homepage: https://cran.r-project.org/package=seriation Description: CRAN Package 'seriation' (Infrastructure for Ordering Objects Using Seriation) Infrastructure for ordering objects with an implementation of several seriation/sequencing/ordination techniques to reorder matrices, dissimilarity matrices, and dendrograms. Also provides (optimally) reordered heatmaps, color images and clustering visualizations like dissimilarity plots, and visual assessment of cluster tendency plots (VAT and iVAT). Hahsler et al (2008) . Package: r-cran-serocalculator Architecture: arm64 Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 798 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_arm64.deb Size: 495864 MD5sum: 15bb1960599c35d226d8e9f7532a6d4d SHA1: 831c19f755ff4d161584c04fbf56c6f214060441 SHA256: c032db6dc9793c94ce726d92ac2c7d44ad63fa8b0bff39cfbcbc26ef3d984647 SHA512: e53c3facdb71e6bd9eb751b76d1a3b5bdc59c425261ecc91126b8ec5498caa46780928cc4554497a74b534862246cf0f09f80b19e39510934001a5f7ed9bb771 Homepage: https://cran.r-project.org/package=serocalculator Description: CRAN Package 'serocalculator' (Estimating Infection Rates from Serological Data) Translates antibody levels measured in cross-sectional population samples into estimates of the frequency with which seroconversions (infections) occur in the sampled populations. Replaces the previous `seroincidence` package. Package: r-cran-serofoi Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6806 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_arm64.deb Size: 1874518 MD5sum: 1d4133192fa890a76d1c73a1e379b2ba SHA1: 6832e6804331dbd9de22d0b95373462176afc73a SHA256: 648fda509c9e2ac8ebef7e2c6e553ae343a643c177169a49ec87a017053c637c SHA512: a6b031f42e279fa73171789b72d6e447a9f602e8eef27bb6fb1cfbeafad1b83c122d7eb45a55d06d9ceac34ed03ff091d463df28e6ad57d65baed12587be3d98 Homepage: https://cran.r-project.org/package=serofoi Description: CRAN Package 'serofoi' (Bayesian Estimation of the Force of Infection from SerologicalData) Estimating the force of infection from time varying, age varying, or constant serocatalytic models from population based seroprevalence studies using a Bayesian framework, including data simulation functions enabling the generation of serological surveys based on this models. This tool also provides a flexible prior specification syntax for the force of infection and the seroreversion rate, as well as methods to assess model convergence and comparison criteria along with useful visualisation functions. Package: r-cran-seroreconstruct Architecture: arm64 Version: 1.1.5-1.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_arm64.deb Size: 807870 MD5sum: ba3e5b27e4a35af25fe3f73c765aa6f3 SHA1: 1a38347120591b48737f067fe5a42fdc7d8e1678 SHA256: 05a58dbc8368650a1acb4a6e4c422f241f1dd13822cf23320f74ade3dfde70c7 SHA512: 0fc1818ba7d8e064b2cd97739bc15a0864b5cdb60940afc8cd5d765e396d56ebe453973a840a3b90f0e2e817171c6812ef8de85b0418efea48d400ddd2cf28ee Homepage: https://cran.r-project.org/package=seroreconstruct Description: CRAN Package 'seroreconstruct' (Reconstructing Antibody Dynamics to Estimate the Risk ofInfluenza Virus Infection) A Bayesian framework for inferring influenza infection status from serial antibody measurements. Jointly estimates season-specific infection probabilities, antibody boosting and waning after infection, and baseline hemagglutination inhibition (HAI) titer distributions via Markov chain Monte Carlo (MCMC). Supports multi-season analysis and subgroup comparisons via a group_by interface. See Tsang et al. (2022) for methodological details. Package: r-cran-serosv Architecture: arm64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7753 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_arm64.deb Size: 3242816 MD5sum: 08976dc25becbb446ecb1b5355f286fa SHA1: 579a0b162bfde12e89ce9caefb59db6e23662d5d SHA256: 4967844b6e2792be86412130bf376a359b06580da6b7bd1db4b74eaa84cf1770 SHA512: dd017b2cb623a8ce1569644d171815292a874a4cda459bcef803f184e3dad717956f407f04943ace27f2764a26da2d8f39e5baa3e241a78393a3d78c571ea590 Homepage: https://cran.r-project.org/package=serosv Description: CRAN Package 'serosv' (Model Infectious Disease Parameters from Serosurveys) An easy-to-use and efficient tool to estimate infectious diseases parameters using serological data. Implemented models include SIR models (basic_sir_model(), static_sir_model(), mseir_model(), sir_subpops_model()), parametric models (polynomial_model(), fp_model()), nonparametric models (lp_model()), semiparametric models (penalized_splines_model()), hierarchical models (hierarchical_bayesian_model()). The package is based on the book "Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective" (Hens, Niel & Shkedy, Ziv & Aerts, Marc & Faes, Christel & Damme, Pierre & Beutels, Philippe., 2013) . Package: r-cran-serrsbayes Architecture: arm64 Version: 0.5-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1665 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_arm64.deb Size: 1108286 MD5sum: 5d58858986f9df506ad434f95fa30a45 SHA1: 370990c735eda33f135413ccaca8e6456014a504 SHA256: 7e670b6c7192fdafa01646d81cbad2886c5237db5cd3a0ce0ea75b494ecf69c8 SHA512: 65cedafebedc94729e0291191319edfa27a624c83f6f2361df64a7879ac5b3c6bf3af3c6be7940d96176334fbcc5c99eba48283eeb2a4c4f6360c5fbf216dfb7 Homepage: https://cran.r-project.org/package=serrsBayes Description: CRAN Package 'serrsBayes' (Bayesian Modelling of Raman Spectroscopy) Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive mixed model (GAMM) to surface-enhanced resonance Raman spectroscopy (SERRS), using the method of Moores et al. (2016) . Multivariate observations of SERRS are highly collinear and lend themselves to a reduced-rank representation. The GAMM separates the SERRS signal into three components: a sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a smoothly-varying baseline; and additive white noise. The parameters of each component of the model are estimated iteratively using SMC. The posterior distributions of the parameters given the observed spectra are represented as a population of weighted particles. Package: r-cran-sets Architecture: arm64 Version: 1.0-25-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 847 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-proxy Filename: pool/dists/resolute/main/r-cran-sets_1.0-25-1.ca2604.1_arm64.deb Size: 627006 MD5sum: f39d8f980fdd1bd78816f1ceb3a5fceb SHA1: bbe0216ce61fc666f02b2d30c8c29111c38467f7 SHA256: 99ecc6b52757712a3006568db4c36befff2249dd4cc683a1431ee23b2cc29e1d SHA512: d9c325119d17bed23378e08039fd89bb7a4d6496901385874b885d0d8c0e2fc0b824cfe4fd8366ea32bab5d09b14c2a24f0649f33d86ff70c12bd54462abb40b 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: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-setwidth_1.1.0-1.ca2604.1_arm64.deb Size: 15862 MD5sum: 60396799d1ed4e66d3d43a44504eae73 SHA1: 07ae2f26e163a4c8c96ca50a74ffdfe1981ffb30 SHA256: bf09bd797b81d4c32727e2ae5650b0cae68c0c19030143587d15406a04e24512 SHA512: cd69d950ec80ec4c2a6ad3b63366b388a2849e97fb68e938d620b974c731a0fc4d5a02bb9b8d46c1762020c7b618bfc8bca659f658de5bbd21d6304763c70bed 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: arm64 Version: 5.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3114 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_arm64.deb Size: 2566814 MD5sum: 66f8b89356ebf6f8130c89a9f83a43ba SHA1: e705570e1d1274c80c6b632edb28c4b77b917526 SHA256: e0d46e80b52c4b31dcb2cd9ea167853982a695343892df76f7f293bd4f5a946d SHA512: 2628cdb36002e7828825103a7f67274b2b65e2091c3a34ae3d9594967aaa48196d9fa75e490d5401c5c23d12c0ee4dd998c1f0f1b2fa9b413722d3fe118cc2f2 Homepage: https://cran.r-project.org/package=Seurat Description: CRAN Package 'Seurat' (Tools for Single Cell Genomics) A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) , Macosko E, Basu A, Satija R, et al (2015) , Stuart T, Butler A, et al (2019) , and Hao, Hao, et al (2020) for more details. Package: r-cran-seuratobject Architecture: arm64 Version: 5.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2534 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1818986 MD5sum: afe46e7f1a8402d4986cb1d6e73222fd SHA1: 664a0022393b8775a990ebd2915fce8a3cc81ec1 SHA256: c9379e3d80d8cf0b7aa28ba764ab10e5289d6df8820e43367f1a19482a0cefad SHA512: bb631112b590a53c01399a123b764aae4c5497ac7d7e009fa3119949bb66fe0a84831e356feaa8b2416d17ee61d38e78ff651537715f3eaff2f7cd680296d614 Homepage: https://cran.r-project.org/package=SeuratObject Description: CRAN Package 'SeuratObject' (Data Structures for Single Cell Data) Defines S4 classes for single-cell genomic data and associated information, such as dimensionality reduction embeddings, nearest-neighbor graphs, and spatially-resolved coordinates. Provides data access methods and R-native hooks to ensure the Seurat object is familiar to other R users. See Satija R, Farrell J, Gennert D, et al (2015) , Macosko E, Basu A, Satija R, et al (2015) , and Stuart T, Butler A, et al (2019) , Hao Y, Hao S, et al (2021) and Hao Y, et al (2023) for more details. Package: r-cran-sf Architecture: arm64 Version: 1.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8429 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_arm64.deb Size: 3646296 MD5sum: c722872a69a61d4d657aabe92f7446f5 SHA1: 462351c72d107781b70eebb50ce1a273c37ad366 SHA256: 0addbacd87f7076bab60f88ebe42ed984d1cddaecc713533febcd5330c7b4ff3 SHA512: a5f141ccb0f4ff956032fb0ed81095acee5d5500e564cd2b3a9d745b7cc5cf6ba767ae888c65224e7e7bd7beb43b8fcd372c20c73f4369f2d30d7397a9d9df1f Homepage: https://cran.r-project.org/package=sf Description: CRAN Package 'sf' (Simple Features for R) Support for simple feature access, a standardized way to encode and analyze spatial vector data. Binds to 'GDAL' for reading and writing data, to 'GEOS' for geometrical operations, and to 'PROJ' for projection conversions and datum transformations. Uses by default the 's2' package for geometry operations on geodetic (long/lat degree) coordinates. Package: r-cran-sfa Architecture: arm64 Version: 1.0.4-1.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_arm64.deb Size: 651520 MD5sum: f823c6e0fa4c25aff654d176a863d6a3 SHA1: fc18f97a858041c20a98ffd77771fd43728c5066 SHA256: bbf70be6b135a894864fe4fde4168419c09499508004db97debae13b4733d36a SHA512: 20c0d5f1fb54110f26128c320b3720a2e645a976939bb834de13018a19220c1d7420d82154dc1dd55d6a738599edeb6764ec98485637b31635d46bf4cdf43df4 Homepage: https://cran.r-project.org/package=sfa Description: CRAN Package 'sfa' (Stochastic Frontier Analysis) Provides a user-friendly framework for estimating a wide variety of cross-sectional and panel stochastic frontier models. Suitable for a broad range of applications, the implementation offers extensive flexibility in specification and estimation techniques. Package: r-cran-sfcr Architecture: arm64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 512 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 318730 MD5sum: 667880ba05acc6a67f12e17afa2a76ae SHA1: da6c2611c6fbc80767ea6c0a2a7d801e9cf30772 SHA256: cd0e75629ad47ff8a388a1b9ce9b4a73919ffd2be978ea1556adbde53a023165 SHA512: 7c671916473e38778cce19ed9c87b30f2562c6b926ef74ce0a78ecb5931c044bc3b9787779308ac84348b7502d4f0e65a292d012e934c70c8bef96234cb5c043 Homepage: https://cran.r-project.org/package=sfcr Description: CRAN Package 'sfcr' (Simulate Stock-Flow Consistent Models) Routines to write, simulate, and validate stock-flow consistent (SFC) models. The accounting structure of SFC models are described in Godley and Lavoie (2007, ISBN:978-1-137-08599-3). The algorithms implemented to solve the models (Gauss-Seidel and Broyden) are described in Kinsella and O'Shea (2010) and Peressini and Sullivan (1988, ISBN:0-387-96614-5). Package: r-cran-sfcurve Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 988 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_arm64.deb Size: 598154 MD5sum: 0917ecd67d2dbe37e38dd975a28d8c59 SHA1: 4688dd10063e12d17e10f7b41e69b7350b1c947b SHA256: 38013cb038114948b9d80a735a8fdba6c118254ee82b7349b0feb17fede19ca9 SHA512: 63f1bf314b48c7216470c279e1d6967a2c5d06aad3e6f6f252bd45fa6e6ca9c2b1b61398e881608ae19c5ffeb4beb2caf4e762e2469279789e945d23c5a8a674 Homepage: https://cran.r-project.org/package=sfcurve Description: CRAN Package 'sfcurve' (2x2, 3x3 and Nxn Space-Filling Curves) Implementation of all possible forms of 2x2 and 3x3 space-filling curves, i.e., the generalized forms of the Hilbert curve , the Peano curve and the Peano curve in the meander type (Figure 5 in ). It can generates nxn curves expanded from any specific level-1 units. It also implements the H-curve and the three-dimensional Hilbert curve. See for more details. Package: r-cran-sfdesign Architecture: arm64 Version: 0.1.5-1.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-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_arm64.deb Size: 180736 MD5sum: 2a6958ca1d1ee6d4f773f7f6e2fac460 SHA1: 4adff2926653e217c885f9c0e5121c63a521ddb4 SHA256: 4a4ac6ed00a5799eb918ba9c811edb7a98092d3ef82f2351e9a20a259d669e34 SHA512: bf292d254e65c37ad5982c68a2d0edffb6bed9ff559cddca4f4f3b6b1ac49337053194a6a9f7bacb095e2938e8a451af0c1c0580fc6c19c45fba6f940b935413 Homepage: https://cran.r-project.org/package=SFDesign Description: CRAN Package 'SFDesign' (Space-Filling Designs) Construct various types of space-filling designs, including Latin hypercube designs, clustering-based designs, maximin designs, maximum projection designs, and uniform designs (Joseph 2016 ). It also offers the option to optimize designs based on user-defined criteria. This work is supported by U.S. National Science Foundation grant DMS-2310637. Package: r-cran-sffdr Architecture: arm64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 699 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 496930 MD5sum: e049e0a67ea8910fe9b496850cc86886 SHA1: 855ac9d4eae7fdf6982edf54b412c4aeec07af0c SHA256: 45abca5ef77b41c3b798d70ef563bcce99e46b1eb3fbac9322ff6de138348f28 SHA512: fd24e229f7ad02108ac12706e5145ce3af9103a7103bfb5a9440a64c6c8def2d8eee3e7db6e3e3e9fbb54b6ad7b90e6870703fc1a0058ce746e9aedb2eb6b791 Homepage: https://cran.r-project.org/package=sffdr Description: CRAN Package 'sffdr' (Surrogate Functional False Discovery Rates for Genome-WideAssociation Studies) Pleiotropy-informed significance analysis of genome-wide association studies with surrogate functional false discovery rates (sfFDR). The sfFDR framework adapts the fFDR to leverage informative data from multiple sets of GWAS summary statistics to increase power in study while accommodating for linkage disequilibrium. sfFDR provides estimates of key FDR quantities in a significance analysis such as the functional local FDR and $q$-value, and uses these estimates to derive a functional $p$-value for type I error rate control and a functional local Bayes' factor for post-GWAS analyses (e.g., fine mapping and colocalization). Package: r-cran-sfheaders Architecture: arm64 Version: 0.4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1281 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 415636 MD5sum: 8821b9d59478f285aaaa926b56405cc3 SHA1: a36294abe257c532196da36d31eee4a1805355a5 SHA256: 0dcc01550e80d24906288d9e5820ad03ca8d0a542a8868ae76b8810c2b0242bb SHA512: 7ae04ee4dc677ad07c6e97a8a6eaae4fbb7cc2df7f73f01f7b1c3e39742b69a910a4191b6d8c3cd6d120fc87192dc65394fc9dafefa84245845ca47d2a2851b9 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: arm64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1098 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 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_arm64.deb Size: 811420 MD5sum: df1164471d61050f4ade715d1f9ee1ce SHA1: 86bdde3b2a62c1903beb18cdb276a60959cf6248 SHA256: abd8c090d402796ed1c9b03a818b9fa029e21dd236fab62dce1c9347fce78200 SHA512: 869165a7357e841efec3bdfdb00e43cfaf4e4e245311b6f145641b46c6981f63ba8cd1a0ec930b683dec29c01b3e3f2d190b770f1578a7ef7e450edb1deaa911 Homepage: https://cran.r-project.org/package=sgd Description: CRAN Package 'sgd' (Stochastic Gradient Descent for Scalable Estimation) A fast and flexible set of tools for large scale estimation. 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Package: r-cran-sgdgmf Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1402 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 705368 MD5sum: 266d251ab60a2f78d045db9153c95ece SHA1: d716f20c0df58128047deadf07e166665785022e SHA256: be65d776c91b9f14b48c692b2ea3f63aa728ea2ecd11ead5fcc75cdba5607cb8 SHA512: e5a0880f0760609921231bfaa0298fde9c5383bd873b53c12c7a8a6151d476e07731d93c1249bd94d307290e9161b6edbf68727d4b97430882dc65c1331bfbd4 Homepage: https://cran.r-project.org/package=sgdGMF Description: CRAN Package 'sgdGMF' (Estimation of Generalized Matrix Factorization Models viaStochastic Gradient Descent) Efficient framework to estimate high-dimensional generalized matrix factorization models using penalized maximum likelihood under a dispersion exponential family specification. Either deterministic and stochastic methods are implemented for the numerical maximization. In particular, the package implements the stochastic gradient descent algorithm with a block-wise mini-batch strategy to speed up the computations and an efficient adaptive learning rate schedule to stabilize the convergence. All the theoretical details can be found in Castiglione et al. (2024, ). Other methods considered for the optimization are the alternated iterative re-weighted least squares and the quasi-Newton method with diagonal approximation of the Fisher information matrix discussed in Kidzinski et al. (2022, ). Package: r-cran-sgdinference Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 665 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_arm64.deb Size: 411102 MD5sum: 98304b3caea8c7983bd7219ef4db7b70 SHA1: 2d2841a0637337b76bd671a6f862425b876dccde SHA256: 292bbba6bbc7ee38b8667f907af202c583a21251d37fdca5b8d2f0a99f2d357f SHA512: 0102cf78c23e8ea3b4039ca16c9f93e6d8d8cf2cac1931a5f6680a4c31b73ff7cbb05deb69fcb43244b38c65dc74cf6aeffe7b3f85160916dd0178c57be78ec7 Homepage: https://cran.r-project.org/package=SGDinference Description: CRAN Package 'SGDinference' (Inference with Stochastic Gradient Descent) Estimation and inference methods for large-scale mean and quantile regression models via stochastic (sub-)gradient descent (S-subGD) algorithms. The inference procedure handles cross-sectional data sequentially: (i) updating the parameter estimate with each incoming "new observation", (ii) aggregating it as a Polyak-Ruppert average, and (iii) computing an asymptotically pivotal statistic for inference through random scaling. The methodology used in the 'SGDinference' package is described in detail in the following papers: (i) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2022) "Fast and robust online inference with stochastic gradient descent via random scaling". (ii) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2023) "Fast Inference for Quantile Regression with Tens of Millions of Observations". Package: r-cran-sgeostat Architecture: arm64 Version: 1.0-27-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 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_arm64.deb Size: 143708 MD5sum: 87baf4762571470c90b2e0e385b110e4 SHA1: 011eeed7575725e136d82093e6e5f0b23b5d58e4 SHA256: 2e9c5a6a50f9cbe30cfb6da47ac3e9a139210900d005363d9bc4034686055501 SHA512: e91d3c21045244fdac4c56f5dabe0030767c9975c59bb1934ce30105656da6b5af6b31244124f3eeec2258e6d86bcb3499b26f2b2a31ccacd016eef1675391fd Homepage: https://cran.r-project.org/package=sgeostat Description: CRAN Package 'sgeostat' (An Object-Oriented Framework for Geostatistical Modeling in S+) An Object-oriented Framework for Geostatistical Modeling in S+ containing functions for variogram estimation, variogram fitting and kriging as well as some plot functions. Written entirely in S, therefore works only for small data sets in acceptable computing time. Package: r-cran-sgl Architecture: arm64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 182 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_arm64.deb Size: 98702 MD5sum: 65d8bc889985e51c629d697386177297 SHA1: 638c1105249bcaec759a8a53bad1d2e43ab18195 SHA256: 5b6f861bd107706dc64dc2f3cc61af0aa47c91e6f0cdb7182c08385671f95e22 SHA512: 002f198dc41535d6c308e1f2d89592453027ca6e472e0a5168d2a2b99c8db2b2845c8e6046f46b72c46b89c9cbd93bae668df54724bf3711ba6158f956b28aff Homepage: https://cran.r-project.org/package=SGL Description: CRAN Package 'SGL' (Fit a GLM (or Cox Model) with a Combination of Lasso and GroupLasso Regularization) Fit a regularized generalized linear model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. Fits linear, logistic and Cox models. Package: r-cran-sglasso Architecture: arm64 Version: 1.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 215 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_arm64.deb Size: 133786 MD5sum: d0b996067396f5dee91f727e0a8b6ed4 SHA1: 952c63f498dfe77b5f175e69e75b93075418f362 SHA256: 73209360053753d9feef86e6e7aec960fe70c5109667592fa4086a88098bf672 SHA512: ada5b370a42ce7d5ac174bbbd2c61fc147435ed691902b8868753f041c6e0c198c9d2f06cf3ecad88064a1c670cf9ae3d060f5c6095556b10a55ba06a572b3cc Homepage: https://cran.r-project.org/package=sglasso Description: CRAN Package 'sglasso' (Lasso Method for RCON(V,E) Models) RCON(V, E) models are a kind of restriction of the Gaussian Graphical Models defined by a set of equality constraints on the entries of the concentration matrix. 'sglasso' package implements the structured graphical lasso (sglasso) estimator proposed in Abbruzzo et al. (2014) for the weighted l1-penalized RCON(V, E) model. Two cyclic coordinate algorithms are implemented to compute the sglasso estimator, i.e. a cyclic coordinate minimization (CCM) and a cyclic coordinate descent (CCD) algorithm. Package: r-cran-sgolay Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 135 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 37140 MD5sum: a33fb3da0f44717c33a66c08e748d3b9 SHA1: 8278b332d0c0e611c30858a68aa77ca8865624d0 SHA256: 0ea90d985ad60abaf6595dbff1a592879c543c8ed99525bdfcaa8b77dfdf7b21 SHA512: b5e61609d8f9bf2f752918fe36e78f1145e0544aa858049a3c6c4981b0f17ad01e4212ed07ca7f7bf9c23f6d08228dc74674bdf2e3198de6f5289847a462d1fc Homepage: https://cran.r-project.org/package=sgolay Description: CRAN Package 'sgolay' (Efficient Savitzky-Golay Filtering) Smoothing signals and computing their derivatives is a common requirement in signal processing workflows. Savitzky-Golay filters are a established method able to do both (Savitzky and Golay, 1964 ). This package implements one dimensional Savitzky-Golay filters that can be applied to vectors and matrices (either row-wise or column-wise). Vectorization and memory allocations have been profiled to reduce computational fingerprint. Short filter lengths are implemented in the direct space, while longer filters are implemented in frequency space, using a Fast Fourier Transform (FFT). Package: r-cran-sgpr Architecture: arm64 Version: 0.1.2-1.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 Filename: pool/dists/resolute/main/r-cran-sgpr_0.1.2-1.ca2604.1_arm64.deb Size: 136538 MD5sum: 64ac36d80f648b6b273c42fa10bb9cf0 SHA1: 8ca05c0eff8ec2de2a4e62dabc49fc4c239da242 SHA256: ec613588332fe870e6274c4b774059cecd8b4f3f1f2b25c222da0954b48cde15 SHA512: cc664dc2743143247e0bbb543a62f8d982b1526576ec9ef60cec1524c507d0432a056d9754d0396f725bc21eff620e0dfeaedbf7ba0967deb099c63ca8348049 Homepage: https://cran.r-project.org/package=SGPR Description: CRAN Package 'SGPR' (Sparse Group Penalized Regression for Bi-Level VariableSelection) Fits the regularization path of regression models (linear and logistic) with additively combined penalty terms. All possible combinations with Least Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP) and Exponential Penalty (EP) are supported. This includes Sparse Group LASSO (SGL), Sparse Group SCAD (SGS), Sparse Group MCP (SGM) and Sparse Group EP (SGE). For more information, see Buch, G., Schulz, A., Schmidtmann, I., Strauch, K., & Wild, P. S. (2024) . Package: r-cran-sgs Architecture: arm64 Version: 0.3.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 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_arm64.deb Size: 359190 MD5sum: 9037e6ebaf58ba6bc6c9f76e8cc82b98 SHA1: ae3ba05f1cea9b9d5764ee354c6cde7126cea59f SHA256: 0ff69ad3188205a26151123bf3e1b6020c61e3367925879be6467af8cd1e27ba SHA512: e5ead2de4ef143dae4552e74aeebbb37dbbdf1ab1db0d53163ec5af2460780d8ec36732a157888d6e4723e79088e20be9c616044b484bd11ac032b6160d45350 Homepage: https://cran.r-project.org/package=sgs Description: CRAN Package 'sgs' (Sparse-Group SLOPE: Adaptive Bi-Level Selection with FDR Control) Implementation of Sparse-group SLOPE (SGS) (Feser and Evangelou (2023) ) models. Linear and logistic regression models are supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported. In addition, a general Adaptive Three Operator Splitting (ATOS) (Pedregosa and Gidel (2018) ) implementation is provided. Group SLOPE (gSLOPE) (Brzyski et al. (2019) ) and group-based OSCAR models (Feser and Evangelou (2024) ) are also implemented. All models are available with strong screening rules (Feser and Evangelou (2024) ) for computational speed-up. Package: r-cran-shapr Architecture: arm64 Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4766 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 2760786 MD5sum: ac25cdbaaace046af04ac470e997c567 SHA1: 6f1ef4cfcfa6e4b1ce18e084ae714e52c87fa53f SHA256: 14ab0e920d866d23cbef7215ea1c477d3f89e300651366a99b491df4cfcb2d71 SHA512: 623abd23569419f3a80da4b64fa134dc18a99928b29e9d19d2daafcbb048c60c5a8437ebeb05c6a3c88e8c7e776d69f3901e1e3a21a378e79a15e6e9529da8a8 Homepage: https://cran.r-project.org/package=shapr Description: CRAN Package 'shapr' (Prediction Explanation with Dependence-Aware Shapley Values) Complex machine learning models are often hard to interpret. However, in many situations it is crucial to understand and explain why a model made a specific prediction. Shapley values is the only method for such prediction explanation framework with a solid theoretical foundation. Previously known methods for estimating the Shapley values do, however, assume feature independence. This package implements methods which accounts for any feature dependence, and thereby produces more accurate estimates of the true Shapley values. An accompanying 'Python' wrapper ('shaprpy') is available through PyPI. Package: r-cran-shard Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1018 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-pkgload, r-cran-rmarkdown, r-cran-testthat, r-cran-ps, r-cran-jsonlite, r-cran-tibble, r-cran-withr Filename: pool/dists/resolute/main/r-cran-shard_0.1.1-1.ca2604.1_arm64.deb Size: 794326 MD5sum: f266536c3179687c4aaafe4e8c10b91e SHA1: 398a642474fb340630d241c925e49270b331322c SHA256: bfa582a8a71c79b7aebfb95c593137bc8a7ea4a4f0d79f346bf6c8839140e52b SHA512: 45ac6b17771fdc3f7f524a1602cbbc41a77b9c4702b68272c747a2ed75323fcc59e3d4a24477bb8e940dbc9f429db4315b40d33e0a17778a96f409d0462e238c Homepage: https://cran.r-project.org/package=shard Description: CRAN Package 'shard' (Deterministic, Zero-Copy Parallel Execution for R) Provides a parallel execution runtime for R that emphasizes deterministic memory behavior and efficient handling of large shared inputs. 'shard' enables zero-copy parallel reads via shared/memory-mapped segments, encourages explicit output buffers to avoid large result aggregation, and supervises worker processes to mitigate memory drift via controlled recycling. Diagnostics report peak memory usage, end-of-run memory return, and hidden copy/materialization events to support reproducible performance benchmarking. Package: r-cran-sharpdata Architecture: arm64 Version: 1.4-1.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-kernsmooth, r-cran-quadprog Filename: pool/dists/resolute/main/r-cran-sharpdata_1.4-1.ca2604.1_arm64.deb Size: 45094 MD5sum: 45d73e534c2b42a02b80aed61e2af47f SHA1: 675ad1441744c0352cf6b945723174e2d16279be SHA256: c939f5eb4978db2d2cbd01e59abeed121835d520058a46ceae9d6c5773c2224a SHA512: 823befb83b66355f1e272ff3e1b44a75bea92b31e86fed3eb9bf9c0b7ebe2b212e52328999a505bf4c0780da971bfb1fc16e502941e4e0e2d6da4137968b7217 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: arm64 Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 227 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 87714 MD5sum: 2fbf6e2aaedd486ce672c5f9874fbecf SHA1: d21220e1a7042e8897e4d1973f1a6940f20d6df3 SHA256: 8452d5b9de8ff3be520363af7dad6e63e88e4f376e4a6d175b716727f71eaf67 SHA512: 7af7f91ff0b79379caf323398a98aeddef5b37da6aedb59c6cb8989430c4706cd7f24b29a7342374bfdcbec8718437bd5b2fba4647c455e14027883ad9644534 Homepage: https://cran.r-project.org/package=sharpeRratio Description: CRAN Package 'sharpeRratio' (Moment-Free Estimation of Sharpe Ratios) An efficient moment-free estimator of the Sharpe ratio, or signal-to-noise ratio, for heavy-tailed data (see ). Package: r-cran-sharppen Architecture: arm64 Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-kernsmooth, r-cran-glmnet, r-cran-np, r-cran-matrix, r-cran-locpol Filename: pool/dists/resolute/main/r-cran-sharppen_2.0-1.ca2604.1_arm64.deb Size: 146522 MD5sum: 502416dcdb5952092de7f03cc726be65 SHA1: 074b799a7d900c9c7dee81064107d793370b58c5 SHA256: f8bb9ab567707c53c7ecf0bddc2c956ab4ce8f961d57a42d5d0b52c4d418c181 SHA512: 991f40ad9573e2094f12327fffe487345f484bbc57b827d1b04cea17180d1aebd3cf3f6f4824682016ffc5f94398197cfe48da80662f1817b96250a7c183e38d Homepage: https://cran.r-project.org/package=sharpPen Description: CRAN Package 'sharpPen' (Penalized Data Sharpening for Local Polynomial Regression) Functions and data sets for data sharpening. Nonparametric regressions are computed subject to smoothness and other kinds of penalties. Package: r-cran-sheetreader Architecture: arm64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 457 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_arm64.deb Size: 162622 MD5sum: 54fa42cd98d3cd9e479025b0e6828d5a SHA1: 57dc6acef73d599360eedb0ef086afae5d6595ad SHA256: 9cf00e20a83a507fffec99f8dcfaf839fa36a5b6cfe14d10dc8205505bc48ea4 SHA512: 49807f60776554c6d7ba837dd2e21f88229f1764d032f0daca2f0c7e924cb8672cb2c524c4f00e587275af72f9efef9d975a8cdea6ba9f761d38d29538124701 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: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 551 Depends: libc6 (>= 2.17), libgcc-s1 (>= 4.2), libstdc++6 (>= 6), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cli, r-cran-rlang, r-cran-tzdb, r-cran-vctrs, r-cran-cpp11 Suggests: r-cran-covr, r-cran-lubridate, r-cran-pillar, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-shide_0.3.0-1.ca2604.1_arm64.deb Size: 237046 MD5sum: 5a09bf4abdf2872048ce1cbdc6827f4c SHA1: f474fb02cd6bfbbaf1303663734aa37c99e33c35 SHA256: dc75152def81ac4e0280a1b63508ecaa92b50f516644d9c8413d3398ef074f8b SHA512: aa5797e4651bdf79abf7c481b953542d2e1810d85c17a4b657de87c10889cacd51d6ae1a68b67b037be01f6993840ceed5883e1854bf38ece0c41127b76c4c88 Homepage: https://cran.r-project.org/package=shide Description: CRAN Package 'shide' (Date/Time Classes Based on Jalali Calendar) Implements S3 classes for storing dates and date-times based on the Jalali calendar. The main design goal of 'shide' is consistency with base R's 'Date' and 'POSIXct'. It provide features such as: date-time parsing, formatting and arithmetic. Package: r-cran-shiftconvolvepoibin Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 136 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-shiftconvolvepoibin_1.0.0-1.ca2604.1_arm64.deb Size: 61088 MD5sum: 07b2831d43c64e7e92c1679722b7f1e1 SHA1: 6277d663825169f4884607aa861bb01240ec558e SHA256: f3ce650d529c390f6cc5a8f6a550f5cb7984abdd1db87c245ee246ae7cb8c43d SHA512: 05394b3194d6e70f4ce490e799ace27be6e4162213fb5051cef06722ce7d86931daae808f0d71237b62471034b6b13090fd135bb3e4f61a13fe0749f64daccb2 Homepage: https://cran.r-project.org/package=ShiftConvolvePoibin Description: CRAN Package 'ShiftConvolvePoibin' (Exactly Computing the Tail of the Poisson-Binomial Distribution) An exact method for computing the Poisson-Binomial Distribution (PBD). The package provides a function for generating a random sample from the PBD, as well as two distinct approaches for computing the density, distribution, and quantile functions of the PBD. The first method uses direct-convolution, or a dynamic-programming approach which is numerically stable but can be slow for a large input due to its quadratic complexity. The second method is much faster on large inputs thanks to its use of Fast Fourier Transform (FFT) based convolutions. Notably in this case the package uses an exponential shift to practically guarantee the relative accuracy of the computation of an arbitrarily small tail of the PBD -- something that FFT-based methods often struggle with. This ShiftConvolvePoiBin method is described in Peres, Lee and Keich (2020) where it is also shown to be competitive with the fastest implementations for exactly computing the entire Poisson-Binomial distribution. Package: r-cran-shiftr Architecture: arm64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 194 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_arm64.deb Size: 76806 MD5sum: c2b909a603d05345462cb8c879486774 SHA1: 083dce6bf1df8b2f35b77448e899e25837bdd240 SHA256: dc4a07ebb9c1579718870f1416b028d1cb51cdfe7e21e983550341f3d4b0928f SHA512: 8c8ccbb89eb04dcfc31d7c38f13cf35a21ec3c914eb95a3767c7c179d85ea67ff243081fb6a21fc52b92818fe322217a086f1a0e6e7683041f28760910db695d Homepage: https://cran.r-project.org/package=shiftR Description: CRAN Package 'shiftR' (Fast Enrichment Analysis via Circular Permutations) Fast enrichment analysis for locally correlated statistics via circular permutations. The analysis can be performed at multiple significance thresholds for both primary and auxiliary data sets with efficient correction for multiple testing. 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Package: r-cran-shortuuid Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 278 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-shortuuid_0.1.1-1.ca2604.1_arm64.deb Size: 146352 MD5sum: 5874d52a6d9a00d6064d5aa27c27f8f8 SHA1: 50db5d28f8f9c8fd175c5778dddfb8b239ff4fb4 SHA256: c5d4e786d39ab3d801d3e83c455f173b82c5a65b96e8d4ceb3413af1d3afd496 SHA512: 8912b41e9f2869995608928411a6b64bca23bf7ac4a7d1276103efe795ed3e33e33ed8a508bd53f935f8edcdbfdaea3602eaa3a3ffea64ccf553dc9e679b96ba Homepage: https://cran.r-project.org/package=shortuuid Description: CRAN Package 'shortuuid' (Generate and Translate Standard UUIDs) Generate and translate standard Universally Unique Identifiers (UUIDs) into shorter - or just different - formats and back. Also implements base58 encoders and decoders. 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Package: r-cran-shp2graph Architecture: arm64 Version: 1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1907 Depends: libc6 (>= 2.35), r-base-core (>= 4.5.0), r-api-4.0, r-cran-igraph, r-cran-sp Filename: pool/dists/resolute/main/r-cran-shp2graph_1-0-1.ca2604.1_arm64.deb Size: 1846692 MD5sum: d2d79a262bb6fed84afa6dc9ebc8610d SHA1: a3576303295227bf6d90eb6178f7f5d2ae38c6f6 SHA256: 2c316a14f8d7715f1f1ae9bf3f87f091d5337e2bf65ad1a4dfd8ea19414acd50 SHA512: 7e37037a23d5cb26a3d84808714cbed00c2fffd35f50405458aaae270230282e0aff7f425621346848583bd7dc97864c92d2bc0c63750af7b369ff530207b5d4 Homepage: https://cran.r-project.org/package=shp2graph Description: CRAN Package 'shp2graph' (Convert a 'SpatialLinesDataFrame' -Class Object to an'igraph'-Class Object) Functions for converting and processing network data from a 'SpatialLinesDataFrame' -Class object to an 'igraph'-Class object. 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Package: r-cran-shrinkcovmat Architecture: arm64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1172 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_arm64.deb Size: 1024840 MD5sum: 11de73a61ab8426e87b647d375325c7c SHA1: 8863cb2b2919a4e002614718faec94c5f1cf398e SHA256: e63f3b0055e08d271a84428e053a9f5de35d76fe9291d6f4a5fe7e6c06b17c2a SHA512: 3d3c32ec610119520698824d169b2ba9e64f15f91c8eda562ebdcc6948f5b9e33474b86f5321bb2a6d1f3ec6cf0f7953bd5907f11432baf397e31ed9d7f58558 Homepage: https://cran.r-project.org/package=ShrinkCovMat Description: CRAN Package 'ShrinkCovMat' (Shrinkage Covariance Matrix Estimators) Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size. 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Details on the algorithms used are provided in Bitto and Frühwirth-Schnatter (2019) and Cadonna et al. (2020) and Knaus and Frühwirth-Schnatter (2023) . For details on the package, please see Knaus et al. (2021) . For the multivariate extension, see the 'shrinkTVPVAR' package. 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Details on the TVP-VAR-SV model and the shrinkage priors can be found in Cadonna et al. (2020) , details on the software can be found in Knaus et al. (2021) , while details on the dynamic shrinkage process can be found in Knaus and Frühwirth-Schnatter (2023) . Package: r-cran-sht Architecture: arm64 Version: 0.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 647 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 463456 MD5sum: cdca984cbf46ef0fa95df68e82210e8a SHA1: 408b12e5345a2560fee1db53c339eb86c528ea30 SHA256: 99c7c5430b32705c2076f70415e8b3e4fbfff72e3a262b151a3a597ec3a9c1ce SHA512: ab84666d724784c507a01f29c0c05f5478e818021abe531c72dcb01263664cfebc16e5526bad648e5498520da2f9560210cb66065d88d6846b21ac123969583a Homepage: https://cran.r-project.org/package=SHT Description: CRAN Package 'SHT' (Statistical Hypothesis Testing Toolbox) We provide a collection of statistical hypothesis testing procedures ranging from classical to modern methods for non-trivial settings such as high-dimensional scenario. For the general treatment of statistical hypothesis testing, see the book by Lehmann and Romano (2005) . Package: r-cran-siber Architecture: arm64 Version: 2.1.10-1.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_arm64.deb Size: 1629358 MD5sum: 2cbeac0e82a5d3ba35f72dd99ffb6c2c SHA1: 97ce8e8df61896f8847280f9bd1c1612d9aae096 SHA256: 25949e1fd097534ead8694562c73a7174a205e21828736880396e46e94599d25 SHA512: fd4189bafb6b2809a79750f80cc08b081990ef524947a75da580b2eda56caa2343a20697cb5e66478f8fa936858d39881dacb5eb46d175eefc118784839120cc Homepage: https://cran.r-project.org/package=SIBER Description: CRAN Package 'SIBER' (Stable Isotope Bayesian Ellipses in R) Fits bi-variate ellipses to stable isotope data using Bayesian inference with the aim being to describe and compare their isotopic niche. Package: r-cran-sieve Architecture: arm64 Version: 2.1-1.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-combinat, r-cran-glmnet, r-cran-mass, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-sieve_2.1-1.ca2604.1_arm64.deb Size: 147916 MD5sum: dd96aa9427454f421ef924548d82a984 SHA1: 4334558f2bfa971035dad7c52197c661955fd011 SHA256: fc6a75a17b7c0987f930dd73bf3fadb214b56b3a609064e2493f6f1e9a9a9bcf SHA512: a931224dd5563c9bb7b5e1d151cd9d6cd90beb2ccb04a0d604c14e2ecc21ba3afb14d1f11c27ff589776ec46957a4f17343e0e2b5f2a230537fa6870e57eea46 Homepage: https://cran.r-project.org/package=Sieve Description: CRAN Package 'Sieve' (Nonparametric Estimation by the Method of Sieves) Performs multivariate nonparametric regression/classification by the method of sieves (using orthogonal basis). The method is suitable for moderate high-dimensional features (dimension < 100). The l1-penalized sieve estimator, a nonparametric generalization of Lasso, is adaptive to the feature dimension with provable theoretical guarantees. We also include a nonparametric stochastic gradient descent estimator, Sieve-SGD, for online or large scale batch problems. Details of the methods can be found in: . Package: r-cran-sieveph Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 516 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_arm64.deb Size: 358608 MD5sum: ae39fa3debb83eda39c5af27b133478b SHA1: 55e8b76232a83034c067192ada50887152e981d4 SHA256: efa37bb41617c14c4143033adf5dd8c60dc0356319d599275e88a3ba17659783 SHA512: bf6a1e28c5e2fac03c38dbcf6fdb8c4808c9055daea0ea61f0c51718e08dbe369e3988813584745a6bac538c59faeaf53abb4bdaab682b322da56506713c2dda Homepage: https://cran.r-project.org/package=sievePH Description: CRAN Package 'sievePH' (Sieve Analysis Methods for Proportional Hazards Models) Implements a suite of semiparametric and nonparametric kernel-smoothed estimation and testing procedures for continuous mark-specific stratified hazard ratio (treatment/placebo) models in a randomized treatment efficacy trial with a time-to-event endpoint. Semiparametric methods, allowing multivariate marks, are described in Juraska M and Gilbert PB (2013), Mark-specific hazard ratio model with multivariate continuous marks: an application to vaccine efficacy. Biometrics 69(2):328-337 , and in Juraska M and Gilbert PB (2016), Mark-specific hazard ratio model with missing multivariate marks. Lifetime Data Analysis 22(4):606-25 . Nonparametric kernel-smoothed methods, allowing univariate marks only, are described in Sun Y and Gilbert PB (2012), Estimation of stratified mark‐specific proportional hazards models with missing marks. Scandinavian Journal of Statistics}, 39(1):34-52 , and in Gilbert PB and Sun Y (2015), Inferences on relative failure rates in stratified mark-specific proportional hazards models with missing marks, with application to human immunodeficiency virus vaccine efficacy trials. Journal of the Royal Statistical Society Series C: Applied Statistics, 64(1):49-73 . Both semiparametric and nonparametric approaches consider two scenarios: (1) the mark is fully observed in all subjects who experience the event of interest, and (2) the mark is subject to missingness-at-random in subjects who experience the event of interest. For models with missing marks, estimators are implemented based on (i) inverse probability weighting (IPW) of complete cases (for the semiparametric framework), and (ii) augmentation of the IPW estimating functions by leveraging correlations between the mark and auxiliary data to 'impute' the augmentation term for subjects with missing marks (for both the semiparametric and nonparametric framework). The augmented IPW estimators are doubly robust and recommended for use with incomplete mark data. The semiparametric methods make two key assumptions: (i) the time-to-event is assumed to be conditionally independent of the mark given treatment, and (ii) the weight function in the semiparametric density ratio/biased sampling model is assumed to be exponential. Diagnostic testing procedures for evaluating validity of both assumptions are implemented. Summary and plotting functions are provided for estimation and inferential results. Package: r-cran-sifinet Architecture: arm64 Version: 1.13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 415 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 182264 MD5sum: c0e95026cc7053db0a7cdadf5e377b39 SHA1: 9bf4e3095093f60ad5389874da113d3cfad30c50 SHA256: da87d918c918d3e88feec2e5335496d81e785a65f8ee8130df94fa705fa7d538 SHA512: f12d6ee28663065969f171e11356b79c0ad7e61c1198268098c347df347bbbf9db6ed64cb020b7b5351ebed87f70660c23dca40af6c21a110e16962191bc2f4c 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: arm64 Version: 2.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5263 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 4692642 MD5sum: c68545062e94a8d27f92f60cb201050c SHA1: 9ea2ae04c5700aee2268e5db785d3a21ac20d56b SHA256: 462eed7e1b910958e0c97f101b9cbc59f6a1eec4916a61eb52a8bcf7927c571e SHA512: 6ff1b742068873a3ac48727cf93fb476929dd2b6d7085d3d3219f675b0c990cba10e2826b5473bb5ab67bb445ce780b5946e4cdbc726de5671623e9bc4700422 Homepage: https://cran.r-project.org/package=sigminer Description: CRAN Package 'sigminer' (Extract, Analyze and Visualize Mutational Signatures for GenomicVariations) Genomic alterations including single nucleotide substitution, copy number alteration, etc. are the major force for cancer initialization and development. Due to the specificity of molecular lesions caused by genomic alterations, we can generate characteristic alteration spectra, called 'signature' (Wang, Shixiang, et al. (2021) & Alexandrov, Ludmil B., et al. (2020) & Steele Christopher D., et al. (2022) ). This package helps users to extract, analyze and visualize signatures from genomic alteration records, thus providing new insight into cancer study. Package: r-cran-sign Architecture: arm64 Version: 0.1.0-1.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_arm64.deb Size: 65328 MD5sum: c023f89d6aa97c8cfc31877638bec97d SHA1: 0bf7551ea77eb632aed99998efa28d3405e9ba3a SHA256: c78f7fbf2aa8f0b3e900f7acf314f695cd01e163f10c69c04359e97f2d01568b SHA512: 71c3b8489b23acc934c14658a574a114c42b3f325e09a1bf8c2585f1470684bb8e2b2f0b54fa5aa6e2fa1162dae1a7b94d7e544998dbac3ce7acf09327db1e0b 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. 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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. 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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) . Package: r-cran-simest Architecture: arm64 Version: 0.4-1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-nnls, r-cran-cobs Filename: pool/dists/resolute/main/r-cran-simest_0.4-1-1-1.ca2604.1_arm64.deb Size: 148786 MD5sum: b148dbf5bf3dfa1f7938e6521fd6b715 SHA1: 46522af0a54087697dd0688b879ac0348d6c88af SHA256: a06b327e9feb9653a88d65ff07af4ae7a005d0bd69e9510d44af4c4740a57b0a SHA512: bd584552bad5c8efc2e7b7beae7dbfbbf506aa2a70492d10f97e43e617abddc781125b20efd255c9cfdb4f01a30893704cb9cb01ae0d0027055edf008e85cfbf Homepage: https://cran.r-project.org/package=simest Description: CRAN Package 'simest' (Constrained Single Index Model Estimation) Estimation of function and index vector in single index model ('sim') with (and w/o) shape constraints including different smoothness conditions. See, e.g., Kuchibhotla and Patra (2020) . Package: r-cran-simeucartellaw Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 132 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-plot3d Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-simeucartellaw_1.0.4-1.ca2604.1_arm64.deb Size: 36848 MD5sum: 137040d0c39ae3b4b153415240bddbae SHA1: fc283626e8ec7e5bbc9bf42da31a11a48503494d SHA256: 1f54c2cbbc21500e036d9e5ff052b14fad62b7f2939d6085a280941a9b99645a SHA512: e1e144d693a10f3563a41a2c22200aa2a4073c80c14152f8a6049f0a5fb0cca1c78250570dd5b6003c418b7f6069abd8a0e07fd4d150319633d09721ab05d9f9 Homepage: https://cran.r-project.org/package=SimEUCartelLaw Description: CRAN Package 'SimEUCartelLaw' (Simulation of Legal Exemption System for European Cartel Law) Monte Carlo simulations of a game-theoretic model for the legal exemption system of the European cartel law are implemented in order to estimate the (mean) deterrent effect of this system. The input and output parameters of the simulated cartel opportunities can be visualized by three-dimensional projections. A description of the model is given in Moritz et al. (2018) . Package: r-cran-simevent Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 913 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-ggplot2, r-cran-survival, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-simevent_0.1.1-1.ca2604.1_arm64.deb Size: 658626 MD5sum: badb00637df1daf4c65bee43cb983c63 SHA1: c8e4a7b2a4540baea644db1fdaff7aae573717f9 SHA256: 443fea9676edcaab06038cbf8804ff67b29cc93f19f4e6d845d21285dba540d4 SHA512: f64aadfa693bc9733b16ca49656bc0c08d2c84d6667b206c8e8f46880ad605fe229987f22b36e000404f59f8bcce568786b65c77ef14163b1199c53bfb1fe043 Homepage: https://cran.r-project.org/package=simevent Description: CRAN Package 'simevent' (Simulation and Analysis of Event History Data) Simulate event history data from a framework where treatment decisions and disease progression are represented as counting process. The user can specify number of events and parameters of intensities thereby creating a flexible simulation framework. Package: r-cran-simexboost Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 81 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass Filename: pool/dists/resolute/main/r-cran-simexboost_0.2.0-1.ca2604.1_arm64.deb Size: 50596 MD5sum: 78a32ea9851346ac73990f7f9118ce75 SHA1: 449827af3c3f73ecbd4a503ffc7c537f0a529c14 SHA256: 3a686a35a8691975d060f61456142404baaaadfac952ebebcc9c95dab851636a SHA512: bd75ce83ed85d8d060073ac3c6a20b9f720dd7b3ad47bf720178f00096508364cbfdad076e3137f0b69868c7cb83d983a1dcc126dab9003c9b6ad97b65c69d05 Homepage: https://cran.r-project.org/package=SIMEXBoost Description: CRAN Package 'SIMEXBoost' (Boosting Method for High-Dimensional Error-Prone Data) Implementation of the boosting procedure with the simulation and extrapolation approach to address variable selection and estimation for high-dimensional data subject to measurement error in predictors. It can be used to address generalized linear models (GLM) in Chen (2023) and the accelerated failure time (AFT) model in Chen and Qiu (2023) . Some relevant references include Chen and Yi (2021) and Hastie, Tibshirani, and Friedman (2008, ISBN:978-0387848570). Package: r-cran-simfam Architecture: arm64 Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1813 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1286702 MD5sum: 1456259fae43f4118c4eddd049c160a0 SHA1: 0bbab0cbfff48124bb3ee9e78d18b28f834aeef8 SHA256: 77766557543a40886ddbf7c2c8de27fc6c497ec2661de913a30098d8103e0362 SHA512: d218924d5226d869e7203888a41a3fc21d30e7859cce7cca7c19b46542217a83c7d02a216f8ab99d136510667fa646f8beb88a1a061309c045c7f08e4e9aad69 Homepage: https://cran.r-project.org/package=simfam Description: CRAN Package 'simfam' (Simulate and Model Family Pedigrees with Structured Founders) The focus is on simulating and modeling families with founders drawn from a structured population (for example, with different ancestries or other potentially non-family relatedness), in contrast to traditional pedigree analysis that treats all founders as equally unrelated. Main function simulates a random pedigree for many generations, avoiding close relatives, pairing closest individuals according to a 1D geography and their randomly-drawn sex, and with variable children sizes to result in a target population size per generation. Auxiliary functions calculate kinship matrices, admixture matrices, and draw random genotypes across arbitrary pedigree structures starting from the corresponding founder values. The code is built around the plink FAM table format for pedigrees. Described in Yao and Ochoa (2022) . Package: r-cran-simframe Architecture: arm64 Version: 0.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2153 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1677288 MD5sum: 84999ade9ac518adb085e4eed7b23d00 SHA1: 5bf8ba8e26d0ec351d7bca050c188b330589588e SHA256: 2dd069fc6cf4474c55753dcaa8c9f20100766a5b62850aee7dc58dfbed04c51d SHA512: 8853717efcbbd44ef27c936b22b452d18d755fae94943509b8b4ea5fb9d148a01c507776698646e02685f97340c88e2854687f6020954a7755a7d90814bd89d0 Homepage: https://cran.r-project.org/package=simFrame Description: CRAN Package 'simFrame' (Simulation Framework) A general framework for statistical simulation, which allows researchers to make use of a wide range of simulation designs with minimal programming effort. The package provides functionality for drawing samples from a distribution or a finite population, for adding outliers and missing values, as well as for visualization of the simulation results. It follows a clear object-oriented design and supports parallel computing to increase computational performance. Package: r-cran-siminf Architecture: arm64 Version: 10.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4086 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_arm64.deb Size: 3341950 MD5sum: 65c242b23ddf965e55b9b0b3ebedff99 SHA1: ed714588db728890c83061b016c18e1f014a2e77 SHA256: 8a102df8887246ca23ca3e25461d85e77c4f3426129017281208bed6d44546ae SHA512: e3020cbeab38d56824b0d5866eb5275603a3d1a1b5235605d0dc2341ae9c38dfea46f062653453e6e19f501393301853b7248d90825920e82420a1d5e20620e0 Homepage: https://cran.r-project.org/package=SimInf Description: CRAN Package 'SimInf' (A Framework for Data-Driven Stochastic Disease SpreadSimulations) Provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations. The framework integrates infection dynamics in subpopulations as continuous-time Markov chains using the Gillespie stochastic simulation algorithm and incorporates available data such as births, deaths and movements as scheduled events at predefined time-points. Using C code for the numerical solvers and 'OpenMP' (if available) to divide work over multiple processors ensures high performance when simulating a sample outcome. One of our design goals was to make the package extendable and enable usage of the numerical solvers from other R extension packages in order to facilitate complex epidemiological research. The package contains template models and can be extended with user-defined models. For more details see the paper by Widgren, Bauer, Eriksson and Engblom (2019) . The package also provides functionality to fit models to time series data using the Approximate Bayesian Computation Sequential Monte Carlo ('ABC-SMC') algorithm of Toni and others (2009) or the Particle Markov Chain Monte Carlo ('PMCMC') algorithm of 'Andrieu' and others (2010) . Package: r-cran-simjoint Architecture: arm64 Version: 0.3.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 802 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 412688 MD5sum: 7e681ec1f3439c881b16d8f7a4cc2d83 SHA1: b875abf347b5534c44dad122a993f306bf63c1b8 SHA256: 29213fe51337caf2892287bbf07170be6bdf78408a8d5aa67caa4f9989c2ee48 SHA512: ab13ac0048c705aa8588f811fa73b544095c63a8776c4e65a4d2cc2f864016edd006074b25809a7634bc0b30f4628561cf9124d7b62f3de7a7e47ac2ca406b8f Homepage: https://cran.r-project.org/package=SimJoint Description: CRAN Package 'SimJoint' (Simulate Joint Distribution) Simulate multivariate correlated data given nonparametric marginals and their joint structure characterized by a Pearson or Spearman correlation matrix. The simulator engages the problem from a purely computational perspective. It assumes no statistical models such as copulas or parametric distributions, and can approximate the target correlations regardless of theoretical feasibility. The algorithm integrates and advances the Iman-Conover (1982) approach and the Ruscio-Kaczetow iteration (2008) . Package functions are carefully implemented in C++ for squeezing computing speed, suitable for large input in a manycore environment. Precision of the approximation and computing speed both substantially outperform various CRAN packages to date. Benchmarks are detailed in function examples. A simple heuristic algorithm is additionally designed to optimize the joint distribution in the post-simulation stage. The heuristic demonstrated good potential of achieving the same level of precision of approximation without the enhanced Iman-Conover-Ruscio-Kaczetow. The package contains a copy of Permuted Congruential Generator. Package: r-cran-simmer Architecture: arm64 Version: 4.4.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3001 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_arm64.deb Size: 1180444 MD5sum: e232e67f2beb52ba366288b1f0a7bb86 SHA1: cd1bfa6a163054b20e0b3605742df4641f0070bf SHA256: 5ed7321272c259e3e1f79d73ad3428de6dbe69f68885bc2c4c5e036c3c700d61 SHA512: 646355c99ac1d385c928a8b45c6a75a19a3cd66a2d647b934188c4330bb00c2e6a93658844ec62d3263bb0efe75ed90692087aafda25455373b8f0f105c1768b Homepage: https://cran.r-project.org/package=simmer Description: CRAN Package 'simmer' (Discrete-Event Simulation for R) A process-oriented and trajectory-based Discrete-Event Simulation (DES) package for R. It is designed as a generic yet powerful framework. The architecture encloses a robust and fast simulation core written in 'C++' with automatic monitoring capabilities. It provides a rich and flexible R API that revolves around the concept of trajectory, a common path in the simulation model for entities of the same type. Documentation about 'simmer' is provided by several vignettes included in this package, via the paper by Ucar, Smeets & Azcorra (2019, ), and the paper by Ucar, Hernández, Serrano & Azcorra (2018, ); see 'citation("simmer")' for details. Package: r-cran-simmr Architecture: arm64 Version: 0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2183 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_arm64.deb Size: 1270156 MD5sum: 00886dfd4a04908fb8469d86ae73aeb2 SHA1: 7bf60bdd9be5e054195ccf634ad5e0a6614ff84e SHA256: c27ca00bdc8aae73685ef240fdae8fc894820491ea3046741dc581b71d2df84c SHA512: 431d92442202ce00f70fe447ce08e848671f739531ef5f77b03afd9071c39da8915ede25cf8fb7669b76041539c769fa87099ada6942ac5aaacc1ca332964be6 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: arm64 Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3191 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2061738 MD5sum: 1b47745d2d4b4a3c15a661832f389aae SHA1: 52f18ff3509eb9b6ab866fd803830492a538da02 SHA256: eb1a33ff2faa43f582e9ebb710a6cacf46dfcc02b1ae2dc111b23e88afba7011 SHA512: 62f82b91d6b163471fd0c751a8ad5591f3bc49ff9639bb696a92336a4c5b57fcad9a0ceabf27b8c7fe6cad14b57f5cd6f3acc032a7c458d468e0973fc8f68ebf Homepage: https://cran.r-project.org/package=SIMplyBee Description: CRAN Package 'SIMplyBee' ('AlphaSimR' Extension for Simulating Honeybee Populations andBreeding Programmes) An extension of the 'AlphaSimR' package () for stochastic simulations of honeybee populations and breeding programmes. 'SIMplyBee' enables simulation of individual bees that form a colony, which includes a queen, fathers (drones the queen mated with), virgin queens, workers, and drones. Multiple colony can be merged into a population of colonies, such as an apiary or a whole country of colonies. Functions enable operations on castes, colony, or colonies, to ease 'R' scripting of whole populations. All 'AlphaSimR' functionality with respect to genomes and genetic and phenotype values is available and further extended for honeybees, including haplo-diploidy, complementary sex determiner locus, colony events (swarming, supersedure, etc.), and colony phenotype values. Package: r-cran-simpop Architecture: arm64 Version: 2.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3207 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_arm64.deb Size: 2940726 MD5sum: 0699fc81032cbffbc5f99a0e04776a8b SHA1: 813fcaddc6118226d99a854e5fe40cc8b34f8159 SHA256: e5e2da5646bd817dbee0b8831d891e803c7d58f8cd9fad6888cc3457847b5e1d SHA512: 21a85c8744bbd390c4a7cb69df318001b161eee49b0f76707e32f1c115f18d40455d035ee50edcc7a90739a116919ca2fb11daa19fe50a380155d822481adf1e Homepage: https://cran.r-project.org/package=simPop Description: CRAN Package 'simPop' (Simulation of Complex Synthetic Data Information) Tools and methods to simulate populations for surveys based on auxiliary data. The tools include model-based methods, calibration and combinatorial optimization algorithms, see Templ, Kowarik and Meindl (2017) ) and Templ (2017) . The package was developed with support of the International Household Survey Network, DFID Trust Fund TF011722 and funds from the World bank. Package: r-cran-simreg Architecture: arm64 Version: 3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 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_arm64.deb Size: 170088 MD5sum: c1a159eb8df88a274b923fb5987ec121 SHA1: 047f936506603841a6ed22ad23d1f8d663c18492 SHA256: 621d0e1c9207f5ab3a5adf9d4ad51d5b3dc2794fb8627c4b65f02cb86a2849ea SHA512: aa8703d2c7d9dfe31133e505e7aea819efebbd55f082e03c768445bb3973591d31304048ec164e8f610585bdca9d27d82fcc21016fb182ec16990ec05756c02b Homepage: https://cran.r-project.org/package=SimReg Description: CRAN Package 'SimReg' (Similarity Regression) Similarity regression, evaluating the probability of association between sets of ontological terms and binary response vector. A no-association model is compared with one in which the log odds of a true response is linked to the semantic similarity between terms and a latent characteristic ontological profile - 'Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases', Greene et al 2016 . Package: r-cran-simrestore Architecture: arm64 Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 906 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_arm64.deb Size: 483202 MD5sum: 244597e4c6d63b28a5e4b2a9e039a7e9 SHA1: f8865cb456061b7475272298418de50c79c74f5b SHA256: 8bfd0d242abfc51343755a22f7fc7a8372dd6121088931e03265f865871ef822 SHA512: cfb0c1b4efbeef577e912017904e7c01a34bb1cdcbf7eec2f8171e9acde3efc715e04e067aa3bfc6c5f3e3525cdd398588975fcfec39c7047c81ff85982d85ad Homepage: https://cran.r-project.org/package=simRestore Description: CRAN Package 'simRestore' (Simulate the Effect of Management Policies on RestorationEfforts) Simulation methods to study the effect of management policies on efforts to restore populations back to their original genetic composition. Allows for single-scenario simulation and for optimization of specific chosen scenarios. Further information can be found in Hernandez, Janzen and Lavretsky (2023) . Package: r-cran-simriv Architecture: arm64 Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2496 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1414344 MD5sum: 3fd9b7ed7950329cde77c0569d7ae511 SHA1: 82464f1f7919264d80da99fe2a2c06682e96954f SHA256: bd6d7794ce239dd6d1fb727f72d5cd42837274280f8930644b5d72cacd4b953c SHA512: 854e28459015e8ed0bcca3be16f17eb22e2da899c6fbf3359706a0c770750eaa8eee95e3876760a3bad069879c232e3631df75dd9530bf267e68bc0c82e7c0fa Homepage: https://cran.r-project.org/package=SiMRiv Description: CRAN Package 'SiMRiv' (Simulating Multistate Movements in River/HeterogeneousLandscapes) Provides functions to generate and analyze spatially-explicit individual-based multistate movements in rivers, heterogeneous and homogeneous spaces. This is done by incorporating landscape bias on local behaviour, based on resistance rasters. Although originally conceived and designed to simulate trajectories of species constrained to linear habitats/dendritic ecological networks (e.g. river networks), the simulation algorithm is built to be highly flexible and can be applied to any (aquatic, semi-aquatic or terrestrial) organism, independently on the landscape in which it moves. Thus, the user will be able to use the package to simulate movements either in homogeneous landscapes, heterogeneous landscapes (e.g. semi-aquatic animal moving mainly along rivers but also using the matrix), or even in highly contrasted landscapes (e.g. fish in a river network). The algorithm and its input parameters are the same for all cases, so that results are comparable. Simulated trajectories can then be used as mechanistic null models (Potts & Lewis 2014, ) to test a variety of 'Movement Ecology' hypotheses (Nathan et al. 2008, ), including landscape effects (e.g. resources, infrastructures) on animal movement and species site fidelity, or for predictive purposes (e.g. road mortality risk, dispersal/connectivity). The package should be relevant to explore a broad spectrum of ecological phenomena, such as those at the interface of animal behaviour, management, landscape and movement ecology, disease and invasive species spread, and population dynamics. Package: r-cran-simstatespace Architecture: arm64 Version: 1.2.16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 933 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_arm64.deb Size: 536756 MD5sum: d6d6abf11c5af9a76b0cd9411dbd5a11 SHA1: b6245c185112e3d53d1bb849c3442b2b224ab406 SHA256: a7809815a7f519b5674178faa76554b9e12d78652d03785089778c3c74c34007 SHA512: cc302ad2183e2d14908df577c7b244073226c630f8f36ae723e87b7cefd60b8911686a7af0aa27a1625d5e636279f9dfb42476b7825583c06fad46db19247734 Homepage: https://cran.r-project.org/package=simStateSpace Description: CRAN Package 'simStateSpace' (Simulate Data from State Space Models) Provides a streamlined and user-friendly framework for simulating data in state space models, particularly when the number of subjects/units (n) exceeds one, a scenario commonly encountered in social and behavioral sciences. This package was designed to generate data for the simulations performed in Pesigan, Russell, and Chow (2025) . Package: r-cran-simstudy Architecture: arm64 Version: 0.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2993 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_arm64.deb Size: 1527080 MD5sum: 59b6945914d06b41f17a5b8084cee7ab SHA1: 6327eb2b05bc25bf69951d829f6b7171c5c1be56 SHA256: aa911e280c523e12f38a9ceeaa8074986cb5f9c585ce10d75454eb302ddf636e SHA512: e921d75f78bf3b9698634eff7877d2ab4669ca48dc4ebbb52c01518c67fdb253687445af28554048f9d5bc4e7b91428f857993b044b5c577b8a0d86ed246dc08 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: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1003 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_arm64.deb Size: 395182 MD5sum: d47eeb23e1c5f8b83fb335f622a9c081 SHA1: f0e2cbc870aeac61b68d704a27b08c7dc873febe SHA256: 896e70ae83106cf32b3c4b1ebb53c5d25c405d21ac155b16188277988d5ca0d6 SHA512: f714ee99616528dbc4f68ee49772f4b0b148d49b8613d32b3558cffe7119f15355f6863991e1d07d498126167c590a55fc4cc729bf63b4243a3fb85c93bf442f Homepage: https://cran.r-project.org/package=SimTOST Description: CRAN Package 'SimTOST' (Sample Size Estimation for Bio-Equivalence Trials ThroughSimulation) Sample size estimation for bio-equivalence trials is supported through a simulation-based approach that extends the Two One-Sided Tests (TOST) procedure. The methodology provides flexibility in hypothesis testing, accommodates multiple treatment comparisons, and accounts for correlated endpoints. Users can model complex trial scenarios, including parallel and crossover designs, intra-subject variability, and different equivalence margins. Monte Carlo simulations enable accurate estimation of power and type I error rates, ensuring well-calibrated study designs. The statistical framework builds on established methods for equivalence testing and multiple hypothesis testing in bio-equivalence studies, as described in Schuirmann (1987) , Mielke et al. (2018) , Shieh (2022) , and Sozu et al. (2015) . Comprehensive documentation and vignettes guide users through implementation and interpretation of results. Package: r-cran-simtrial Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3094 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_arm64.deb Size: 914952 MD5sum: 9cd8f8db665bd3139168294f791eb58a SHA1: 9253b89e5c1082f599031112a8fd024996422884 SHA256: e519e18cd8b374e025f7efade7c40783117eea160a4dd5e2aea5c4400810c1d3 SHA512: 1e2fbbe0fc38706e2df2433c9260015e94ef9273f48ce7ab489397e36e2d2181bc41520aa8833c0f561424b5c0ec5864a8df0f6fdcbc8ba22cb83e0750d85162 Homepage: https://cran.r-project.org/package=simtrial Description: CRAN Package 'simtrial' (Clinical Trial Simulation) Provides some basic routines for simulating a clinical trial. The primary intent is to provide some tools to generate trial simulations for trials with time to event outcomes. Piecewise exponential failure rates and piecewise constant enrollment rates are the underlying mechanism used to simulate a broad range of scenarios such as those presented in Lin et al. (2020) . However, the basic generation of data is done using pipes to allow maximum flexibility for users to meet different needs. Package: r-cran-simts Architecture: arm64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3713 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_arm64.deb Size: 2280464 MD5sum: 6e71fe39b0139646feab81801fd78c93 SHA1: adf3507871273d83933b93869ca484963900f3cf SHA256: 73ec7e45140a18d85dc7ca3ff92e6f663c74e5f8e1897d92fb65bc8f6f2d2eff SHA512: bda0460c84c712efb2f88e7a0a13f4fca9b758d350a9c86abee30bd44da838f5448ffcd9caa298a41c8ef32cace40af5f8337645b8344dc28639fdc0e6e48c96 Homepage: https://cran.r-project.org/package=simts Description: CRAN Package 'simts' (Time Series Analysis Tools) A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) . More details can also be found in the paper linked to via the URL below. Package: r-cran-singr Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2867 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 2679288 MD5sum: daa3006fcb77a0eaea6d9f4b846aee5f SHA1: f8da13d61ae43f592b06b0bc56532f295a836b0e SHA256: e63ebd707af7aca63869bb8ebc091a96c8cf80ba597c63bfe42ec4365f5dc02c SHA512: 22ca9c613e22ce3866c7ddb97b4cae38280e9724564f0647aa803bb8a586db75641b59d7753aeb91a81a5b0cb1fd8fd193bd8e4a1da01b3441e751bdb62abd3f Homepage: https://cran.r-project.org/package=singR Description: CRAN Package 'singR' (Simultaneous Non-Gaussian Component Analysis) Implementation of SING algorithm to extract joint and individual non-Gaussian components from two datasets. SING uses an objective function that maximizes the skewness and kurtosis of latent components with a penalty to enhance the similarity between subject scores. Unlike other existing methods, SING does not use PCA for dimension reduction, but rather uses non-Gaussianity, which can improve feature extraction. Benjamin B.Risk, Irina Gaynanova (2021) . Package: r-cran-siphynetwork Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 848 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 540364 MD5sum: c494aa3d0aae6ec6d2bf3a01e5c7485e SHA1: 63a72e336f656cb1f0943f82f3e521eeafdd8c41 SHA256: ea4193e10b997a94a109376fd09336806536b4daa64fd341d497dd39d7af778e SHA512: e2e43be1b45a4cce702b81232de94dd7c544696f82f1c583c36347d7530dafb3ce6927f7d7ac46346a7c86cf8fe730cb3c587137238afd3c7109cbbe83538e7b Homepage: https://cran.r-project.org/package=SiPhyNetwork Description: CRAN Package 'SiPhyNetwork' (A Phylogenetic Simulator for Reticulate Evolution) A simulator for reticulate evolution under a birth-death-hybridization process. Here the birth-death process is extended to consider reticulate Evolution by allowing hybridization events to occur. The general purpose simulator allows the modeling of three different reticulate patterns: lineage generative hybridization, lineage neutral hybridization, and lineage degenerative hybridization. Users can also specify hybridization events to be dependent on a trait value or genetic distance. We also extend some phylogenetic tree utility and plotting functions for networks. We allow two different stopping conditions: simulated to a fixed time or number of taxa. When simulating to a fixed number of taxa, the user can simulate under the Generalized Sampling Approach that properly simulates phylogenies when assuming a uniform prior on the root age. Package: r-cran-sirmcmc Architecture: arm64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 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_arm64.deb Size: 98228 MD5sum: 7a9c2e163d556cf9a447175860983f89 SHA1: 73de1db6a3c3d1361360c08f5773d19d5a874412 SHA256: 74f48eb3d8306d1d07598a2e04f630265242615cb53b8efc6f99a4c374f7352b SHA512: 0f7db207b15051297205e44600f2a056566f20ce36dd9e2cf2b1e8f6719901030102be96b2beb2349a1f4fc983d37a9aeda5a03a9914e787092dbb9b089c8aa2 Homepage: https://cran.r-project.org/package=SIRmcmc Description: CRAN Package 'SIRmcmc' (Compartmental Susceptible-Infectious-Recovered (SIR) Model ofCommunity and Household Infection) We build an Susceptible-Infectious-Recovered (SIR) model where the rate of infection is the sum of the household rate and the community rate. We estimate the posterior distribution of the parameters using the Metropolis algorithm. Further details may be found in: F Scott Dahlgren, Ivo M Foppa, Melissa S Stockwell, Celibell Y Vargas, Philip LaRussa, Carrie Reed (2021) "Household transmission of influenza A and B within a prospective cohort during the 2013-2014 and 2014-2015 seasons" . Package: r-cran-sirt Architecture: arm64 Version: 4.2-133-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9249 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_arm64.deb Size: 8491584 MD5sum: 9ba8521b93f9a9d3735b62b6fd4ad5e6 SHA1: 39a911bea9d315ecc3d4d16614401c1147af969c SHA256: 3fd07a14db37e3fabdd69f84c80ecc5d299c3f7bd75ede590ab357002741b395 SHA512: fbc4b39b00532a2d392c994daa57b001f9eaffd1c402e354ef32af5be19f93d8923b21a7eb6eca83a0d2cf1106fe091251ac31b8aad3d4a2cecfcb3c120813a4 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: arm64 Version: 1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4025 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_arm64.deb Size: 3843526 MD5sum: 7857e4c9f32dc1a9f7335fbdfddbe704 SHA1: bfab7182e55ddfd878b11a883ae2f579eee04e12 SHA256: 7b0925bb70cb99fa432cacb75097e72944d782c3f7b6c0d6bb7f574c749a9452 SHA512: 0dd3e9146344b29df8da6c3a54d0fd7e4907440535f494a2ee542a94708a04dfc9ae0f1c7babe033494538047b4560f344836e962d166c8c3d1ac7d12219a5d0 Homepage: https://cran.r-project.org/package=SIS Description: CRAN Package 'SIS' (Sure Independence Screening) Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Through this publicly available package, we provide a unified environment to carry out variable selection using iterative sure independence screening (SIS) (Fan and Lv (2008)) and all of its variants in generalized linear models (Fan and Song (2009)) and the Cox proportional hazards model (Fan, Feng and Wu (2010)). Package: r-cran-sisireg Architecture: arm64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-zoo, r-cran-reticulate Filename: pool/dists/resolute/main/r-cran-sisireg_1.2.1-1.ca2604.1_arm64.deb Size: 194764 MD5sum: 4a6d409c21754e305d3705d570b409c5 SHA1: 33f081f40a5e4a98674c1b29a6acf0356c8365f8 SHA256: 5a50caa679cd8719dacf2fae4248488b6cf08c8bf6e0eb0dda61d774d942f492 SHA512: b8eea3994264519a284f8777fe9c455978414d8f26d575734f9aa4202f1bdea90ddd3760e01e32b661de0ce855ae0c6f2a9284ffb0f5b1cd58c28f0153f48ff6 Homepage: https://cran.r-project.org/package=sisireg Description: CRAN Package 'sisireg' (Sign-Simplicity-Regression-Solver) Implementation of the SSR-Algorithm. The Sign-Simplicity-Regression model is a nonparametric statistical model which is based on residual signs and simplicity assumptions on the regression function. Goal is to calculate the most parsimonious regression function satisfying the statistical adequacy requirements. Theory and functions are specified in Metzner (2020, ISBN: 979-8-68239-420-3, "Trendbasierte Prognostik") and Metzner (2021, ISBN: 979-8-59347-027-0, "Adäquates Maschinelles Lernen"). Package: r-cran-sit Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 48436 MD5sum: 07b6d5edc808b6249b26a64cef1ba13b SHA1: eb9d1aa424207cbea2a6f546caf885e6f9eb4807 SHA256: 2c052e41304ff59fe4a4764f06c3e83c8715d51eeaad8afa4d843a30d6fed966 SHA512: 695ef20b2211d0ae93b636d2f36438d1b809420a66649dc13e94c398da738587f8f49ce7c18b52406ea59c3cb7b591d2023a29b520f8208e12047a70ddf92adb Homepage: https://cran.r-project.org/package=SIT Description: CRAN Package 'SIT' (Association Measurement Through Sliced Independence Test (SIT)) Computes the sit coefficient between two vectors x and y, possibly all paired coefficients for a matrix. The reference for the methods implemented here is Zhang, Yilin, Canyi Chen, and Liping Zhu. 2022. "Sliced Independence Test." Statistica Sinica. . This package incorporates the Galton peas example. Package: r-cran-sith Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1009 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 584540 MD5sum: 6acf1b4773fcc6a6fa75dfe269505bc1 SHA1: 137fd32a0870dff13fc01855f0bd50d5e2763022 SHA256: 602a9cef44823ce9e0aa05bdd884ce7f0ce358c53e14c9a45c8abc99338f1a5c SHA512: efda27bbc6acf47e8f12e3e176b483d9b6870bce8add7b6f72717be0cfc13e0350450467c4ef19c2741f7a99bde0f267ffa56d27862ccfc07312c20c9ebd10ed Homepage: https://cran.r-project.org/package=SITH Description: CRAN Package 'SITH' (A Spatial Model of Intra-Tumor Heterogeneity) Implements a three-dimensional stochastic model of cancer growth and mutation similar to the one described in Waclaw et al. (2015) . Allows for interactive 3D visualizations of the simulated tumor. Provides a comprehensive summary of the spatial distribution of mutants within the tumor. Contains functions which create synthetic sequencing datasets from the generated tumor. Package: r-cran-sitmo Architecture: arm64 Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 955 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 130242 MD5sum: 7a857379470521749a389bb8d4c751c6 SHA1: bb107ef879cfebb5c1445e1c175db7a3c411bc9b SHA256: 69880e6b69e1c1d2af1f89cf5386e3d04c79b52f3352aa4b446bf8ed7bbd38f4 SHA512: b83046b7f7d1e6b2078e15bed10f22f8da2f722759e70c5e12cf0724976a9b1e701d7f50c66a746307089f7244f8d0d87b88b1aaf4fc59edf5b9dfee4f4da76a Homepage: https://cran.r-project.org/package=sitmo Description: CRAN Package 'sitmo' (Parallel Pseudo Random Number Generator (PPRNG) 'sitmo' HeaderFiles) Provided within are two high quality and fast PPRNGs that may be used in an 'OpenMP' parallel environment. In addition, there is a generator for one dimensional low-discrepancy sequence. The objective of this library to consolidate the distribution of the 'sitmo' (C++98 & C++11), 'threefry' and 'vandercorput' (C++11-only) engines on CRAN by enabling others to link to the header files inside of 'sitmo' instead of including a copy of each engine within their individual package. Lastly, the package contains example implementations using the 'sitmo' package and three accompanying vignette that provide additional information. Package: r-cran-sits Architecture: arm64 Version: 1.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4073 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_arm64.deb Size: 2957872 MD5sum: aed8afdba5c67d1a511201c41eb5c513 SHA1: 654ac6297988b4ebd314a4412dc1ce57333fd97e SHA256: f269ca63ac672305e4b1b6cab6ff9d7f74d6fab8ef9a6e5dffefe0d0e36b7dcc SHA512: a0db231f3ceb9fa2121babc1aa7d3ca29c144856785f8cc686764c8f19a1b7fa73460e25e9194d7471478824ffacd7a3bb16fdb39921c5420ef845ee2d52f011 Homepage: https://cran.r-project.org/package=sits Description: CRAN Package 'sits' (Satellite Image Time Series Analysis for Earth Observation DataCubes) An end-to-end toolkit for land use and land cover classification using big Earth observation data. Builds satellite image data cubes from cloud collections. Supports visualization methods for images and time series and smoothing filters for dealing with noisy time series. Enables merging of multi-source imagery (SAR, optical, DEM). Includes functions for quality assessment of training samples using self-organized maps and to reduce training samples imbalance. Provides machine learning algorithms including support vector machines, random forests, extreme gradient boosting, multi-layer perceptrons, temporal convolution neural networks, and temporal attention encoders. Performs efficient classification of big Earth observation data cubes and includes functions for post-classification smoothing based on Bayesian inference. Enables best practices for estimating area and assessing accuracy of land change. Includes object-based spatio-temporal segmentation for space-time OBIA. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core. Package: r-cran-sk4fga Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 964 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 902176 MD5sum: 8ea47c764b7fc0886c3d8bbd6ba3f016 SHA1: c8e181c96139276a3b22e47b9204a3666540d5d5 SHA256: b9479cb8e9042d710b698242e30c7f99200e60dd73e0dcdfc16ba9679b059e64 SHA512: 2a24fdcff3ec52e281c79d772a67a1ed298a562613bfb92e387040d9c70eeff3f3ff554703533a6efb10b5abeef3845fa5b3d773e420cde2f19b66650cb65b36 Homepage: https://cran.r-project.org/package=SK4FGA Description: CRAN Package 'SK4FGA' (Scott-Knott for Forensic Glass Analysis) In forensics, it is common and effective practice to analyse glass fragments from the scene and suspects to gain evidence of placing a suspect at the crime scene. This kind of analysis involves comparing the physical and chemical attributes of glass fragments that exist on both the person and at the crime scene, and assessing the significance in a likeness that they share. The package implements the Scott-Knott Modification 2 algorithm (SKM2) (Christopher M. Triggs and James M. Curran and John S. Buckleton and Kevan A.J. Walsh (1997) "The grouping problem in forensic glass analysis: a divisive approach", Forensic Science International, 85(1), 1--14) for small sample glass fragment analysis using the refractive index (ri) of a set of glass samples. It also includes an experimental multivariate analog to the Scott-Knott algorithm for similar analysis on glass samples with multiple chemical concentration variables and multiple samples of the same item; testing against the Hotellings T^2 distribution (J.M. Curran and C.M. Triggs and J.R. Almirall and J.S. Buckleton and K.A.J. Walsh (1997) "The interpretation of elemental composition measurements from forensic glass evidence", Science & Justice, 37(4), 241--244). Package: r-cran-skat Architecture: arm64 Version: 2.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1450 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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_arm64.deb Size: 1313268 MD5sum: 13f39f600fd48d7f3822b54d5a4d641e SHA1: 05d48ff8decbfe75d2e24329911ba3bcc8495d39 SHA256: e198817a2a905df05624919bf02bb0ccb6a30554bb7b11e841dae85fb2ef1554 SHA512: 063309c97008a396b0a8d617e97f764f15d9baebc6804004c505c52f0744cfabb121c5a7881dd21b8f2442bd2b42fd8c1fbbec02d903d7f7118e7e6e19553d9f Homepage: https://cran.r-project.org/package=SKAT Description: CRAN Package 'SKAT' (SNP-Set (Sequence) Kernel Association Test) Functions for kernel-regression-based association tests including Burden test, SKAT and SKAT-O. These methods aggregate individual SNP score statistics in a SNP set and efficiently compute SNP-set level p-values. Package: r-cran-sketching Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2418 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1799978 MD5sum: 470a1455c4d6134da2e5e289d4cc2cfc SHA1: d7d9b414317567f29189612db539fa830f079a51 SHA256: c2a29a8a73c25e17a4132c5badc8f38b6cccb003f43a74676975319deb38a919 SHA512: a61596af787ca040428b90051b8290d97f665cd403e5231a790cb7cceb0d74a22f733c248125b005b577154b577dc501f2cdd5b1d71e04a9cc79fad0c81f1143 Homepage: https://cran.r-project.org/package=sketching Description: CRAN Package 'sketching' (Sketching of Data via Random Subspace Embeddings) Construct sketches of data via random subspace embeddings. For more details, see the following papers. Lee, S. and Ng, S. (2022). "Least Squares Estimation Using Sketched Data with Heteroskedastic Errors," Proceedings of the 39th International Conference on Machine Learning (ICML22), 162:12498-12520. Lee, S. and Ng, S. (2020). "An Econometric Perspective on Algorithmic Subsampling," Annual Review of Economics, 12(1): 45–80. Package: r-cran-skfcpd Architecture: arm64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 483 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_arm64.deb Size: 237772 MD5sum: cedf950838fb6c84f6e086c5843e65ba SHA1: 3fc567c62f7452c7f43d7c0917286f9d45f2f855 SHA256: eb898352ca1c16cf76d5e82dcf70935e090695455e675826363c61669a0c2d75 SHA512: 35908c80c10fd2a1209c5ea57dfbedc2bc2526b127674a0aae665015bcb9c241ea37859402d0bd4d2ffdbd6da45d76af37360660a2abc096858a67553c578ea7 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. It enables fast single and multiple change points with missing values. See the reference: Hanmo Li, Yuedong Wang, Mengyang Gu (2023), . Package: r-cran-sklarsomega Architecture: arm64 Version: 3.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 624 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_arm64.deb Size: 531658 MD5sum: 717f89c58cbdf8e2c993377c98d51a25 SHA1: 0c28aa38fbafdba96a8372244dff6d553de747ea SHA256: fe797bee200768d3f46dbfeb4d34e9d2ed95383781d29505ee82d340e2a8d34d SHA512: db8320e97dc6b29982e5acd1ee6a635ef30cc1fe093b5ec70199f987916fbecdd735cd56073bc8b91993761c4dff41efa8c09a3fa0e76a012514cf13f5b4f43a 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: arm64 Version: 1.9.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1307 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_arm64.deb Size: 833880 MD5sum: fe8adb76a81d2f3aaaf66f5ce91e7a2a SHA1: 662fe750da778bdc7be6b1b20b67ddde13a59c07 SHA256: a78f343f11861092e6da46551eeed4501a779905ed1eb6b9a3e36809b880c95b SHA512: 6a8e2f6606637fe2b9431b328749aa1a062cf2f4753fc871afdf2f3bed73b59a984372e07922ab5f6472fd6dfd8d21001828702f8aaf78b3ef5582a321bdc4d1 Homepage: https://cran.r-project.org/package=skpr Description: CRAN Package 'skpr' (Design of Experiments Suite: Generate and Evaluate OptimalDesigns) Generates and evaluates D, I, A, Alias, E, T, and G optimal designs. Supports generation and evaluation of blocked and split/split-split/.../N-split plot designs. Includes parametric and Monte Carlo power evaluation functions, and supports calculating power for censored responses. Provides a framework to evaluate power using functions provided in other packages or written by the user. Includes a Shiny graphical user interface that displays the underlying code used to create and evaluate the design to improve ease-of-use and make analyses more reproducible. For details, see Morgan-Wall et al. (2021) . Package: r-cran-skylight Architecture: arm64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 570 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_arm64.deb Size: 266974 MD5sum: 4627d01dba56a7a4f7762f42354869ef SHA1: d37c8fc47b53f0bfbe0aa4ebbbec180040155a46 SHA256: c523da325535fd43ecf86a27253045eaa7d158087c46e74a68a44ad94acf2a15 SHA512: f59dec6f7634e807a18335aae5506bb0703ff7dff99dbb05c968e7038767b2194504a6751b5eace5be2065588e3c93e7b3d35d0583af549081f6b266ec341970 Homepage: https://cran.r-project.org/package=skylight Description: CRAN Package 'skylight' (A Simple Sky Illuminance Model) A tool to calculate sky illuminance values (in lux) for both sun and moon. The model is a translation of the Fortran code by Janiczek and DeYoung (1987) . Package: r-cran-slam Architecture: arm64 Version: 0.1-55-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-slam_0.1-55-1.ca2604.1_arm64.deb Size: 185702 MD5sum: b92b7bd6d0b39b685feffbadc78c9f52 SHA1: d9043c13abdcd32961a835939d4498f772293562 SHA256: ac9bab79bc91f913cd860d228c9fd80aaf577a2eb5b5c160602d8e60ebf90d84 SHA512: c63c6a504ec0c5cb768a8b3144ead9e25af1abb81f01492fed4755209bf1ccdd215c6f18d5e08c2ac8224e706ec5cb4a9146b0d2d69692259489adae4ebbfe05 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. Package: r-cran-slasso Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3161 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-inline, r-cran-rcpp, r-cran-fda, r-cran-fda.usc, r-cran-matrixcalc, r-cran-matrixstats, r-cran-mass, r-cran-plot3d, r-cran-cxxfunplus, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-slasso_1.0.1-1.ca2604.1_arm64.deb Size: 2979606 MD5sum: f725a483a74f390d177eddd6681ede38 SHA1: a329f005ec7cdee2ee41273398a9bbf53b3e6d12 SHA256: dd6e15bbea5e808dadc9bbe960d6bd86d82993f7faa7ddef79356bf1a4d93e5e SHA512: 235ceabd5d46775806707086bef7866f433e68c656d7675fb0936a2f6ebdecc4f80129368c48cb7b5214e879d27cbd2eb46e34cab369858322ec982dc0f7e0ae Homepage: https://cran.r-project.org/package=slasso Description: CRAN Package 'slasso' (S-LASSO Estimator for the Function-on-Function Linear Regression) Implements the smooth LASSO estimator for the function-on-function linear regression model described in Centofanti et al. (2022) . Package: r-cran-slca Architecture: arm64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4587 Depends: libc6 (>= 2.43), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-diagrammer, r-cran-magrittr, r-cran-mass, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-slca_1.4.0-1.ca2604.1_arm64.deb Size: 4240352 MD5sum: 3e8c8580b566a3ee45b479494bdb8077 SHA1: c3190bdbd1aa376dab2d67b920d4cd0f1e56df1d SHA256: 37b7913a6bf472b445c16141501a0daa916753ea87290f708de815cd5570291f SHA512: c374e9cd5d616ed53f44c21ebf227b5f3845fc5ebacaed63138237c03a1a61e2178e7164b101826a6fcee9827613ca79dd7c63a7c04e11f5c0b94cb9a52e352d Homepage: https://cran.r-project.org/package=slca Description: CRAN Package 'slca' (Structural Modeling for Multiple Latent Class Variables) Provides comprehensive tools for the implementation of Structural Latent Class Models (SLCM), including Latent Transition Analysis (LTA; Linda M. 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Package: r-cran-slcm Architecture: arm64 Version: 0.1.1-1.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), libgomp1 (>= 4.2.1), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-edmdata, r-cran-rcpparmadillo Suggests: r-cran-altdoc Filename: pool/dists/resolute/main/r-cran-slcm_0.1.1-1.ca2604.1_arm64.deb Size: 142080 MD5sum: c3bfe7da00abc5fd34e6de73473382a5 SHA1: aac63ff446b4ba8e9a1651d15e678a91ad2ea962 SHA256: 3de65c4093d1070bcac8f48a5fa907a2f72e026ec717c9509b78f24f2be53692 SHA512: b89e51a7199edaf3374a0325293d4346e4779443c504b09680d74479504bdc956e11649bee6e397e2bf758f9e7ccdcb6d0414125dae6cc5718c85cab6ded133a Homepage: https://cran.r-project.org/package=slcm Description: CRAN Package 'slcm' (Sparse Latent Class Model for Cognitive Diagnosis) Perform a Bayesian estimation of the exploratory Sparse Latent Class Model for Binary Data described by Chen, Y., Culpepper, S. A., and Liang, F. (2020) . Package: r-cran-sld Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 159 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 58838 MD5sum: 32659b7d94a2e48a3422f66c692bb7f7 SHA1: 5d5c10802c19cde6ee946ac4be30ae28e59e1d2b SHA256: f78d48de14a5757ef5dbd45854ac2bb105e4c71e9f881b84545619393c157cb2 SHA512: 558fa35bd7c98ab722a9e1f802d5840e0996218defce3583967a743e4b01afc1bada6d49386e274f1e1708661e41e0bef23fdca66b426c9352ad01f5adcc4f74 Homepage: https://cran.r-project.org/package=sld Description: CRAN Package 'sld' (Estimation and Use of the Quantile-Based Skew LogisticDistribution) The skew logistic distribution is a quantile-defined generalisation of the logistic distribution (van Staden and King 2015). 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Package: r-cran-slfm Architecture: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 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_arm64.deb Size: 84220 MD5sum: 6a2e72737b6acb9a22ea3ffc211bf8ef SHA1: 26e6e6a0d5d2beab15c950a89b3c4b1f149e138d SHA256: 9da381a1e64a7c61c45bbfa240639e747b5016d874b287930679acc9382621f5 SHA512: 9120a12afcf40ee54c78eb8fdd76e3a686e71f4b49c0c0e50de6d8291826b0d5ef14c4df227be3cff76bddcb5511da5b2226b1fb603ead9e129030cdcc4d3a7a 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: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3596 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_arm64.deb Size: 2085210 MD5sum: 9bd4cac32874af389f95632578c7940e SHA1: c7a3b647ac5b9cb2e53e52df24b6ca36503626d8 SHA256: 7f7a1bb3e3cb11af247edcc51732a40951127329fdd260196eee4417e0fb272e SHA512: 39b1285cf0252ba0cba5421a4ea374ed373fabf48e51824eb8cfda4e05df852b6ee61c9940e2b7129c984711cac6c2bc2cd231cc7377f1390f69e5184a692737 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: arm64 Version: 2.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 114 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_arm64.deb Size: 24586 MD5sum: 61b9dc0234fb5a87852202945f0aa635 SHA1: 412c6179e3c9dea3c88d1975b5ed025aa864e30c SHA256: b9ce50d2dd83668eb6489d78421345bccf70679ee16fe23484a7e351b67d12dc SHA512: 461afd533544206144c0bfb414992a95253713fcdddf0335c66103cbf1449f9b24dd1aff3d25e8f5273184ff67b8681e9a1521f057c5afc4f7a6c758943e37af Homepage: https://cran.r-project.org/package=SLHD Description: CRAN Package 'SLHD' (Maximin-Distance (Sliced) Latin Hypercube Designs) Generate the optimal Latin Hypercube Designs (LHDs) for computer experiments with quantitative factors and the optimal Sliced Latin Hypercube Designs (SLHDs) for computer experiments with both quantitative and qualitative factors. Details of the algorithm can be found in Ba, S., Brenneman, W. A. and Myers, W. R. (2015), "Optimal Sliced Latin Hypercube Designs," Technometrics. Important function in this package is "maximinSLHD". Package: r-cran-slideimp Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 849 Depends: libblas3 | libblas.so.3, libc6 (>= 2.34), libgcc-s1 (>= 4.2), 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_arm64.deb Size: 417090 MD5sum: fc8e5b4b507a28b95ff64287bc3009b4 SHA1: de3009a9dff9494fb2f23cf9e130ddf73f34652d SHA256: 58d94a41fb14ccd30424627d379e979fed20df7f71aa64e77d10b2b9049b7bad SHA512: f6b091d5a3a163964c76aa5971aec8cd5a55f86983d5a8a24f4f26de9b6f915fdf202e6a85868af10d6d2df9db9fcf40bb52dbc77ecbd51760a9f5a32f314ac6 Homepage: https://cran.r-project.org/package=slideimp Description: CRAN Package 'slideimp' (Numeric Matrices K-NN and PCA Imputation) Fast k-nearest neighbors (K-NN) and principal component analysis (PCA) imputation algorithms for missing values in high-dimensional numeric matrices, i.e., epigenetic data. For extremely high-dimensional data with ordered features, a sliding window approach for K-NN or PCA imputation is provided. Additional features include group-wise imputation (e.g., by chromosome), hyperparameter tuning with repeated cross-validation, multi-core parallelization, and optional subset imputation. The K-NN algorithm is described in: Hastie, T., Tibshirani, R., Sherlock, G., Eisen, M., Brown, P. and Botstein, D. (1999) "Imputing Missing Data for Gene Expression Arrays". The PCA imputation is an optimized version of the imputePCA() function from the 'missMDA' package described in: Josse, J. and Husson, F. (2016) "missMDA: A Package for Handling Missing Values in Multivariate Data Analysis". 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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. 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Kernel-based non-parametric methods for density/regression estimation and numerical routines for empirical likelihood maximisation are implemented in 'Rcpp' for speed. Package: r-cran-smoothhazard Architecture: arm64 Version: 2025.07.24-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: libc6 (>= 2.38), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-prodlim, r-cran-lava, r-cran-mvtnorm Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-smoothhazard_2025.07.24-1.ca2604.1_arm64.deb Size: 261002 MD5sum: 6abea4e33082d06b1da4d5e11c2303cc SHA1: 8ca48eee2c43a713220e78a9df39ce11de1689cc SHA256: 85b5b84af367788e604c0d84db501e3e4920297c061f46b79a63871906d99fc3 SHA512: f5e4358c456f980a4645f54dd50d55ba48dabfd7dc7c1f5a484a6c2d2b3af59115a85ad51c83d400c0ce3401f96ddb2b4a265732d7357c9e2297f33740567bb0 Homepage: https://cran.r-project.org/package=SmoothHazard Description: CRAN Package 'SmoothHazard' (Estimation of Smooth Hazard Models for Interval-Censored Data) Estimation of two-state (survival) models and irreversible illness- death models with possibly interval-censored, left-truncated and right-censored data. Proportional intensities regression models can be specified to allow for covariates effects separately for each transition. We use either a parametric approach with Weibull baseline intensities or a semi-parametric approach with M-splines approximation of baseline intensities in order to obtain smooth estimates of the hazard functions. Parameter estimates are obtained by maximum likelihood in the parametric approach and by penalized maximum likelihood in the semi-parametric approach. 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The estimation is conducted via local polynomial regression using an automatically selected bandwidth obtained by a built-in iterative plug-in algorithm or a bandwidth fixed by the user. A Nadaraya-Watson kernel smoother is also built-in as a comparison. With version 1.1.0, a linearity test for the trend function, forecasting methods and backtesting approaches are implemented as well. The smoothing methods of the package are described in Feng, Y., Gries, T., and Fritz, M. (2020) . Package: r-cran-smr Architecture: arm64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 219 Depends: libc6 (>= 2.17), 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-smr_2.1.0-1.ca2604.1_arm64.deb Size: 79990 MD5sum: 415c269973142e88ddda908688442ff8 SHA1: 78d42ac4af305390c46c3cd61e7d4edeb3fbd76c SHA256: 82c8bbee3e36be62479447fd501ae2e0873fd7dfaf7b5558bc49b4fe6f00226f SHA512: eb43f7d62c9f6d77366a852ad910d26b60b343c61a968f750fe0969f7f8f98cfda48b44b139f326c5a4288b8be8e1d8c26c13a1bc0fbdd06906499d2986044df Homepage: https://cran.r-project.org/package=SMR Description: CRAN Package 'SMR' (Externally Studentized Midrange Distribution) Computes the studentized midrange distribution (pdf, cdf and quantile) and generates random numbers. 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Supports both standard image arrays and geospatial raster objects, with a design that can be extended to other spatial data frameworks. The algorithm groups adjacent pixels into compact, coherent regions based on spectral similarity and spatial proximity. A high-performance implementation supports images with arbitrary spectral bands. Package: r-cran-snowballc Architecture: arm64 Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 770 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-snowballc_0.7.1-1.ca2604.1_arm64.deb Size: 355180 MD5sum: 6ebf9695e46e989067f6faabb5bf63f0 SHA1: 8a9ff161a93d53fc2e505e39868fc1c4cce06aee SHA256: 7c6fc17d9da6744167c57dbd47fbabeecfca3a3fba1609a27b458a735136353f SHA512: 5d22e26a144a695d9364e434536bc99865d003bd809907546291466c21daef290c0183bc95dc4fa00124edad2ffd04cb2e7e20631ad32e6812dc3d266981ef92 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: arm64 Version: 1.0.2-1.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-igraph, r-cran-rcpp, r-cran-rdpack Filename: pool/dists/resolute/main/r-cran-snowboot_1.0.2-1.ca2604.1_arm64.deb Size: 226954 MD5sum: 6a7e19f774f8c6f170b7a3ca6c9c2a1d SHA1: 0728e0d5f34da18e3e535c643ff0d04606ecc796 SHA256: cc5c989b1b0e46739f668b3fa16889664c8249774dc597afc5ccc328199d3764 SHA512: ac7d5a14e1a4004d7d9208cd063f5295774dd76c719686e46b9e32cdb1ddf5606f6f5049a505d5d66e52c08261c4e6c287ce838c8f9bcbaaf0c07ecb1993873e 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. The non-parametric methods of analysis center on snowball and bootstrap sampling for estimating functions of network degree distribution. For other parameters of interest, see, e.g., 'bootnet' package. Package: r-cran-snpassoc Architecture: arm64 Version: 2.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1597 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1374266 MD5sum: 0016afb8fc900d72f398cc83dcda0edc SHA1: 3c2a397614e029bcf81bf559fbf0bba4edf91aec SHA256: b1a627097fe322817563c6f2646f8219e3ea66e80acc1f16bd92af047665b63a SHA512: 6c6475bb06b6bc97fdd9998ba9c1ce2925f3b4eefcfbfd5f2c0fcac59a48401fb06f6a1c74598c8a19a0eacee67fe1dc88e7f66069c57ab507c6eb2e2619ea73 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: arm64 Version: 0.18.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 375 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 283628 MD5sum: 82d754a3d2e4db721134a772f859a541 SHA1: d8e7568eed28e5e7f26d9c9f7a3ec2f660d4e13d SHA256: 9ea49dd2bd9c231564b6e782b98848db3b942b5907fa9ce815e7361c78bed4d3 SHA512: ccdd248d4a4918bd9d5e6c38aee74e9b4dc1256a73a8e0b5136670ff3533021e1a7821b0f67ee32d959e42893cf464cf0a30b1cf87b789335ca25fd228295f1f 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: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1279 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_arm64.deb Size: 969874 MD5sum: 06f8ee71e07e90dd0353a3040728b441 SHA1: 2a1cd5dfe9f8ed7c00ab8f9dcc7900f0ba381a96 SHA256: 9fa281e167d70a5ca030754c6cd9a41cb0baf7747ef6cd38ae015ab758918aef SHA512: c060443fe7c6640526e5e1264b7f6616d2957c9f6ba4960f4445ef43c8a299a9f910f2522070184bd23e822d208d4e7d94d33e2fdeec35d70fab37d5615bbd97 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: arm64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 635 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 377972 MD5sum: 02242400de84858cf747f8eb9f7f0d4d SHA1: e4a124781856f4749e23964e3ebfe87ce93b91c0 SHA256: 7cd4fbba4ba2eb2a45e7661dd30071cfad0565fde9be7666433cb46966566d43 SHA512: 9eeda618b426c13ea26fde91332b7542fa1766068dcef064f829d25498874df194196cf7a5872998b55b9727ce16252bc547ad335f371e957d5206984c10bc87 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: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 492 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 230404 MD5sum: 97052ffa912564d451955858b1f3cb78 SHA1: 9f096759b536006fb4b073f26ad7fb20caf6679d SHA256: 98b10a834cd148ed7dcc99961f355ab1aeb59eaa50e7efaf9ade7316f3bf1aaf SHA512: 042d8e842373316d8d014da3a79396d828ba53bd386af9a0ec9de4cb0064d49916963902ad44d4b2ba806e902ed244b0ca5ccec42593bd58d0661e2d92ab0a6e Homepage: https://cran.r-project.org/package=SNSeg Description: CRAN Package 'SNSeg' (Self-Normalization(SN) Based Change-Point Estimation for TimeSeries) Implementations self-normalization (SN) based algorithms for change-points estimation in time series data. This comprises nested local-window algorithms for detecting changes in both univariate and multivariate time series developed in Zhao, Jiang and Shao (2022) . Package: r-cran-sobol4r Architecture: arm64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2696 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-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_arm64.deb Size: 1396482 MD5sum: afb3a79885518be465a5c34fccb60b90 SHA1: 4c62324b96bca417df07583de080ce1c2d33d654 SHA256: a8bdda5cec1e5edbce33ab8eea463ebdddd18b2cc162afd229e75ef368091013 SHA512: ce59de4718f21a2575dd6c1761cdf3c5a09392e35b684208bd32de71238e7ea64af685145b5a5f9083c1bd0bc79a80abab882df3da196eaff9b768ec25e47f89 Homepage: https://cran.r-project.org/package=Sobol4R Description: CRAN Package 'Sobol4R' (Sobol Indices for Models with Fixed and Stochastic Parameters) Tools to design experiments, compute Sobol sensitivity indices, and summarise stochastic responses inspired by the strategy described by Zhu and Sudret (2021) . Includes helpers to optimise toy models implemented in C++, visualise indices with uncertainty quantification, and derive reliability-oriented sensitivity measures based on failure probabilities. It is further detailed in Logosha, Maumy and Bertrand (2022) and (2023) or in Bertrand, Logosha and Maumy (2024) , and . Package: r-cran-sobol Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 916 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 286744 MD5sum: e0ba1213dd819f3f344568b48c029a3b SHA1: 6ef6e5a997ef209224da1d6e23471b7047db37ee SHA256: 37b4d95447ea6e54ddbeb1f233fd97b94fe74e1572f0644b50a7ab95f4e42f25 SHA512: 16fdd304b0febeefa544beb64bce4dceb06e90c6cafb22f945d95c92e21f623fe81e9b05a7c3574f1e7fff91904697340a611136199dd47290709a7e7623517c 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: arm64 Version: 0.1.1-1.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 Filename: pool/dists/resolute/main/r-cran-soccer_0.1.1-1.ca2604.1_arm64.deb Size: 55520 MD5sum: 177ea73f72c60d03527385fc5b4d21f6 SHA1: f3e14083fab1165400be1df3bad2fbbc35d4c1ae SHA256: 7aa17e08fcdee65655a77016891d2452186388adf04a9ab14378ead623e3fd05 SHA512: 3c19345a7a31ea589e56fa997d711990fd72e091c24d2a2c572105e8ebe484ca2938efe4d9484cdf92aa367b23516f6d952f493b594ac2508b8e2578a87b0efb 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: arm64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1420 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 264632 MD5sum: a9dda6640e0e5b228ffa8656baf0f584 SHA1: 71d5afd71e2ae073e1dbe6472be83ab7cebb9a21 SHA256: f44881eeec447c04e519de00c49886215d9acc28c94f7c45e8a3c93618477803 SHA512: 44f375807c205de5efb0ab865bd46b0e9de169f2852d4971658c73b67e5b3ee8482928989ae0d6710a05fc018ff615203da7ec34d945e0d5c5e0ea7e1fec4358 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. 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Package: r-cran-softbart Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1155 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 841848 MD5sum: ac16c03aa12b9a0644b3e8ccd05b2fbb SHA1: e040609259cebe374373c69cb1299387438f9688 SHA256: 879bbc25a0a36a9dfc603bb49be337216a67e9de5aaa631a7b745d7b4eead5a6 SHA512: b97e78786b5b8176cfe98407760d24d84d2037c6f9bd5f3b6b668100b4bc714398827a9a6c8441da9bce78c69121979263bc2f0f9bf461f00c219d82a0f5bb88 Homepage: https://cran.r-project.org/package=SoftBart Description: CRAN Package 'SoftBart' (Implements the SoftBart Algorithm) Implements the SoftBart model of described by Linero and Yang (2018) , with the optional use of a sparsity-inducing prior to allow for variable selection. For usability, the package maintains the same style as the 'BayesTree' package. Package: r-cran-softimpute Architecture: arm64 Version: 1.4-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1153 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-softimpute_1.4-3-1.ca2604.1_arm64.deb Size: 484064 MD5sum: 302ec595345082e9abe221e8c39e5c41 SHA1: 937e832de1e0c2634c1099253da47049788e29c7 SHA256: 91e540d67d6dc2c2048bab7dfde1e3cb55eef989090e3e6da17b0b28efcfdb14 SHA512: 90e99b4f19d8209537ddadc00e7d36f3b9724c000ef7cb733ed94c31de04dddbf805744633e4614101f0761df585d433c1908b2520b7bb9eda9f927354ed3fac 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: arm64 Version: 0.3-5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 320 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_arm64.deb Size: 236382 MD5sum: 76c6298e5058b4deb65e9add6e530d13 SHA1: 3fc209ee74fdd762937746f2be070de53d522895 SHA256: 56b046ebd07c8711d47512c70987751321addc56cddeddc51a2c9ba8fe0a2980 SHA512: a9092f6a016b746e72e4110a2d56b90dff5a460392f713eb485531ead52a70c3942edef174cddb253b217b618e3f8197801baac0c6257372164e8c20608158fd 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: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1150 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 846940 MD5sum: 5b090a88bb3393c383499a3fabc6b832 SHA1: 1a8ccd3c2fdc7bd017417d005438970eebbb1e25 SHA256: 3206cce63dd6dd2009ed8d9b85c16fe2abb644627e2d22c2fb010d417ef44809 SHA512: e5b26fba61b8a1e42b9c56a12fe50b26a6a72dbb3554680d18740048091803e03ef0bfb6e15c99045fe7212a1b17b75cdb3f727f6fb449e512c7357522cdca7d Homepage: https://cran.r-project.org/package=SOMMD Description: CRAN Package 'SOMMD' (Self Organising Maps for the Analysis of Molecular Dynamics Data) Processes data from Molecular Dynamics simulations using Self Organising Maps. Features include the ability to read different input formats. Trajectories can be analysed to identify groups of important frames. Output visualisation can be generated for maps and pathways. Methodological details can be found in Motta S et al (2022) . I/O functions for xtc format files were implemented using the 'xdrfile' library available under open source license. The relevant information can be found in inst/COPYRIGHT. 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REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the Henderson-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available. Package: r-cran-sorcering Architecture: arm64 Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 995 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_arm64.deb Size: 594298 MD5sum: 88cdef08f879548438de41ea86d07c26 SHA1: d4aa6fcea1a79fb4970da7e6fcaab25c1b06ba05 SHA256: a71e49e746445649ef31d57d6b54693560f3673cb8e4390f8e82a559506e9f1e SHA512: 7ae9f9fb3594b8208896bea5d762f3aa7e34ffd861c2dcd2e89aeccab505b5e09e36c4917f0c107f6eb0eb0c895661655df56adc2f6088bbe52b3733ff4bdac5 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: arm64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 514 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 411036 MD5sum: 68075853b0daaca92a29f418e569e8dc SHA1: cc5325b8d71920541d379eb3efd7249037392dc5 SHA256: 7d1efeb288d8e36f27c12d7becc4fe89a0a5b19dc1548183d96ddf5fbebea1b4 SHA512: caff38ca2c3a61ede33d7c3694544b30132e99a0b41352bc2fddbf167b37ffa6a0c96bf17681c22430ab8644482c26524f46a9dcae36cc52491499d4ca32b2d9 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. 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The 'sourcetools' package provides both an R and C++ interface for the tokenization of R code, and helpers for interacting with the tokenized representation of R code. Package: r-cran-sox Architecture: arm64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1517 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-glmnet, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-sox_1.2.2-1.ca2604.1_arm64.deb Size: 355942 MD5sum: b7791fb2bd675fec0959f48eac83e5ec SHA1: b0689cbe23110dcd2f6bcf1bf502f2a4297f1856 SHA256: eb4047e65e25b6e1b777a523eb029563a9ec50588a1f620dd29c50c326d5395a SHA512: 7baaa12a286d100f87bf98b18a7d9d04b2a883cba7868ad7c59bef55ad7f7de5da3419c6f5c51b2965cd2eaf29f315d4072301100af115382dd56c6cc2a47ea7 Homepage: https://cran.r-project.org/package=sox Description: CRAN Package 'sox' (Structured Learning in Time-Dependent Cox Models) Efficient procedures for fitting and cross-validating the structurally-regularized time-dependent Cox models. Package: r-cran-soynam Architecture: arm64 Version: 1.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5089 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 5039070 MD5sum: 7bf847a27c6a2985ad05de51379b5002 SHA1: 911510841a686742da99806c65133ca5518968ce SHA256: 9ca11020bf9825eb5cac58bb6fb44027106ab6969190a1896fc91c1bec92359a SHA512: 3065b870f9e888aa41bbaeea07e4fda00825a12ad29a55de5441f106eb8aacfca696fabf705965e314da100247ff45e452ae68a2ab1292822cbdb16747890070 Homepage: https://cran.r-project.org/package=SoyNAM Description: CRAN Package 'SoyNAM' (Soybean Nested Association Mapping Dataset) Genomic and multi-environmental soybean data. Soybean Nested Association Mapping (SoyNAM) project dataset funded by the United Soybean Board (USB). BLUP function formats data for genome-wide prediction and association analysis. Package: r-cran-sp Architecture: arm64 Version: 1:2.2-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9296 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Suggests: r-cran-rcolorbrewer, r-cran-gstat, r-cran-deldir, r-cran-knitr, r-cran-maps, r-cran-mapview, r-cran-rmarkdown, r-cran-sf, r-cran-terra, r-cran-raster Filename: pool/dists/resolute/main/r-cran-sp_2.2-1-1.ca2604.1_arm64.deb Size: 4559994 MD5sum: 59375b48a327c7feabe850154734fc97 SHA1: 95c780a18d0883dad1c96acda85dc71ffa9733f7 SHA256: 8f8fd90623bdbc21c38fada5fe1e907fcb3eb76a318f3cffade59ad89677454b SHA512: 96435d26bafe89a1485cfe47560e8ab4bbc803280fc16d2b38bdf281514e885f4d728710ec403e85622648af6923efbca0da0fb77718b56c00a38e86e44bfe7e Homepage: https://cran.r-project.org/package=sp Description: CRAN Package 'sp' (Classes and Methods for Spatial Data) Classes and methods for spatial data; the classes document where the spatial location information resides, for 2D or 3D data. Utility functions are provided, e.g. for plotting data as maps, spatial selection, as well as methods for retrieving coordinates, for subsetting, print, summary, etc. From this version, 'rgdal', 'maptools', and 'rgeos' are no longer used at all, see for details. Package: r-cran-spabundance Architecture: arm64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2559 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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_arm64.deb Size: 2192832 MD5sum: fd527d7118643330bf6f604954a22fe7 SHA1: 3e99a175d4ec8ed75cc066ffed7e5e2083d7becc SHA256: a608e58327eb7089a32c1400128917e902a5adea516ffbe048f3e38bc47279ac SHA512: df086edb4dd100b286229a1e3f4e583b0ffac4129028780375860339b3c81692f881cfd6cdad4109aa126cc1964d519990110196a0d2d98d9eab132617704a75 Homepage: https://cran.r-project.org/package=spAbundance Description: CRAN Package 'spAbundance' (Univariate and Multivariate Spatial Modeling of SpeciesAbundance) Fits single-species (univariate) and multi-species (multivariate) non-spatial and spatial abundance models in a Bayesian framework using Markov Chain Monte Carlo (MCMC). Spatial models are fit using Nearest Neighbor Gaussian Processes (NNGPs). Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) and Finley, Datta, and Banerjee (2022) . Fits single-species and multi-species spatial and non-spatial versions of generalized linear mixed models (Gaussian, Poisson, Negative Binomial), N-mixture models (Royle 2004 ) and hierarchical distance sampling models (Royle, Dawson, Bates (2004) ). Multi-species spatial models are fit using a spatial factor modeling approach with NNGPs for computational efficiency. Package: r-cran-spacci Architecture: arm64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6599 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 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_arm64.deb Size: 5117416 MD5sum: 8f01454201c5d0eb007d0457f9bf49c3 SHA1: 64919c16fec8961185d851f3b4514f6074d10713 SHA256: f75620369152b0593c0c25ad32b134d515d1dd306f11ec94fd9f63a90629b3c8 SHA512: 7f02d1b6c2852ef6b9d34b721788ccd9b98b467c34861b79286e83a4f917522e154b849d3a1f62fd8a4d075124fc96e10d2d9ad58f7fadf8327979fd8888d130 Homepage: https://cran.r-project.org/package=SpaCCI Description: CRAN Package 'SpaCCI' (Spatially Aware Cell-Cell Interaction Analysis) Provides tools for analyzing spatial cell-cell interactions based on ligand-receptor pairs, including functions for local, regional, and global analysis using spatial transcriptomics data. Integrates with databases like 'CellChat' , 'CellPhoneDB' , 'Cellinker' , 'ICELLNET' , and 'ConnectomeDB' to identify ligand-receptor pairs, visualize interactions through heatmaps, chord diagrams, and infer interactions on different spatial scales. Package: r-cran-spacefillr Architecture: arm64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 14700 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 4695392 MD5sum: e4a3eb89dfe21c0fc8e7cf303b87ba77 SHA1: eec73c2c71e2ca8f2e686176d0703bf96e7639e3 SHA256: db28800bfcad59dc495ec0654e505abfa03914739a52806d25b7e0cc468ce135 SHA512: 813bc07f1b75cc0d4bbae17a7427fecbf50d0239f11bf3d1ad5068684d7f8718b086f1bb1a16debe1ed2eaab53cf513dbcc3bccfee56eee1a2bc74bee7d1104d Homepage: https://cran.r-project.org/package=spacefillr Description: CRAN Package 'spacefillr' (Space-Filling Random and Quasi-Random Sequences) Generates random and quasi-random space-filling sequences. Supports the following sequences: 'Halton', 'Sobol', 'Owen'-scrambled 'Sobol', 'Owen'-scrambled 'Sobol' with errors distributed as blue noise, progressive jittered, progressive multi-jittered ('PMJ'), 'PMJ' with blue noise, 'PMJ02', and 'PMJ02' with blue noise. Includes a 'C++' 'API'. Methods derived from "Constructing Sobol sequences with better two-dimensional projections" (2012) S. Joe and F. Y. Kuo, "Progressive Multi-Jittered Sample Sequences" (2018) Christensen, P., Kensler, A. and Kilpatrick, C., and "A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space" (2019) E. Heitz, B. Laurent, O. Victor, C. David and I. Jean-Claude, . Package: r-cran-spacetimebss Architecture: arm64 Version: 0.4-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2793 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 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_arm64.deb Size: 2637936 MD5sum: 78b15bfd6ed08d5d28c9b9d1d3b50871 SHA1: d7e092c1d11c6ef4282cc56600d2046a78666337 SHA256: 7db6e7d6ae69f6e31fc4075b826dbc9918d011a957bd449fc985254346132944 SHA512: 647fb96df6bb32bfa65789b66759d9a738fc2319ee6be8ff8d8dbfd3c32cc9d5b5a3a07c43f8f4dd1d196e08e7231a7c5d3eb9c5045e950435174917fc775405 Homepage: https://cran.r-project.org/package=SpaceTimeBSS Description: CRAN Package 'SpaceTimeBSS' (Blind Source Separation for Multivariate Spatio-Temporal Data) Simultaneous/joint diagonalization of local autocovariance matrices to estimate spatio-temporally uncorrelated random fields. Package: r-cran-spaco Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 249 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-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_arm64.deb Size: 117274 MD5sum: c8238a8d0c926b2861799e942c17920e SHA1: bf23b17dcd05f54f85b47310995798bd84b8e2bb SHA256: 6d6d66442c96eaabf26d613059c837a12cc60a3c53d803272de438d63a26aed7 SHA512: b86f0c2d6e5e2fc8e97c64a130c11042a379d7cafae63ab7c55c7429fb1421784e4593b4af761506bbd9d5efcf808576ddc54b3a3bdb3e11798897f965ee3252 Homepage: https://cran.r-project.org/package=SPACO Description: CRAN Package 'SPACO' (Spatial Component Analysis for Spatial Sequencing Data) Spatial components offer tools for dimension reduction and spatially variable gene detection for high dimensional spatial transcriptomics data. Construction of a projection onto low-dimensional feature space of spatially dependent metagenes offers pre-processing to clustering, testing for spatial variability and denoising of spatial expression patterns. For more details, see Koehler et al. (2026) . Package: r-cran-spacoap Architecture: arm64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 619 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_arm64.deb Size: 192066 MD5sum: bc355095cf63db3966cc98649559dadb SHA1: 1e228338af42690279255ad5f9c7a01c06d2c8ad SHA256: c1263c4cf933cea0980bd4a331fa0aaf5d61a4e0368b4f22c64d564f830306c7 SHA512: ed901bd2096305a6bcce30e0b2edcf985eef7f4164bbfeeb0b81b245d9560063737980bfde0331a5ed0aab7512734c29aee7b2525ae326e8d3372bed992bc22f Homepage: https://cran.r-project.org/package=SpaCOAP Description: CRAN Package 'SpaCOAP' (High-Dimensional Spatial Covariate-Augmented OverdispersedPoisson Factor Model) A spatial covariate-augmented overdispersed Poisson factor model is proposed to perform efficient latent representation learning method for high-dimensional large-scale spatial count data with additional covariates. Package: r-cran-spades.tools Architecture: arm64 Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1488 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1324252 MD5sum: 064cf97babe3eeab85e25f2805dafbeb SHA1: d7395703720c7a319bd00fdd466d65c40ed395dd SHA256: 8a25ac9c3e8ba7842f7bd78f059a482c9f42d0e94fe2424d66075c9616ff9ed7 SHA512: 37ceb89f6ad9c9780a58f513ddaeb67d8a9bd9a201687fa52645373a74347a29eb4fad9fa8f8d6630495adbff37e92378ac8dc169a6a1d16766ad197683d19ee Homepage: https://cran.r-project.org/package=SpaDES.tools Description: CRAN Package 'SpaDES.tools' (Additional Tools for Developing Spatially Explicit DiscreteEvent Simulation (SpaDES) Models) Provides GIS and map utilities, plus additional modeling tools for developing cellular automata, dynamic raster models, and agent based models in 'SpaDES'. Included are various methods for spatial spreading, spatial agents, GIS operations, random map generation, and others. See '?SpaDES.tools' for an categorized overview of these additional tools. The suggested package 'NLMR' can be installed from the following repository: (). Package: r-cran-spam64 Architecture: arm64 Version: 2.10-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-spam Filename: pool/dists/resolute/main/r-cran-spam64_2.10-0-1.ca2604.1_arm64.deb Size: 75258 MD5sum: b2c6442616e96687e7fbf69bcf29d697 SHA1: 69b5c4f03cb7985d41fe7eab464575ee71849d1d SHA256: 49fa4b9efa1b9f351db1251f7b124da0c35ef0917add98e3324ce47d0b727072 SHA512: fef60f96f196669a290d910eb6dcbc440d1401c80103c2615d412a377dff359b46c76b217fba03d48ff6b9eb63f05d26baf922f0241d55207fd96d3ae41e3b38 Homepage: https://cran.r-project.org/package=spam64 Description: CRAN Package 'spam64' (64-Bit Extension of the SPArse Matrix R Package 'spam') Provides the Fortran code of the R package 'spam' with 64-bit integers. Loading this package together with the R package spam enables the sparse matrix class spam to handle huge sparse matrices with more than 2^31-1 non-zero elements. Documentation is provided in Gerber, Moesinger and Furrer (2017) . Package: r-cran-spam Architecture: arm64 Version: 2.11-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2496 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-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_arm64.deb Size: 1851040 MD5sum: 08c6906e010171b52bf3f6c2500d93f8 SHA1: 90c80658db23dc059ff184e35e5552a6b3c698be SHA256: dde3cec44b6460e0fe95ea8c7309a2e9d5bbacbc95ef7e288e028df250c52051 SHA512: f41a74acb1f37becf7b3343d3264d36930f29a5315939aa2ccb11a923b90eefb0460dd7330a0ad51ec72775bf3848b3074662bd1b7fc8ce518a8455a73671b38 Homepage: https://cran.r-project.org/package=spam Description: CRAN Package 'spam' (SPArse Matrix) Set of functions for sparse matrix algebra. Differences with other sparse matrix packages are: (1) we only support (essentially) one sparse matrix format, (2) based on transparent and simple structure(s), (3) tailored for MCMC calculations within G(M)RF. (4) and it is fast and scalable (with the extension package spam64). Documentation about 'spam' is provided by vignettes included in this package, see also Furrer and Sain (2010) ; see 'citation("spam")' for details. Package: r-cran-spamm Architecture: arm64 Version: 4.6.65-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5214 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_arm64.deb Size: 4434832 MD5sum: 48482e68aebd8d9024abf1ceb920e6eb SHA1: c7c888b4d40390785acf1efeb88b9e518a52515a SHA256: 55b1e4e5f90c34669895b67c46b422dcacd7621c802dc16ac9539d03109f1594 SHA512: 139885b4ed30da407a5aec7c3f9163297126120305fbfddc149f9e3e8f80ca063535c16c31c48d31f9d66ebcd876158858677c8eda4e259e4c6435581ccb3510 Homepage: https://cran.r-project.org/package=spaMM Description: CRAN Package 'spaMM' (Mixed-Effect Models, with or without Spatial Random Effects) Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 ), and Markov random field models on irregular grids (as considered in the 'INLA' package, ), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 ) are also implemented. Package: r-cran-spanner Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9585 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 9220028 MD5sum: adde411fc177551c50a0726b36b7f1ca SHA1: cc9bc8e47fc405d19048935189a34410848da559 SHA256: 6e3b68ce1907512e1679d543408876bc2160ed5cf4168e6621c511d0071097bf SHA512: 899e71ba7f77eb95fbce601bc47bc34f4a1483e43cf506d9833cb9f9ae5a9a2c372980f82d88874eab1e21547f4dbee55e96c2acc7405bafb0c162890a82bb6d Homepage: https://cran.r-project.org/package=spanner Description: CRAN Package 'spanner' (Utilities to Support Lidar Applications at the Landscape,Forest, and Tree Scale) Implements algorithms for terrestrial, mobile, and airborne lidar processing, tree detection, segmentation, and attribute estimation (Donager et al., 2021) , and a hierarchical patch delineation algorithm 'PatchMorph' (Girvetz & Greco, 2007) . Tree detection uses rasterized point cloud metrics (relative neighborhood density and verticality) combined with RANSAC cylinder fitting to locate tree boles and estimate diameter at breast height. Tree segmentation applies graph-theory approaches inspired by Tao et al. (2015) with cylinder fitting methods from de Conto et al. (2017) . PatchMorph delineates habitat patches across spatial scales using organism-specific thresholds. Built on 'lidR' (Roussel et al., 2020) . Package: r-cran-spant Architecture: arm64 Version: 4.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3772 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-cran-abind, r-cran-plyr, r-cran-pbapply, r-cran-pracma, r-cran-stringr, r-cran-expm, r-cran-signal, r-cran-minpack.lm, r-cran-ptw, r-cran-mmand, r-cran-rnifti, r-cran-rniftyreg, r-cran-fields, r-cran-numderiv, r-cran-nloptr, r-cran-irlba, r-cran-jsonlite Suggests: r-cran-viridislite, r-cran-shiny, r-cran-shinyfiles, r-cran-ggplot2, r-cran-miniui, r-cran-knitr, r-cran-kableextra, r-cran-rmarkdown, r-cran-testthat, r-cran-ragg, r-cran-doparallel, r-cran-digest, r-cran-readxl, r-cran-fslr, r-cran-car, r-cran-divest, r-cran-rpyants Filename: pool/dists/resolute/main/r-cran-spant_4.1.0-1.ca2604.1_arm64.deb Size: 2800970 MD5sum: 75175d6053884bfc9703539bbe10b538 SHA1: cfef0079aa1f306c03c64a9f10d1d5306a3c4f50 SHA256: bef71c9ca82fd833f9875a32bb9f45506631ea7fe1e627d0285dc0d9185b12be SHA512: c8683227feffcb58832c7d8843145e6817a4d074e88082d01212aeac161b25e87f113a5cd8d112bafbac7eb321d964906bdd8061bae9d53ca0f2fbf0c7795624 Homepage: https://cran.r-project.org/package=spant Description: CRAN Package 'spant' (MR Spectroscopy Analysis Tools) Tools for reading, visualising and processing Magnetic Resonance Spectroscopy data. The package includes methods for spectral fitting: Wilson (2021) , Wilson (2025) and spectral alignment: Wilson (2018) . Package: r-cran-sparcl Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 177 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_arm64.deb Size: 82876 MD5sum: b08e1d04e66643431004c53fb1d23156 SHA1: 9be067d109574d260878cc7ee5304f934dcdd987 SHA256: 7b301a5a8e184a096e97666f4222ce66e57cc6ce17bfd99906b7ae4b7c45ad27 SHA512: 30f170e1cad05d2ab37358ad2e535e77fafc5fc5dd281d7584a553745b94dcc79758ffbe37ed5b5b9b1aaf62654bdc6b56b15fad1bc005d0236e6111ef82331b 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: arm64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 243 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 87362 MD5sum: 09a4db2d49b23dcb087a08eddf4c33a8 SHA1: 319ec59f2b134c04ecfbe37994d2fcd36d7cf2ab SHA256: a9fa3180b2a9854037663d4b62814faf51cba267ce662bbd6f58c74699b8d75f SHA512: 7044f690b16a23591158ef22ac001b08c61fd38c58b5e469ab69efd2898d80db42edbd64b81079b8e2f861cccfb0f8c6b92087e396b1b70bff846e5933527c37 Homepage: https://cran.r-project.org/package=SparseChol Description: CRAN Package 'SparseChol' (Sparse Matrix C++ Classes Including Sparse Cholesky LDLDecomposition of Symmetric Matrices) 'C++' classes for sparse matrix methods including implementation of sparse LDL decomposition of symmetric matrices and solvers described by Timothy A. Davis (2016) . Provides a set of C++ classes for basic sparse matrix specification and linear algebra, and a class to implement sparse LDL decomposition and solvers. See for details. Package: r-cran-sparsedfm Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1037 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_arm64.deb Size: 652278 MD5sum: 8d8505e7cc3622ffc9819605a4af6165 SHA1: e3a0e1ff53d7c12f3f1b8e3332c823fa852e7a44 SHA256: ebf27232a59698a0d4eca93a475a8991af7121c47e147fa421322aad140342e0 SHA512: 70f942f13cf1f9d827e8aa8615ac9fca431eb1027d7ad586128e35bfd6ddc308e177dcfb56032a4895d3065a5e0dd17743d33f4dee4d856883061e2a1dda1638 Homepage: https://cran.r-project.org/package=sparseDFM Description: CRAN Package 'sparseDFM' (Estimate Dynamic Factor Models with Sparse Loadings) Implementation of various estimation methods for dynamic factor models (DFMs) including principal components analysis (PCA) Stock and Watson (2002) , 2Stage Giannone et al. (2008) , expectation-maximisation (EM) Banbura and Modugno (2014) , and the novel EM-sparse approach for sparse DFMs Mosley et al. (2023) . Options to use classic multivariate Kalman filter and smoother (KFS) equations from Shumway and Stoffer (1982) or fast univariate KFS equations from Koopman and Durbin (2000) , and options for independent and identically distributed (IID) white noise or auto-regressive (AR(1)) idiosyncratic errors. Algorithms coded in 'C++' and linked to R via 'RcppArmadillo'. Package: r-cran-sparsegl Architecture: arm64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1029 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_arm64.deb Size: 733112 MD5sum: 04b84387944ba6e4bfa7bc07e384ef46 SHA1: 693524314cda5660c98b04d9879ee1210ba7c239 SHA256: cb6e38e0dc6f594a72ea90fb4e9f2316bbd0d3277126fc4aee4e49c5a3c780ca SHA512: 802a3cdd672c27be7c09377f09418e6624dea5162da702e3e73c61f651f86047a05a5d98d139ec47c2545fc01725b46d16e06c4f9df775f9d82d6507848d11a7 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: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 550264 MD5sum: 49705c94147ee1d81a9535f8ed1de14d SHA1: 0cd55a64403c1f0f2f725200dc5f03f3cf114e4c SHA256: 3a38499d0f1805e718c59d2dc34d7aaabc8b69808eefaa1be47f59e179884d65 SHA512: 4971614ce9965f0d3d61363f217b9f54f92d88a994e440e19cb4232a3087f3bb25b3c2f19fe84629505c0e72d88c319ee60a02b8aa6c621363d0449593f1a0af Homepage: https://cran.r-project.org/package=SparseICA Description: CRAN Package 'SparseICA' (Sparse Independent Component Analysis) Provides an implementation of the Sparse ICA method in Wang et al. (2024) for estimating sparse independent source components of cortical surface functional MRI data, by addressing a non-smooth, non-convex optimization problem through the relax-and-split framework. This method effectively balances statistical independence and sparsity while maintaining computational efficiency. Package: r-cran-sparseinv Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 73612 MD5sum: fc798eecde15d1ab217eb4d7ea834b38 SHA1: 693dbaf1612842b6a826997694907ab61ac6b471 SHA256: 30dd396554ba59956a20600ab3046c4be223df374fdde83bbf845af9fa17853b SHA512: 187bc50315e7a7cfcb0f9d34599c084e13d05c1713c6528e193d0ed62f3cb5ea214802cbab7921d125fdd4747a2d47ddb52dfed8aed3105085d0893e7dd607dd Homepage: https://cran.r-project.org/package=sparseinv Description: CRAN Package 'sparseinv' (Computation of the Sparse Inverse Subset) Creates a wrapper for the 'SuiteSparse' routines that execute the Takahashi equations. These equations compute the elements of the inverse of a sparse matrix at locations where the its Cholesky factor is structurally non-zero. The resulting matrix is known as a sparse inverse subset. Some helper functions are also implemented. Support for spam matrices is currently limited and will be implemented in the future. See Rue and Martino (2007) and Zammit-Mangion and Rougier (2018) for the application of these equations to statistics. Package: r-cran-sparselm Architecture: arm64 Version: 0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 190 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-sparselm_0.5-1.ca2604.1_arm64.deb Size: 58300 MD5sum: f873981d1235c07bcc85333e44f609d1 SHA1: d7c6b8d5bf5ff03d25acf606219ae0bbd1954b0f SHA256: 407f4064dc78a45cdfa6f2b409c64e72096a75ba0d0c97231f6129e7b7186cb5 SHA512: 986cb08d696c5779fab79e5def437a243c51d567d6e57946589fc8d30de1e7ef44be82f119ac4ef515ce1c34f1f56b470aa083c678ab2799dd29f842f9dd80f2 Homepage: https://cran.r-project.org/package=sparseLM Description: CRAN Package 'sparseLM' (Interface to the 'sparseLM' Levenberg-Marquardt Library) Provides an R interface to the 'sparseLM' C library for large-scale nonlinear least squares problems with arbitrarily sparse Jacobians. The underlying solver implements a sparse variant of the Levenberg-Marquardt algorithm for minimizing sum-of-squares objective functions, supports user-supplied analytic Jacobians or finite-difference approximation, and is designed to exploit sparsity for improved memory use and performance. This package exposes the solver in R and uses sparse matrix classes and the 'CHOLMOD' sparse Cholesky factorization routines through the 'Matrix' package interface. Methods from the C library are described in Lourakis (2010) . Package: r-cran-sparselpm Architecture: arm64 Version: 1.0-1.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-gtools, r-cran-vegan, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-sparselpm_1.0-1.ca2604.1_arm64.deb Size: 88884 MD5sum: d2b10cf83158ec5d20d17326170b511e SHA1: 39bd0c8677538661b9fc3feb2a888192878be125 SHA256: 4e156725881ee02e3e7da5bfcef3b3bc7c4b266e5288036fea34caa086886b8b SHA512: dfad5ef281bd19e6685927cf92987b1d61d4e4f9a185949454b76fef899e8c72076e4dd3c0cf1fac9e4dbc41445e4518dc7dbfabd0a63df840b159518b6ec0fd Homepage: https://cran.r-project.org/package=SparseLPM Description: CRAN Package 'SparseLPM' (The Sparse Latent Position Model for Nonnegative InteractionData) Models the nonnegative entries of a rectangular adjacency matrix using a sparse latent position model, as illustrated in Rastelli, R. (2018) "The Sparse Latent Position Model for nonnegative weighted networks" . Package: r-cran-sparseltseigen Architecture: arm64 Version: 0.2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 310 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 104432 MD5sum: 25a9fa364fb14e3d31aa6c216fd21533 SHA1: 568280544c4c6faf7e9bda5bd8866dac75da289c SHA256: 5e85c2f6ea2a6d84f34c12f2663a616b3594951af09eb630d981b25d9cb53594 SHA512: 89ce25a1ea2d42530839fb9a81048a03ee591db6a85184f883ecb8a573d09f17045d8c833613b82a9f95d1dd8633be2c05c25ae5d257e61b786f7df1e7c69785 Homepage: https://cran.r-project.org/package=sparseLTSEigen Description: CRAN Package 'sparseLTSEigen' (RcppEigen back end for sparse least trimmed squares regression) Use RcppEigen to fit least trimmed squares regression models with an L1 penalty in order to obtain sparse models. Package: r-cran-sparselu Architecture: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 180 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 44046 MD5sum: 130c7b56a8130784fd4ad983b3442af5 SHA1: 90be0c0e21053a743e59b8b4bdfbcf20dc291bae SHA256: 3f34bbc5d2b6d661bab035d20549e0167ee9b19d96bb51af011ec4e909bc425c SHA512: 018995cc4647930912cde3f134dc860b132f87250b360fc684ba3eaf5eb6d12a32a8e922e3c3b1c42c0bfacf0b1a4b406f32790f5dc97080ef1da0616b006700 Homepage: https://cran.r-project.org/package=sparselu Description: CRAN Package 'sparselu' (Sparse LU Decomposition via SuiteSparse) Provides an interface to the SuiteSparse UMFPACK LU factorisation routines for sparse matrices stored in compressed column format. Implements the algorithm described in Davis (2004) . Package: r-cran-sparsem Architecture: arm64 Version: 1.84-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1559 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_arm64.deb Size: 805236 MD5sum: 91dffc35b657f0b466e26dd8e41f50ea SHA1: 4b7e0c646c97855878ce9afa4e5fe0251541627a SHA256: a7d9f485fed9130bf75793f34218b0416cf1d3c1c1e371bb0f6022625cd30fe2 SHA512: 181cc0144b05b2999fec5fadd2c3564f2f7750596f2eee9f695e47f895110d82ff4d1eaa173e12f83431e695c05e7f6e13f1e30f25681b70f5e46af17454b74c 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: arm64 Version: 1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 191 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_arm64.deb Size: 92584 MD5sum: b7edd7ba471c261f9f9bce0570ce0e43 SHA1: b595df35b2206446184584b26ee13744b7dbd9b1 SHA256: 89cd08ea5dc8f47431a0393cdaa3e8b4801a5c771f9902c88659047c4979e284 SHA512: 7040ce9cb45e48f31c6d54505d2a59e152d7c837cebcf0fb7330deb52a35b48f5dfd60f28397e58d0842bac0869cae9e4504c0509bc8cdc74ba5c00fb1cb637e Homepage: https://cran.r-project.org/package=sparsenet Description: CRAN Package 'sparsenet' (Fit Sparse Linear Regression Models via Nonconvex Optimization) Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010). Implements the methodology described in Mazumder, Friedman and Hastie (2011) . Sparsenet computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent. Package: r-cran-sparsereg Architecture: arm64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 334 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 174254 MD5sum: 61c4e51f69dd4611d8ef33de9624e956 SHA1: 9f886e7c189dea015eee81f3a3f19919bccc5f57 SHA256: 0c7b31cceb71667ca91984234613125c74f30ac870b982d96be5fe5efa7542aa SHA512: fe1b186d8e8462c645e9441f77631425afd7532f5e2f694988d0405482afd02ef2e8236d5acd66525a3894427137a93749a76456a6fcdd844c65a6d90e7921e8 Homepage: https://cran.r-project.org/package=sparsereg Description: CRAN Package 'sparsereg' (Sparse Bayesian Models for Regression, Subgroup Analysis, andPanel Data) Sparse modeling provides a mean selecting a small number of non-zero effects from a large possible number of candidate effects. This package includes a suite of methods for sparse modeling: estimation via EM or MCMC, approximate confidence intervals with nominal coverage, and diagnostic and summary plots. The method can implement sparse linear regression and sparse probit regression. Beyond regression analyses, applications include subgroup analysis, particularly for conjoint experiments, and panel data. Future versions will include extensions to models with truncated outcomes, propensity score, and instrumental variable analysis. Package: r-cran-sparsesem Architecture: arm64 Version: 4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2101 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_arm64.deb Size: 1857838 MD5sum: af3b8e1267e6989e18eb1c77c221cfb2 SHA1: 9a290b57bd2200c452e2edb84e9ae7447684d883 SHA256: d562b3ad42f25ad0a194dd7766b16190eb72d08c128293e85b47ab5dfc98c10a SHA512: 9b1a9a3238a5dafc545c4c90462fcdc76982640026f7b29a0e2bda3c250f516bc7c9c38f55e857b6fcb72fcb111fc12e105148987f766879725e190f006b15ed Homepage: https://cran.r-project.org/package=sparseSEM Description: CRAN Package 'sparseSEM' (Elastic Net Penalized Maximum Likelihood for Structural EquationModels with Network GPT Framework) Provides elastic net penalized maximum likelihood estimator for structural equation models (SEM). The package implements `lasso` and `elastic net` (l1/l2) penalized SEM and estimates the model parameters with an efficient block coordinate ascent algorithm that maximizes the penalized likelihood of the SEM. Hyperparameters are inferred from cross-validation (CV). A Stability Selection (STS) function is also available to provide accurate causal effect selection. The software achieves high accuracy performance through a `Network Generative Pre-trained Transformer` (Network GPT) Framework with two steps: 1) pre-trains the model to generate a complete (fully connected) graph; and 2) uses the complete graph as the initial state to fit the `elastic net` penalized SEM. Package: r-cran-sparsesvd Architecture: arm64 Version: 0.2-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 116 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-sparsesvd_0.2-3-1.ca2604.1_arm64.deb Size: 31722 MD5sum: 1000194f075305b0c65d20816c240bb5 SHA1: b22d2f993d23643b903c5632cee7c83b06c6a2e8 SHA256: 2596169b0350f6be0602ce4bba74e023aac288d91a952302e885e607aed9a304 SHA512: cca99db1aaf8a509a506146a0a0c91b8ca7e2abf4adc8e83ac0ca7e7942af8ff6993d14b6dc4eaf75e51e5df5397715774a3877c4b94c0a0b589df17c5caa6be Homepage: https://cran.r-project.org/package=sparsesvd Description: CRAN Package 'sparsesvd' (Sparse Truncated Singular Value Decomposition (from 'SVDLIBC')) Wrapper around the 'SVDLIBC' library for (truncated) singular value decomposition of a sparse matrix. Currently, only sparse real matrices in Matrix package format are supported. Package: r-cran-sparsesvm Architecture: arm64 Version: 1.1-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 154 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_arm64.deb Size: 64332 MD5sum: 2b61b224adfd6c3bdf10e87dd33e205d SHA1: ff10fa6f435892601a3e0b157d7ae226beed44b1 SHA256: 442a9603f93c3b3e749232809d6e473902ceb263b3a919eed2bff28f9524d180 SHA512: d1ed15117d565815eeccdb35cd5cd2e4082220c06105a8e9e6dfbf202a7223d941a65178d1a5f44ef661cc1493f189563f884f9fcd8fbb5a5fe0a340e0d07643 Homepage: https://cran.r-project.org/package=sparseSVM Description: CRAN Package 'sparseSVM' (Solution Paths of Sparse High-Dimensional Support Vector Machinewith Lasso or Elastic-Net Regularization) Offers a fast algorithm for fitting solution paths of sparse SVM models with lasso or elastic-net regularization. Reference: Congrui Yi and Jian Huang (2017) . Package: r-cran-sparsetscgm Architecture: arm64 Version: 5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 176 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-glasso, r-cran-longitudinal, r-cran-huge, r-cran-mass, r-cran-mvtnorm, r-cran-network, r-cran-abind Filename: pool/dists/resolute/main/r-cran-sparsetscgm_5.0-1.ca2604.1_arm64.deb Size: 80602 MD5sum: 351680d18293c1b35e754fe8d815239f SHA1: eccefddc1cf21c61d8e637828bea894deb8f94d9 SHA256: 577d299001111db66e33111fff5441141404b0b7271e070b5eecaf07271d0ded SHA512: 20ba7e7f4c1069f8374d16e92815880d52a8cd57dff1d8e4f02f4eade19d887234deb9aee36b206a145a17f6b8b65e58426941f310cb8a387edf72d216f345e0 Homepage: https://cran.r-project.org/package=SparseTSCGM Description: CRAN Package 'SparseTSCGM' (Sparse Time Series Chain Graphical Models) Computes sparse vector autoregressive coefficients and sparse precision matrices for time series chain graphical models. Methods are described in Abegaz and Wit (2013) . Package: r-cran-sparsevb Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 251 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 83536 MD5sum: 42557851d247232c60548e8abf595a74 SHA1: ea000d7dcac3370089d32b8c6b62371df8fd8b76 SHA256: 231d8bf2e6f147d9f5340918c1e346a77df93e0aa9097434983f5def2c004c71 SHA512: 4c18e3a043fc3b6c0761bc18fbb1831609341361b313c159e3676755676eae4627a92beb655843bab1e51018c65c4d922d62ef5d1484fa7497226a9364f50374 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: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 605 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_arm64.deb Size: 224454 MD5sum: 9f9e2cd1a06c77febb1df3dc6a4a7716 SHA1: 75b105cd7a1af128f906647ce9e09bcc221b7dce SHA256: 9d42db09de3fd59e24298ae385ff0d24f279fe0afa102363e334af6129457c7e SHA512: 21cb07e1a783f499b0266988f816adecd15f70c78a319ba64902fbc154f2c255a7b128ea2acc09aca9dc8c94831d9e81132f8058ccbf6f62cce5cd313b1c182a Homepage: https://cran.r-project.org/package=sparseVCBART Description: CRAN Package 'sparseVCBART' (Sparse Varying Coefficient BART with Global-Local Priors") Fits sparse linear varying coefficient models (VCMs), which assert a linear relationship between an outcome and several covariates that is allowed to change as functions of additional variables known as effect modifiers. Designed for high-dimensional settings where the number of covariates (i.e., number of slopes) is comparable to or larger than the number of observations. Approximates the coefficient functions using a version of Bayesian Additive Regression Trees that can perform global-local shrinkage. For more details see Ghosh, Bhogale, and Deshpande (2026+) . Package: r-cran-sparsevctrs Architecture: arm64 Version: 0.3.6-1.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-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_arm64.deb Size: 192966 MD5sum: 27788aea1e32d5de0fe9975c493873db SHA1: 602b1d8b885433423d2f10067afda8388fa831fa SHA256: e4dfd2670bd2dced169d754362dbc99e133cecc6091d2660e08a4fd3beb1ccf7 SHA512: ba8fc8983739b4c4edad77f8e8fc1b1c7a71aed9b2b1e0ca5aca066339b504b4d0ec287f3d6d14b3c80d13afce3e42a3ef9f70a33fe5cd65fc4d83bd466200ec Homepage: https://cran.r-project.org/package=sparsevctrs Description: CRAN Package 'sparsevctrs' (Sparse Vectors for Use in Data Frames) Provides sparse vectors powered by ALTREP (Alternative Representations for R Objects) that behave like regular vectors, and can thus be used in data frames. Also provides tools to convert between sparse matrices and data frames with sparse columns and functions to interact with sparse vectors. Package: r-cran-sparsio Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 185 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_arm64.deb Size: 53178 MD5sum: eaee054849cb902033ee3ba08aa5acdc SHA1: 865ba16ea802d5f2d2ad4148a8b3f901a373b165 SHA256: e47770741852ffe6a561d71f0dbf4fbb79dad43be7086292cfade02550c6697d SHA512: 188dd66801b29c168f335981ed8a7e8cda8aadb2dedcd8f477b922630abf75947d5eb8c53a5e6af8b9a7e79cdb9c54eb6f44573f701766ce50e9570b60f09a01 Homepage: https://cran.r-project.org/package=sparsio Description: CRAN Package 'sparsio' (I/O Operations with Sparse Matrices) Fast 'SVMlight' reader and writer. 'SVMlight' is most commonly used format for storing sparse matrices (possibly with some target variable) on disk. For additional information about 'SVMlight' format see . Package: r-cran-sparta Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 421 Depends: libc6 (>= 2.17), 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-rmarkdown, r-cran-knitr, r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-sparta_1.0.1-1.ca2604.1_arm64.deb Size: 165538 MD5sum: 5aa9afeeb2d3757caddb420234acc0e6 SHA1: 75e04e9dbddbea9648b90766b7bc07d0ecd46863 SHA256: 7438ed3d27cbe269cb06cefe509888d988f5f034fa5200abf6ff85d18e93593b SHA512: 32c1eb57503c2bc56d87b6dd9f8bd6bff7c001c18594bf9633a341a66d16095f6e6b7c5e5ffc4c877eb7379ae41757d6499ac5998e07f83080e6d712ee3e797a Homepage: https://cran.r-project.org/package=sparta Description: CRAN Package 'sparta' (Sparse Tables) Fast Multiplication and Marginalization of Sparse Tables . Package: r-cran-sparvaride Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 210 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 46078 MD5sum: 9b04972336baed50f541a31fa86635b7 SHA1: d8c4c8fdc327cea7b1a6e038d8bd134b2dd18091 SHA256: 855b05b9be84a6c05228d019cf9e7dcb15fbff0b824aa66defd70b391e1253a4 SHA512: 3ebd73d61c1428847fb2c4264f9690a75a9f6881a7abf982d6b78e5ed50e589a8e9f554678aacfad92c7e014759b14181a3a70366512c756299677e05378b96b Homepage: https://cran.r-project.org/package=sparvaride Description: CRAN Package 'sparvaride' (Variance Identification in Sparse Factor Analysis) This is an implementation of the algorithm described in Section 3 of Hosszejni and Frühwirth-Schnatter (2026) . The algorithm is used to verify that the counting rule CR(r,1) holds for the sparsity pattern of the transpose of a factor loading matrix. As detailed in Section 2 of the same paper, if CR(r,1) holds, then the idiosyncratic variances are generically identified. If CR(r,1) does not hold, then we do not know whether the idiosyncratic variances are identified or not. Package: r-cran-spas Architecture: arm64 Version: 2026.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1542 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-checkmate, r-cran-mass, r-cran-matrix, r-cran-msm, r-cran-numderiv, r-cran-plyr, r-cran-reshape2, r-cran-tmb, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-spas_2026.4.1-1.ca2604.1_arm64.deb Size: 445920 MD5sum: 3ed406a0d476680db41e4a5aae0a7c26 SHA1: ef9a8149ea5c25e1236d7e0b12fa897c765041fd SHA256: 1076a511bd1a0a1f6f00c714dbf34176d99b6b97e4c5d294f6a43d10f871b929 SHA512: 500bb1ca3ffb88626463adef38431f003d9f370f480694b7e2f870c9919ed69b5c73b6defd7f29fa9f24552e44f9b14976af117fdf3bedea9f4d5cc6516d865a Homepage: https://cran.r-project.org/package=SPAS Description: CRAN Package 'SPAS' (Stratified-Petersen Analysis System) The Stratified-Petersen Analysis System (SPAS) is designed to estimate abundance in two-sample capture-recapture experiments where the capture and recaptures are stratified. This is a generalization of the simple Lincoln-Petersen estimator. Strata may be defined in time or in space or both, and the s strata in which marking takes place may differ from the t strata in which recoveries take place. When s=t, SPAS reduces to the method described by Darroch (1961) . When s. Schwarz and Taylor (1998) describe the use of SPAS in estimating return of salmon stratified by time and geography. A related package, BTSPAS, deals with temporal stratification where a spline is used to model the distribution of the population over time as it passes the second capture location. This is the R-version of the (now obsolete) standalone Windows program of the same name. Package: r-cran-spass Architecture: arm64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 435 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_arm64.deb Size: 263802 MD5sum: e611a7339ad362130179e41a854b2014 SHA1: 7fc6a19c28b2f8890a405f22936d38090fe2a156 SHA256: 4e8e2df05433fad8bffddbc3003c538d33fcb6839bbe71bbdd1bf4478de790d5 SHA512: 96fa412ec667f755c1ad7bf16c3f10288d0ea069d34ed96f77f1c3ce094baaff8b30c06542fbd8d0e90a1df8fa27682c8925890e7da517c3591fb7bb94730313 Homepage: https://cran.r-project.org/package=spass Description: CRAN Package 'spass' (Study Planning and Adaptation of Sample Size) Sample size estimation and blinded sample size reestimation in Adaptive Study Design. Package: r-cran-spate Architecture: arm64 Version: 1.7.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1540 Depends: libc6 (>= 2.17), libfftw3-double3 (>= 3.3.10), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-truncnorm Filename: pool/dists/resolute/main/r-cran-spate_1.7.5-1.ca2604.1_arm64.deb Size: 1338506 MD5sum: 0a16debb36f05f177ab29623b684af37 SHA1: f4b65942be8935d2b83e2b23572e9fee64ab8356 SHA256: 88da9afd883092f3d7cb1110481608e5a3c7e20e28345bbe4d749ee1bd985662 SHA512: 3817c342d97ad7144525898ada7e94b5c4bf9a31c2798688b6b1eab91ce799b2553126110de12b5fe94c6bfe71440cd1b2b1c2fe0eac8c1cee59ac3de3cbd411 Homepage: https://cran.r-project.org/package=spate Description: CRAN Package 'spate' (Spatio-Temporal Modeling of Large Data Using a Spectral SPDEApproach) Functionality for spatio-temporal modeling of large data sets is provided. A Gaussian process in space and time is defined through a stochastic partial differential equation (SPDE). The SPDE is solved in the spectral space, and after discretizing in time and space, a linear Gaussian state space model is obtained. When doing inference, the main computational difficulty consists in evaluating the likelihood and in sampling from the full conditional of the spectral coefficients, or equivalently, the latent space-time process. In comparison to the traditional approach of using a spatio-temporal covariance function, the spectral SPDE approach is computationally advantageous. See Sigrist, Kuensch, and Stahel (2015) for more information on the methodology. This package aims at providing tools for two different modeling approaches. First, the SPDE based spatio-temporal model can be used as a component in a customized hierarchical Bayesian model (HBM). The functions of the package then provide parameterizations of the process part of the model as well as computationally efficient algorithms needed for doing inference with the HBM. Alternatively, the adaptive MCMC algorithm implemented in the package can be used as an algorithm for doing inference without any additional modeling. The MCMC algorithm supports data that follow a Gaussian or a censored distribution with point mass at zero. Covariates can be included in the model through a regression term. Package: r-cran-spatgraphs Architecture: arm64 Version: 3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 329 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 153178 MD5sum: a6dbcbec4e18215ec5471e9f539bce23 SHA1: 6d06f01b197a6306a61ae614cdf6db64a01545c7 SHA256: bf79ec8fad73e6de2123cfb75d59b8d68a6485ead670ce0289bf8b62b28972ea SHA512: 25c3384018bf20f056aff8f8d42eaf20683527dbf006040d019784cbfb8df21ddb0ddce19b2b0b7f01775ec5bc62f0829b08ff2f251bc458e15df8b987cf3697 Homepage: https://cran.r-project.org/package=spatgraphs Description: CRAN Package 'spatgraphs' (Graph Edge Computations for Spatial Point Patterns) Graphs (or networks) and graph component calculations for spatial locations in 1D, 2D, 3D etc. Package: r-cran-spaths Architecture: arm64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1947 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 511902 MD5sum: 29436f768381b83cd5919764355951b4 SHA1: ae258c0625379eb001cba0845046329c76c1e82f SHA256: 171dbd680d0720fe96f45c680e74c05de3702048f162a564044b2d097adef4b5 SHA512: ff099b4b15980385426fed9c60840537cd798818a290504fe5238af05f4c294d3a0e210d0465bb908683d712ead097b01d88113448091201d89df2cd775cf77c Homepage: https://cran.r-project.org/package=spaths Description: CRAN Package 'spaths' (Shortest Paths Between Points in Grids) Shortest paths between points in grids. Optional barriers and custom transition functions. Applications regarding planet Earth, as well as generally spheres and planes. Optimized for computational performance, customizability, and user friendliness. Graph-theoretical implementation tailored to gridded data. Currently focused on Dijkstra's (1959) algorithm. Future updates broaden the scope to other least cost path algorithms and to centrality measures. Package: r-cran-spatial Architecture: arm64 Version: 7.3-18-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-spatial_7.3-18-1.ca2604.1_arm64.deb Size: 131154 MD5sum: 6078417d0eb051bb91cdbb0a85bd4575 SHA1: ceb338c147d70aef12b1416ff6776171953a8ada SHA256: 1f148f2ef374a4288a60069a34742b2785254e368c6cfa0489466d5b241cf88c SHA512: d553c48ed26f9ae3456ea841cd657d393cc7493a2342fc1a61be2a45bf1b479bfeaaf0c4df17981bf57385383dd55f8c48c989155a49fbf5e5255e0dc5c3b0d7 Homepage: https://cran.r-project.org/package=spatial Description: CRAN Package 'spatial' (Functions for Kriging and Point Pattern Analysis) Functions for kriging and point pattern analysis. Package: r-cran-spatialbss Architecture: arm64 Version: 0.16-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1072 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_arm64.deb Size: 804920 MD5sum: 2bb550b20faf2652a7d15b383062e649 SHA1: b341134db21c3afa6ff34dec3d911fcc0799b66b SHA256: 8e117637deb257dc326125c77f6d6c16c60c0a65d5f80597308cff2c016273aa SHA512: 266b1b010db93df7aab9eb3c0139c55364b43e3b4e93cfa510807e467f20b643c93445d32214c7e3ba948e16db9f91d2643d3f9f4a1bf7768aa09ac422bd4674 Homepage: https://cran.r-project.org/package=SpatialBSS Description: CRAN Package 'SpatialBSS' (Blind Source Separation for Multivariate Spatial Data) Blind source separation for multivariate spatial data based on simultaneous/joint diagonalization of (robust) local covariance matrices. This package is an implementation of the methods described in Bachoc, Genton, Nordhausen, Ruiz-Gazen and Virta (2020) . Package: r-cran-spatialepi Architecture: arm64 Version: 1.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 605 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_arm64.deb Size: 381538 MD5sum: 2a7372fc9b28e2e61fabdcc58f330743 SHA1: 758f7de6668b715207478f1f0fe7d3dce1f4df45 SHA256: 8a7deb1b689e845d652e1a9516d2fe46754837028254888886726402c8741441 SHA512: 392945c7a340601df742b0f45050d2a36359d32f7d725c57d067df7380c00bb6d6051392b17a525e8aff9d4c5fd8389cae963301156716a47521720d3b39eec1 Homepage: https://cran.r-project.org/package=SpatialEpi Description: CRAN Package 'SpatialEpi' (Methods and Data for Spatial Epidemiology) Methods and data for cluster detection and disease mapping. Package: r-cran-spatialextremes Architecture: arm64 Version: 2.1-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2224 Depends: libblas3 | libblas.so.3, libc6 (>= 2.35), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-maps, r-cran-fields Filename: pool/dists/resolute/main/r-cran-spatialextremes_2.1-0-1.ca2604.1_arm64.deb Size: 1841804 MD5sum: f02249104dd205ec38bd1e0e6229405c SHA1: 27615fd6ae70b6486c0f2416f0439790b584b1ca SHA256: 02dc71f6905ed51c3940ed31aca3f3e289f65c540af244d680300f610a25291a SHA512: 58dc1d12286db40849eb3e6cdb14a89d2e0f9e9418879c86d7dfde6be009887416b80b65a1121b4cb5eaa05dec833cc196d8dbe8e2d155c33f731d6a8cfd86bf Homepage: https://cran.r-project.org/package=SpatialExtremes Description: CRAN Package 'SpatialExtremes' (Modelling Spatial Extremes) Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models. More precisely, this package allows (conditional) simulations from various parametric max-stable models, analysis of the extremal spatial dependence, the fitting of such processes using composite likelihoods or least square (simple max-stable processes only), model checking and selection and prediction. Other approaches (although not completely in agreement with the extreme value theory) are available such as the use of (spatial) copula and Bayesian hierarchical models assuming the so-called conditional assumptions. The latter approaches is handled through an (efficient) Gibbs sampler. Some key references: Davison et al. (2012) , Padoan et al. (2010) , Dombry et al. (2013) . Package: r-cran-spatialge Architecture: arm64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1043 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 755542 MD5sum: dda304540219b2a87bdcba5482f479fb SHA1: d4fe2dd23abcf0355e2011bd35aaa11386689987 SHA256: 32757b1a9de51cca06c25cf2d2b0866ea03eb689768d8bd874636c088b7223bc SHA512: a722d18452ed8715d7bb5d5f342b10d720cf66c171c84c4a7d38991ba27559b2734cc9c59018327f9a5f62921e85d9a55dd6f1067ca5b71cdfcc8b709f13df6a Homepage: https://cran.r-project.org/package=spatialGE Description: CRAN Package 'spatialGE' (Visualization and Analysis of Spatial Heterogeneity inSpatially-Resolved Gene Expression) Visualization and analysis of spatially resolved transcriptomics data. The 'spatialGE' R package provides methods for visualizing and analyzing spatially resolved transcriptomics data, such as 10X Visium, CosMx, or csv/tsv gene expression matrices. It includes tools for spatial interpolation, autocorrelation analysis, tissue domain detection, gene set enrichment, and differential expression analysis using spatial mixed models. Package: r-cran-spatialgev Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2772 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_arm64.deb Size: 1331446 MD5sum: 6f836e63a9206af71410a84e586a30de SHA1: 83a23d05f4d019d4145f54acdb5f3440196cb251 SHA256: e9ce81e4d15b8392377cb06c8c7f8b308fe4e8d4192d31159c5fcfadd42a763b SHA512: e7ede20a669571c3b031792c2abbfac7212335055e5ac13a72dfe7f1dbd1c0427cbdf172432e5cd2f11ac35cdbac639eb80264df25c351197421f167294a7799 Homepage: https://cran.r-project.org/package=SpatialGEV Description: CRAN Package 'SpatialGEV' (Fit Spatial Generalized Extreme Value Models) Fit latent variable models with the GEV distribution as the data likelihood and the GEV parameters following latent Gaussian processes. The models in this package are built using the template model builder 'TMB' in R, which has the fast ability to integrate out the latent variables using Laplace approximation. This package allows the users to choose in the fit function which GEV parameter(s) is considered as a spatially varying random effect following a Gaussian process, so the users can fit spatial GEV models with different complexities to their dataset without having to write the models in 'TMB' by themselves. This package also offers methods to sample from both fixed and random effects posteriors as well as the posterior predictive distributions at different spatial locations. Methods for fitting this class of models are described in Chen, Ramezan, and Lysy (2024) . Package: r-cran-spatialinference Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1628 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_arm64.deb Size: 1202958 MD5sum: 3c3d9a410f75bf173afc496ed0534b72 SHA1: 519ff680f614176ffaff54a79286fcb0ae30b2ba SHA256: a726f784eda02d70ebd4e8ba9c1786c101c8c4f18e61a533da0502aacfe3e9a9 SHA512: 53dc0420fd8fcd0f633ae1ae5ae82568feda2a81ef44680155d82bbe346b03ce6c244915d68b129a81030ebc296a882dfa073cb0582098e98bd70e92354cba94 Homepage: https://cran.r-project.org/package=SpatialInference Description: CRAN Package 'SpatialInference' (Tools for Statistical Inference with Geo-Coded Data) Fast computation of Conley (1999) spatial heteroskedasticity and autocorrelation consistent (HAC) standard errors for linear regression models with geo-coded data, with a fast C++ implementation by Christensen, Hartman, and Samii (2021) . Performance-critical distance calculations, kernel weighting, and variance component accumulation are implemented in C++ via 'Rcpp' and 'RcppArmadillo'. Includes tools for estimating the spatial correlation range from covariograms and correlograms following the bandwidth selection method proposed in Lehner (2026) , and diagnostic visualizations for bandwidth selection. Package: r-cran-spatialising Architecture: arm64 Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 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_arm64.deb Size: 191234 MD5sum: 61039143f56027ba80a709ba314b8973 SHA1: af94a1337206abae467618a8d9ded243ced1da56 SHA256: e9379f640218adc16d21f4700b62c57256300055e24c1164d9f9d08cfdc82504 SHA512: 27c130e48e0b959596dcf73df8e7b8356c5dc06f4553aa3efc8a750147d9ef310b60e60185e75ee6a1c872eda3aa1f251286a8057df0e0e1065feccbca6afb00 Homepage: https://cran.r-project.org/package=spatialising Description: CRAN Package 'spatialising' (Ising Model for Spatial Data) Performs simulations of binary spatial raster data using the Ising model (Ising (1925) ; Onsager (1944) ). It allows to set a few parameters that represent internal and external pressures, and the number of simulations (Stepinski and Nowosad (2023) ). Package: r-cran-spatialkde Architecture: arm64 Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 254 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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_arm64.deb Size: 128528 MD5sum: bc7594de17ae6a3b7b3d7d5b40dccc51 SHA1: 9178b7eecbd979c6d0922e70d19f5e8bfa23f4c9 SHA256: 933c5aa533907672265daf086afc0f03999b4ea2644b01e04d24be832dd76aee SHA512: 50624548475bd8f44cc891f58d032adb840ec58a6e87938ba1aaeb1bc96005df3ba8c1e99622a8d010f26a5905d0edf3feba6d0e61019adeb1dd9340aaacec6b 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: arm64 Version: 1.1-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 234 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 147968 MD5sum: 95ac0bbbf9011286b23668827c80d9d7 SHA1: 10e9d6a8bfafb4beefbca0508a45aa13db8c6997 SHA256: c6d7a6f449ef672eb9840663af0c85cf8b222a3cc1228b171d931979f6a3c7fb SHA512: 0105380ab80a776a1871d23126d06cc77296d0a2181d90dad825d9e5f053dac5f904acd6657b7fc12e1fc3c95f52b5a72a2b9cfcf030939357f8b45c9d572647 Homepage: https://cran.r-project.org/package=SpatialNP Description: CRAN Package 'SpatialNP' (Multivariate Nonparametric Methods Based on Spatial Signs andRanks) Test and estimates of location, tests of independence, tests of sphericity and several estimates of shape all based on spatial signs, symmetrized signs, ranks and signed ranks. For details, see Oja and Randles (2004) and Oja (2010) . Package: r-cran-spatialpack Architecture: arm64 Version: 0.4-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 654 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_arm64.deb Size: 585506 MD5sum: 6473457b2ada01e300683192d51bcc91 SHA1: b38f945f1affd73ecd64dfcaff23484e99120029 SHA256: 804ec44862da909adaf5a8bb619e1f286794151451c084bd6458baae997a77ba SHA512: 2e39080500af67ea761e7bc9cdb8091cf34cbfdf019dec0ae87916961d54615b37af6de2080a5f66d2abc6c0a8174d57df893715f203bfd45816317a1252a76b Homepage: https://cran.r-project.org/package=SpatialPack Description: CRAN Package 'SpatialPack' (Tools for Assessment the Association Between Two SpatialProcesses) Tools to assess the association between two spatial processes. Currently, several methodologies are implemented: A modified t-test to perform hypothesis testing about the independence between the processes, a suitable nonparametric correlation coefficient, the codispersion coefficient, and an F test for assessing the multiple correlation between one spatial process and several others. Functions for image processing and computing the spatial association between images are also provided. Functions contained in the package are intended to accompany Vallejos, R., Osorio, F., Bevilacqua, M. (2020). Spatial Relationships Between Two Georeferenced Variables: With Applications in R. Springer, Cham . Package: r-cran-spatialreg Architecture: arm64 Version: 1.4-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4367 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spdata, r-cran-matrix, r-cran-sf, r-cran-spdep, r-cran-coda, r-cran-mvtnorm, r-cran-boot, r-cran-learnbayes, r-cran-nlme, r-cran-multcomp, r-cran-marginaleffects Suggests: r-cran-rspectra, r-cran-tmap, r-cran-foreign, r-cran-spam, r-cran-knitr, r-cran-lmtest, r-cran-expm, r-cran-sandwich, r-cran-rmarkdown, r-cran-igraph, r-cran-tinytest, r-cran-codingmatrices Filename: pool/dists/resolute/main/r-cran-spatialreg_1.4-3-1.ca2604.1_arm64.deb Size: 1552538 MD5sum: 0e4e52ca950ae9dfbd91001c7caf151c SHA1: 51025bcee15e61010069399bdd029c8d6ee27cb1 SHA256: 95bd60829e1ba48b0ef0623fed514a7f48d0b8be8218271f4bba1f13ad9693b0 SHA512: 59c2531d61323c04f8dc78058b189f7b0542423dfd2f4f46874cc56d82fd00706fea9aaaa5636f9f4d8b2b6cca5d47a5f448c6b050c927fe338e2ed9be4cd86a Homepage: https://cran.r-project.org/package=spatialreg Description: CRAN Package 'spatialreg' (Spatial Regression Analysis) A collection of all the estimation functions for spatial cross-sectional models (on lattice/areal data using spatial weights matrices) contained up to now in 'spdep'. These model fitting functions include maximum likelihood methods for cross-sectional models proposed by 'Cliff' and 'Ord' (1973, ISBN:0850860369) and (1981, ISBN:0850860814), fitting methods initially described by 'Ord' (1975) . The models are further described by 'Anselin' (1988) . Spatial two stage least squares and spatial general method of moment models initially proposed by 'Kelejian' and 'Prucha' (1998) and (1999) are provided. Impact methods and MCMC fitting methods proposed by 'LeSage' and 'Pace' (2009) are implemented for the family of cross-sectional spatial regression models. Methods for fitting the log determinant term in maximum likelihood and MCMC fitting are compared by 'Bivand et al.' (2013) , and model fitting methods by 'Bivand' and 'Piras' (2015) ; both of these articles include extensive lists of references. A recent review is provided by 'Bivand', 'Millo' and 'Piras' (2021) . 'spatialreg' >= 1.1-* corresponded to 'spdep' >= 1.1-1, in which the model fitting functions were deprecated and passed through to 'spatialreg', but masked those in 'spatialreg'. From versions 1.2-*, the functions have been made defunct in 'spdep'. From version 1.3-6, add Anselin-Kelejian (1997) test to `stsls` for residual spatial autocorrelation . Package: r-cran-spatialrisk Architecture: arm64 Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5667 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 4522334 MD5sum: 4196c0ec75793a3ee42585e25d1ab8bb SHA1: 7fbefb893c8964a0ea1ea7aa08503d38505f0eff SHA256: 0b09aa31bad0a4304992b04f52549edb9d8a3f3d7b7dea187aa6ff8705ed46c8 SHA512: 9dd26c29f3deb0852472cb0f6ac5e310a7b0d69058fb8f29e979f39e10155dca0eee45393fa9ec8668f1e01b8cfd7df3e67801817f7947672ed0efa607822e4c Homepage: https://cran.r-project.org/package=spatialrisk Description: CRAN Package 'spatialrisk' (Spatial Concentration and Radius-Based Risk Calculations) Provides methods for spatial concentration and radius-based risk calculations. The package focuses on efficient determination of the sum of observations within a given radius, identifying local concentration hotspots, and aggregating point data to polygon geometries. These methods are useful for applications such as insurance, urban analytics, environmental exposure analysis, and other spatial point pattern workflows. The underlying maximum covering problem is described by Church (1974) . Package: r-cran-spatialsample Architecture: arm64 Version: 0.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2005 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-ggplot2, r-cran-glue, r-cran-purrr, r-cran-rlang, r-cran-rsample, r-cran-sf, r-cran-tibble, r-cran-tidyselect, r-cran-units, r-cran-vctrs, r-cran-cpp11 Suggests: r-cran-covr, r-cran-gifski, r-cran-knitr, r-cran-lwgeom, r-cran-modeldata, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr, r-cran-vdiffr, r-cran-whisker, r-cran-withr, r-cran-yardstick Filename: pool/dists/resolute/main/r-cran-spatialsample_0.6.1-1.ca2604.1_arm64.deb Size: 1592566 MD5sum: 0a8aa912258e9f3fb2473119335a1ce0 SHA1: d3fd1445cc682fb42b488e8234e84543beba6757 SHA256: 4ff6c33f80eabea27e43b6cd5f7d922cdde14f5bd3bbc55681a0253d65872a3c SHA512: b22a0d0ce273b5ab96d905f6ac160821c0e92135ee8b0d5942060a813ba270295352de3130b1b5afa6c924cda74f70c9523b56ca098639b4b06140ef2f1c1a31 Homepage: https://cran.r-project.org/package=spatialsample Description: CRAN Package 'spatialsample' (Spatial Resampling Infrastructure) Functions and classes for spatial resampling to use with the 'rsample' package, such as spatial cross-validation (Brenning, 2012) . The scope of 'rsample' and 'spatialsample' is to provide the basic building blocks for creating and analyzing resamples of a spatial data set, but neither package includes functions for modeling or computing statistics. The resampled spatial data sets created by 'spatialsample' do not contain much overhead in memory. Package: r-cran-spatialtools Architecture: arm64 Version: 1.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 560 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 318994 MD5sum: 1f6c1fa12c9217919caf90ac681dc97e SHA1: 7c500f7d7c97cc50a6602fcb2dc9f5be1ecfbda8 SHA256: 6156a7055c373e2878327f6e5b9238fcebabab65338f772994b26cb332c49a83 SHA512: 89da937e54b7609a5c8eb542cedee34805689e1b6a755d016842fd97ca707a46736754033a86b489f59b3639ab6e9959312c364a40b3933b142c6553b37255a6 Homepage: https://cran.r-project.org/package=SpatialTools Description: CRAN Package 'SpatialTools' (Tools for Spatial Data Analysis) Tools for spatial data analysis. Emphasis on kriging. Provides functions for prediction and simulation. Intended to be relatively straightforward, fast, and flexible. Package: r-cran-spatialwarnings Architecture: arm64 Version: 3.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1627 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_arm64.deb Size: 1398986 MD5sum: 23bc4c6ef5a4105bf7fe10c25115f442 SHA1: ee2664287348e143a001b7f4f933d0918255bdcd SHA256: e120d5b7f52745ac8ce17e67b29a8b887e0dc792345511eda9cab0eb0c03f873 SHA512: 31e3e89dcf4bff6e9be9cf087427afc2661910cffcb9834f1ce08ac0b92c3e3478360f6ee35c89faeee1644365aa8b8800fe8a8134b61aaf182bd2f9d01018fe Homepage: https://cran.r-project.org/package=spatialwarnings Description: CRAN Package 'spatialwarnings' (Spatial Early Warning Signals of Ecosystem Degradation) Tools to compute and assess significance of early-warnings signals (EWS) of ecosystem degradation. EWS are spatial metrics derived from raster data -- e.g. spatial autocorrelation -- that increase before an ecosystem undergoes a non-linear transition (Genin et al. (2018) ). Package: r-cran-spatialwidget Architecture: arm64 Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3338 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_arm64.deb Size: 797174 MD5sum: 37fc2c5810a6fef56899bb5308f793b6 SHA1: ca146f7c4e691a84e40d54eec2403e2406d39a9e SHA256: 9fbd0c1f2f413414bc8a90ed4338055d66f874f750c7b2ee8266dff551ca2ae3 SHA512: ef05c9d5b4f01b925a361d2dd66eb79ac89003f85bde6b1f480c04247f518a4811cb13fd831b3a4bbbe572ea95873b928e3929acf59fc93606efad8cf8d7e881 Homepage: https://cran.r-project.org/package=spatialwidget Description: CRAN Package 'spatialwidget' (Formats Spatial Data for Use in Htmlwidgets) Many packages use 'htmlwidgets' for interactive plotting of spatial data. This package provides functions for converting R objects, such as simple features, into structures suitable for use in 'htmlwidgets' mapping libraries. Package: r-cran-spatmca Architecture: arm64 Version: 1.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 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_arm64.deb Size: 188836 MD5sum: 842708a5b9b75c202503c320cac65338 SHA1: d27875dec346e3ef017d5f48e8336941370c4d25 SHA256: 3da1a4c299fe52750562dd74656ac7e02012914f34c9e928cde8bed2eb684f90 SHA512: a3cf175da7f9ac49a72e19aea2698bd3d2260a1c832c1c636a25f71884f70412a2bcf1f0d7c10b9d69fa9f34aae7befcdbd7c652ef14335d573ad73364fdd2a1 Homepage: https://cran.r-project.org/package=SpatMCA Description: CRAN Package 'SpatMCA' (Regularized Spatial Maximum Covariance Analysis) Provide regularized maximum covariance analysis incorporating smoothness, sparseness and orthogonality of couple patterns by using the alternating direction method of multipliers algorithm. The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D (Wang and Huang, 2018 ). Package: r-cran-spatopic Architecture: arm64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 940 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), libstdc++6 (>= 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_arm64.deb Size: 612080 MD5sum: 288272ea252dcfd8bac382cf536b4c66 SHA1: 260447244d643168485962f0dc1963f52d2e270a SHA256: 6399225c87027af0429ab7ecfe8255de2cef87edf02cd8862e6bcbde71d9818e SHA512: 3203e97528e4529d02d0f1641ab8fbc54227ca12b0b391a7d93b7514787da4361ee842ca9c0371bfc438740a80c1476c324dd9c3e5e082e3cb104c8016e50f4f Homepage: https://cran.r-project.org/package=SpaTopic Description: CRAN Package 'SpaTopic' (Topic Inference to Identify Tissue Architecture in MultiplexedImages) A novel spatial topic model to integrate both cell type and spatial information to identify the complex spatial tissue architecture on multiplexed tissue images without human intervention. The Package implements a collapsed Gibbs sampling algorithm for inference. 'SpaTopic' is scalable to large-scale image datasets without extracting neighborhood information for every single cell. For more details on the methodology, see . Package: r-cran-spatpca Architecture: arm64 Version: 1.3.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 826 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_arm64.deb Size: 404408 MD5sum: e10517aef24186f89602aba27b2e8d25 SHA1: 9d17588ae776c5f5c0f20016a1d7b77553cc1f16 SHA256: 6fde845ae2558c278e91f5279b473dc3850112b3c7030e7f56c126df073a16f4 SHA512: 63a15f64bb74f4473636b241ad2543c29613e7b02abda6aeb93d607449049b8a2771920ba48a862df81d115d6012a3aba01ca23d29f1e463d2805501617f0594 Homepage: https://cran.r-project.org/package=SpatPCA Description: CRAN Package 'SpatPCA' (Regularized Principal Component Analysis for Spatial Data) Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, ). The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D. Package: r-cran-spatpomp Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2260 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-pomp, r-cran-foreach, r-cran-dplyr, r-cran-tidyr, r-cran-stringr, r-cran-abind, r-cran-rlang, r-cran-ggplot2 Suggests: r-cran-doparallel, r-cran-dorng Filename: pool/dists/resolute/main/r-cran-spatpomp_1.1.0-1.ca2604.1_arm64.deb Size: 1978720 MD5sum: e6fafd17a20c8b54d48a2ae14af6857b SHA1: 35de78a2e8b0d525733716a8e49a3bbe5eaa51e0 SHA256: 3bfd113db99e5613da10bdb8f9f31f5cd120328d1cad43f1dba4a01a159deefb SHA512: d8b336a87fdb4c6edeb65b5cd2e8741f9a4c7a847773d748464c3fbf0de007a85a09a041bc50dda310ffb542ca445f8349195367a1a92f393f27b5cdc4ca256e Homepage: https://cran.r-project.org/package=spatPomp Description: CRAN Package 'spatPomp' (Inference for Spatiotemporal Partially Observed Markov Processes) Inference on panel data using spatiotemporal partially-observed Markov process (SpatPOMP) models. The 'spatPomp' package extends 'pomp' to include algorithms taking advantage of the spatial structure in order to assist with handling high dimensional processes. See Asfaw et al. (2024) for further description of the package. Package: r-cran-spatstat.explore Architecture: arm64 Version: 3.8-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3829 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-nlme, r-cran-spatstat.utils, r-cran-spatstat.sparse, r-cran-goftest, r-cran-matrix, r-cran-abind Suggests: r-cran-sm, r-cran-gsl, r-cran-locfit, r-cran-spatial, r-cran-fftwtools, r-cran-spatstat.linnet, r-cran-spatstat.model, r-cran-spatstat Filename: pool/dists/resolute/main/r-cran-spatstat.explore_3.8-0-1.ca2604.1_arm64.deb Size: 3539322 MD5sum: b92b615d4ce036a90ef95b445e231064 SHA1: 9071fb37afda09034eefe5cd9b81275177e5d605 SHA256: 4838662fd1494a509691fe1128ca3dd6956efbca0b8ae328bbe1c043fb3135e2 SHA512: b79ee33effe76917bda5627eda9865bd3cc8d2629b79e3ccd9c3aa5c2c8eba80270c4919d5af5869ee71bd361b01c8cab514b73163a897320923f31e3c432eb4 Homepage: https://cran.r-project.org/package=spatstat.explore Description: CRAN Package 'spatstat.explore' (Exploratory Data Analysis for the 'spatstat' Family) Functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Package: r-cran-spatstat.geom Architecture: arm64 Version: 3.7-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4661 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.utils, r-cran-deldir, r-cran-polyclip Suggests: r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.linnet, r-cran-spatial, r-cran-fftwtools, r-cran-spatstat Filename: pool/dists/resolute/main/r-cran-spatstat.geom_3.7-3-1.ca2604.1_arm64.deb Size: 4123378 MD5sum: e8f35200697f9e588138b304825cb058 SHA1: 052cccc1562585e0f1707dd5df0aa16ec9ed22df SHA256: 68df85fa23aae8cbd1a5f05b0ee621779528b160af215d0818e08ae971f98033 SHA512: 62a473b08ab77bbb341b44295bd848f66206a71d9f6bc73756046585aee2f02b7196c375c024767d369a5299a879a4a08707df51a894393f8391cfbd8ff2ce71 Homepage: https://cran.r-project.org/package=spatstat.geom Description: CRAN Package 'spatstat.geom' (Geometrical Functionality of the 'spatstat' Family) Defines spatial data types and supports geometrical operations on them. Data types include point patterns, windows (domains), pixel images, line segment patterns, tessellations and hyperframes. Capabilities include creation and manipulation of data (using command line or graphical interaction), plotting, geometrical operations (rotation, shift, rescale, affine transformation), convex hull, discretisation and pixellation, Dirichlet tessellation, Delaunay triangulation, pairwise distances, nearest-neighbour distances, distance transform, morphological operations (erosion, dilation, closing, opening), quadrat counting, geometrical measurement, geometrical covariance, colour maps, calculus on spatial domains, Gaussian blur, level sets of images, transects of images, intersections between objects, minimum distance matching. (Excludes spatial data on a network, which are supported by the package 'spatstat.linnet'.) Package: r-cran-spatstat.knet Architecture: arm64 Version: 3.1-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2267 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.sparse, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat.linnet, r-cran-spatstat, r-cran-spatstat.utils, r-cran-matrix Filename: pool/dists/resolute/main/r-cran-spatstat.knet_3.1-3-1.ca2604.1_arm64.deb Size: 2230016 MD5sum: 3806907ac370c459de8c131b7cf760cd SHA1: ae5600b9485f42a192ba7fc764caa4bef6401efa SHA256: 51a15891b66bad1eee12e27e377934733a084908e5310cd6169ffa9a554ce2c0 SHA512: 6dbbbd7b40677c31b89209ba5c2a856f5b5c958ae494c7685fa0b582376e682f62da4f61ea9dec8ce5a071c7674a72e32606bd474815f3b4b83706ea8631b9dd Homepage: https://cran.r-project.org/package=spatstat.Knet Description: CRAN Package 'spatstat.Knet' (Extension to 'spatstat' for Large Datasets on a Linear Network) Extension to the 'spatstat' family of packages, for analysing large datasets of spatial points on a network. The geometrically- corrected K function is computed using a memory-efficient tree-based algorithm described by Rakshit, Baddeley and Nair (2019). Package: r-cran-spatstat.linnet Architecture: arm64 Version: 3.5-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1947 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.random, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-matrix, r-cran-spatstat.utils, r-cran-spatstat.sparse Suggests: r-cran-goftest, r-cran-locfit, r-cran-spatstat Filename: pool/dists/resolute/main/r-cran-spatstat.linnet_3.5-0-1.ca2604.1_arm64.deb Size: 1763228 MD5sum: b13f73203f82e069c3a5ade5e4f3dfb4 SHA1: 9961346465655a83b905ce78abac3aed59a033c4 SHA256: d9aae01bf1331eaef744c3d681c0b97e3eac1a55c841e0e4b38d99d6abec10d6 SHA512: 89230fd8a698e169e8d253459f782fb44e107c34321375c9e4bc9b6762f705be9681855b6a64c868db2c9673ec590f1840cc243b414df43e78e0cb22947e84ba Homepage: https://cran.r-project.org/package=spatstat.linnet Description: CRAN Package 'spatstat.linnet' (Linear Networks Functionality of the 'spatstat' Family) Defines types of spatial data on a linear network and provides functionality for geometrical operations, data analysis and modelling of data on a linear network, in the 'spatstat' family of packages. Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks. Data types defined on a network include point patterns, pixel images, functions, and tessellations. Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. Random point patterns on a network can be generated using a variety of models. Package: r-cran-spatstat.model Architecture: arm64 Version: 3.7-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3865 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_arm64.deb Size: 3538024 MD5sum: 74a2662622535d14495c812e5977854f SHA1: ae253414f83a8ffcd8894e06c4441283ec05b070 SHA256: 074a67f6b610e5fde74d9a8a0dcbe4251179ee9ad5946c63d971d3085d74f6ba SHA512: 34d7d72818e4999e5f4f891871513ab144a447f5e4599c180a583e9e39a61ee2ed196ce845301d08a56f93b5c3dbc5d3c35c2084e6659c6a7bf6d8c74fda0d38 Homepage: https://cran.r-project.org/package=spatstat.model Description: CRAN Package 'spatstat.model' (Parametric Statistical Modelling and Inference for the'spatstat' Family) Functionality for parametric statistical modelling and inference for spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Supports parametric modelling, formal statistical inference, and model validation. Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots. Package: r-cran-spatstat.random Architecture: arm64 Version: 3.4-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1428 Depends: libc6 (>= 2.35), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-spatstat.data, r-cran-spatstat.univar, r-cran-spatstat.geom, r-cran-spatstat.utils Suggests: r-cran-spatial, r-cran-spatstat.linnet, r-cran-spatstat.explore, r-cran-spatstat.model, r-cran-spatstat, r-cran-gsl Filename: pool/dists/resolute/main/r-cran-spatstat.random_3.4-5-1.ca2604.1_arm64.deb Size: 1230182 MD5sum: 69043ec023b42b66a041c74b4523a223 SHA1: d694c515e4b4c758d65c96c76275529a41dbecc4 SHA256: 6eae2a31be9e0335536778c723301d2bcdca3231c60f0c228359547ac63d24e7 SHA512: f73be5528cb796a6e35219dbb222174bde6f2923c856a6f7b75ced19ecca764edc61f02e26da75d72581b9c23f958bafdd3505e0d50bb760c3a3116f31a665c8 Homepage: https://cran.r-project.org/package=spatstat.random Description: CRAN Package 'spatstat.random' (Random Generation Functionality for the 'spatstat' Family) Functionality for random generation of spatial data in the 'spatstat' family of packages. Generates random spatial patterns of points according to many simple rules (complete spatial randomness, Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns (thinning, random shift, jittering), simulated realisations of random point processes including simple sequential inhibition, Matern inhibition models, Neyman-Scott cluster processes (using direct, Brix-Kendall, or hybrid algorithms), log-Gaussian Cox processes, product shot noise cluster processes and Gibbs point processes (using Metropolis-Hastings birth-death-shift algorithm, alternating Gibbs sampler, or coupling-from-the-past perfect simulation). Also generates random spatial patterns of line segments, random tessellations, and random images (random noise, random mosaics). Excludes random generation on a linear network, which is covered by the separate package 'spatstat.linnet'. Package: r-cran-spatstat.sparse Architecture: arm64 Version: 3.2-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 348 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_arm64.deb Size: 238222 MD5sum: 886a3d513236a5c27f0aa36b0ac70646 SHA1: 1c4ad640abcc7413e500b55979587889df5a36d1 SHA256: 64d776e29e2d6ee5b226f9a480ba6b44a1fe2aee3301deda6603ce54f11dadad SHA512: b07a58bc479fb1dcc623cbd1a18099f2c8670ff81bfe616a8289fba235476393c03fe8ad8ff48fe0385d8bf9534cef9fb429b52416bec0562134a695e137a8dd Homepage: https://cran.r-project.org/package=spatstat.sparse Description: CRAN Package 'spatstat.sparse' (Sparse Three-Dimensional Arrays and Linear Algebra Utilities) Defines sparse three-dimensional arrays and supports standard operations on them. The package also includes utility functions for matrix calculations that are common in statistics, such as quadratic forms. Package: r-cran-spatstat.univar Architecture: arm64 Version: 3.2-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 466 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0, r-cran-spatstat.utils Filename: pool/dists/resolute/main/r-cran-spatstat.univar_3.2-0-1.ca2604.1_arm64.deb Size: 355086 MD5sum: f7d51ad430ef86ab816dc26768b861d6 SHA1: e3a3a7cd424dbae8fc6d3d27bfa717d4f886d037 SHA256: 0583d1451c3d3796b89905ddb839990520106fd72d1e0894749e125c003d6e67 SHA512: 5ec2303096709aed407756cc1391062753c023c75610d3d6d3f9866a241e6169ba1e3282b024bbaee9385dc5be676b27e9025db72845c8e3b9c567e0224518e6 Homepage: https://cran.r-project.org/package=spatstat.univar Description: CRAN Package 'spatstat.univar' (One-Dimensional Probability Distribution Support for the'spatstat' Family) Estimation of one-dimensional probability distributions including kernel density estimation, weighted empirical cumulative distribution functions, Kaplan-Meier and reduced-sample estimators for right-censored data, heat kernels, special distributions, kernel properties, quantiles and integration. Package: r-cran-spatstat.utils Architecture: arm64 Version: 3.2-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 539 Depends: r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-spatstat.model Filename: pool/dists/resolute/main/r-cran-spatstat.utils_3.2-3-1.ca2604.1_arm64.deb Size: 401658 MD5sum: a6a7773c9f9dee315ea195d70067dcb9 SHA1: 69e6694cd5290d5cbf7ef97fb0c43613087a4029 SHA256: a8221f732a7a6ab9e648fd38b78b3cd7e46a85bf926c3d1e7d9b6a7069aaa067 SHA512: b86072288f979a4bae8fbc232b8f6816e9dfad0e5867c13c47fdbd84da563b681da000b8e0b0d54fd04ad43cc132ce836a2e0788a04375ba622c4b6ce6539a8b 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: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1204 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_arm64.deb Size: 654068 MD5sum: e07ed2cbd1931bb0a9bc37e0dccc7937 SHA1: 572d8dfa3cb37f59e766a9bf6f4e51abd9f6099a SHA256: c4db82c4eb9bded7a470c4343ff745c54342d820491b0ab087d9b35d3db72c34 SHA512: 8541b337f795cef2a92ac13bcfd7a0111c0936638f7f666a839ffddf932a0f1384fdd469227eb2d5d6e0c62243ff3f425027479db5ee217228e4e1dda3813ebc Homepage: https://cran.r-project.org/package=spbal Description: CRAN Package 'spbal' (Spatially Balanced Sampling Algorithms) Encapsulates a number of spatially balanced sampling algorithms, namely, Balanced Acceptance Sampling (equal, unequal, seed point, panels), Halton frames (for discretizing a continuous resource), Halton Iterative Partitioning (equal probability) and Simple Random Sampling. Robertson, B. L., Brown, J. A., McDonald, T. and Jaksons, P. (2013) . Robertson, B. L., McDonald, T., Price, C. J. and Brown, J. A. (2017) . Robertson, B. L., McDonald, T., Price, C. J. and Brown, J. A. (2018) . Robertson, B. L., van Dam-Bates, P. and Gansell, O. (2021a) . Robertson, B. L., Davies, P., Gansell, O., van Dam-Bates, P., McDonald, T. (2025) . Package: r-cran-spbayes Architecture: arm64 Version: 0.4-8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1337 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), 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_arm64.deb Size: 1117460 MD5sum: fa5279bfd694b7245d37ab78812a6c61 SHA1: 453a54c85d5bcd1596af1d40c2fbd8f8f18ebb69 SHA256: 094e587ab222d93fa3ed45dfc6c5cb987ab6384df345bfff099c5ed37a1fbc4a SHA512: 192a2b1b25e873959365f0ef4cfc2137ddd58101b972580839543f14675b401276e822b0ec0e5d734002b2aef77a024e2113a359ce2bc586a7864304ad45b4b8 Homepage: https://cran.r-project.org/package=spBayes Description: CRAN Package 'spBayes' (Univariate and Multivariate Spatial-Temporal Modeling) Fits univariate and multivariate spatio-temporal random effects models for point-referenced data using Markov chain Monte Carlo (MCMC). Details are given in Finley, Banerjee, and Gelfand (2015) and Finley and Banerjee . Package: r-cran-spbayessurv Architecture: arm64 Version: 1.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2676 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_arm64.deb Size: 954796 MD5sum: c366521dddcafd086fa6dd75c9617bea SHA1: b0fb5b8c46f8980ca4b4927dd618dd0e3391c1f1 SHA256: 2e970032fc60cfdfa421b2058db45254193a445377433c2f767d5ad5e987d1d3 SHA512: 6074b60d687e39b94bd027c2d30941b364bc011e655c7ca1f03a4dc64a5f53efe06c5334b93274a76e11fc690354c20d054c3c783c7f8a78dcfb6c72d6adf0e4 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: arm64 Version: 2.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1122 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_arm64.deb Size: 542110 MD5sum: 2be72ee78554693c189f48907073f86b SHA1: 86a9689ad6d0c3d5a991d73e89c28bb36c9bb946 SHA256: 26f18d964d32d1af7e2f01e62623c14bc451af352bb77d83657af08be21b5b3e SHA512: c38a897853369bd922caa5ae58c5eebe2d9371f5206a037a1937bee8208cdeae6d557d441b512a9aa2ecd35a5f9e4ad55e84b3da472653556afecce009a60090 Homepage: https://cran.r-project.org/package=spBPS Description: CRAN Package 'spBPS' (Bayesian Predictive Stacking for Scalable Geospatial TransferLearning) Provides functions for Bayesian Predictive Stacking within the Bayesian transfer learning framework for geospatial artificial systems, as introduced in "Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach" (Presicce and Banerjee, 2025) . This methodology enables efficient Bayesian geostatistical modeling, utilizing predictive stacking to improve inference across spatial datasets. The core functions leverage 'C++' for high-performance computation, making the framework well-suited for large-scale spatial data analysis in parallel and distributed computing environments. Designed for scalability, it allows seamless application in computationally demanding scenarios. Package: r-cran-spbsampling Architecture: arm64 Version: 1.3.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 616 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_arm64.deb Size: 445762 MD5sum: 0c226af4cb47895b2ae018e6bc231fb6 SHA1: bac399c56c72e3d4af7354676b55f04574bab91a SHA256: 7d0b1afa164e76230e6f3a2d0c499bc071df42049a597a9782e2780d6a00e5f4 SHA512: 6d562e11d4c66a368f4017536c61299d8f173d94e80c58e42d0b43173327cda66862f41e55fcad695fb343a4aebf48406a3445556e02e47a5e3b58d19ea6c294 Homepage: https://cran.r-project.org/package=Spbsampling Description: CRAN Package 'Spbsampling' (Spatially Balanced Sampling) Selection of spatially balanced samples. In particular, the implemented sampling designs allow to select probability samples well spread over the population of interest, in any dimension and using any distance function (e.g. Euclidean distance, Manhattan distance). For more details, Pantalone F, Benedetti R, and Piersimoni F (2022) , Benedetti R and Piersimoni F (2017) , and Benedetti R and Piersimoni F (2017) . The implementation has been done in C++ through the use of 'Rcpp' and 'RcppArmadillo'. Package: r-cran-spc Architecture: arm64 Version: 0.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1246 Depends: libc6 (>= 2.38), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-spc_0.7.2-1.ca2604.1_arm64.deb Size: 789768 MD5sum: 8cb454f258e804103234df962599b1a1 SHA1: f464f73aed77b873b8fc2c00949d4249fcfedac0 SHA256: 964e4496ac9266583bc04785e7e2eefa96cfd116e1a35b3aa9f996bfbb08356a SHA512: 8696cab8363c237047d1d87a19e09466803b310847594a9833011a67b36f773c5b047d4e088c0165ad1a4558b733def99ad7c0e36fcf22a7992fb680648b4904 Homepage: https://cran.r-project.org/package=spc Description: CRAN Package 'spc' (Statistical Process Control -- Calculation of ARL and OtherControl Chart Performance Measures) Evaluation of control charts by means of the zero-state, steady-state ARL (Average Run Length) and RL quantiles. Setting up control charts for given in-control ARL. The control charts under consideration are one- and two-sided EWMA, CUSUM, and Shiryaev-Roberts schemes for monitoring the mean or variance of normally distributed independent data. ARL calculation of the same set of schemes under drift (in the mean) are added. Eventually, all ARL measures for the multivariate EWMA (MEWMA) are provided. Package: r-cran-spcf Architecture: arm64 Version: 0.1.1-1.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_arm64.deb Size: 1028620 MD5sum: 3938a83725aaba22858f947af8d72fec SHA1: c939cb0096434e69c49a5e5f32941c57f9e9ffde SHA256: 4c7edae99ee785176ede4c7439e0fe2b709a178f07f5b4241c74c10e9729f2e9 SHA512: 3980651e66f0e829269daf4fa6deb9813c2415bfaa3617a584ebcb0d8c84d10ddb9cc4cc33f8d057629714dcfb7fe25db2f706be88fd2240f59d1c2712bc3c72 Homepage: https://cran.r-project.org/package=spCF Description: CRAN Package 'spCF' (Coarse-to-Fine Spatial Modeling) Provides functions for coarse-to-fine spatial modeling (CFSM), enabling fast spatial prediction, regression, and uncertainty quantification. This method is suitable for moderate to large samples. For further details, see Murakami et al. (2026) . Package: r-cran-spcp Architecture: arm64 Version: 1.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1128 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 602114 MD5sum: 7320ba1c5a3c23c6564cccee2585ddf9 SHA1: c617988a198a9da138b13f74663dd88684895794 SHA256: 625631d417ad51a93858fe39a675ac1849c19bab9b0a193aebff0baf061cfe03 SHA512: 171c1179a31217249979681c1a5f056964d3a7156e80522e4735e82cca6cf2ee435dd3cba4b87f47b4231ba8e671d5abcc85236e5db1fa7deb91969208a20cf0 Homepage: https://cran.r-project.org/package=spCP Description: CRAN Package 'spCP' (Spatially Varying Change Points) Implements a spatially varying change point model with unique intercepts, slopes, variance intercepts and slopes, and change points at each location. Inference is within the Bayesian setting using Markov chain Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget), probit or Tobit link and the five spatially varying parameter are modeled jointly using a multivariate conditional autoregressive (MCAR) prior. The MCAR is a unique process that allows for a dissimilarity metric to dictate the local spatial dependencies. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in the corresponding paper published in Spatial Statistics by Berchuck et al (2019): "A spatially varying change points model for monitoring glaucoma progression using visual field data", . Package: r-cran-spcr Architecture: arm64 Version: 2.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 183 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-spcr_2.1.1-1.ca2604.1_arm64.deb Size: 96058 MD5sum: a5164c46fc73cc62dd0b3ab63a6f2810 SHA1: df3647241d97f5eb8ee8280ab749654ceb0ce9c4 SHA256: c5c8bd529b5ae510cebf5a638c7ff25a71632f9175bf558190b0001a062cd24a SHA512: 0984e53578fb389b6911726265d8782fdc4c6f4c521306039cabcd9f8420584f210e56f2fcd961713dbf719dff0ce3fd5f32af5957c7c5828c7a65f43b300565 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. 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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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Package: r-cran-spef Architecture: arm64 Version: 1.0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 585 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_arm64.deb Size: 301460 MD5sum: b377a32cc670d8055c256b1e7483acaf SHA1: c61365b91af49ba147af19b7322fcbdde81ffaac SHA256: b5a891930a0b455a3ae587b776b640d857fd675c1d45e220b5058cab07acc465 SHA512: 23860c1f27f366a319f364ba3340f3e2c322a55ee307259d411239ae5b3938ecb7fef57177695820f5a104d303bbebd4f38300075a17921dcc07b84f170184fd 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) . 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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: arm64 Version: 0.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 868 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_arm64.deb Size: 476678 MD5sum: bdca024e6c83ef5793e6e607543b33c3 SHA1: bbb8a96b23a6ff26a86dc0ad04e651d47db9ef40 SHA256: 9db8a448fa3665d16c2a34879978daf862f49edc76089132432a3ff00d22731a SHA512: 734b91d63f71ec231c2a0a0b572f5574ee50c7e146cef0c9206a8b1db9d49b53ffbb68ad5dcfdbbddddac8fca651d447b098fc1c1ec032af59d66ef2d1f68e17 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: arm64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 649 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_arm64.deb Size: 408410 MD5sum: ee78f689542749df5d92ef4cbf65b1ae SHA1: eb1a324d224af31960f660a4e63a8c1b707e28bd SHA256: 44598fb661f208e2fdc5450fa9889056056313b06ff579cdba5193dfaffffe0e SHA512: 6fafa27cf445322c37ac0a1a72ec920b7889efce77519d6ca5640c74422977ba45987dcb086811123f1103b28740e452ba3ad058bcfdc5b10b910d510dbdcfdd 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: arm64 Version: 1.0-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 596 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-spgs_1.0-4-1.ca2604.1_arm64.deb Size: 496724 MD5sum: d2c3b262712ca759631c5c8bd85909e8 SHA1: 0d5da85d64427728d813881bacbbafe24bc518bc SHA256: 19eb76fcbacc8c648b75a5e52e6a01324ae86dffecd10eb78755f921b2b34f9e SHA512: 85960b71f591e4abdd86c1f70abcdc6b2bc672ed1a4e6fe19afdad5adefae241e3c205485597a9338322335a840f4950d4c77f9c529eec834eae195868441c81 Homepage: https://cran.r-project.org/package=spgs Description: CRAN Package 'spgs' (Statistical Patterns in Genomic Sequences) A collection of statistical hypothesis tests and other techniques for identifying certain spatial relationships/phenomena in DNA sequences. 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Package: r-cran-sphunif Architecture: arm64 Version: 1.4.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2046 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 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_arm64.deb Size: 1276290 MD5sum: b2261a637bcaf46f9fbfe7d49bf9862a SHA1: d77d9fe3af60e843e0ac2b3e03f918544c852dee SHA256: 1309c067dc59e3b5c4ea1512caad068a36c4925d1d9622e9c42b9cc057ec3d1c SHA512: 52286a72d9d2eb3dade11b8074854c2ba9810a5204829d8591cb1a5fe395ec3a8c201ac7d6b4015e183fa90adb538bb2ba715ecf6d9668fa5dcf1ed3de55c894 Homepage: https://cran.r-project.org/package=sphunif Description: CRAN Package 'sphunif' (Uniformity Tests on the Circle, Sphere, and Hypersphere) Implementation of uniformity tests on the circle and (hyper)sphere. The main function of the package is unif_test(), which conveniently collects more than 35 tests for assessing uniformity on S^{p-1} = {x in R^p : ||x|| = 1}, p >= 2. The test statistics are implemented in the unif_stat() function, which allows computing several statistics for different samples within a single call, thus facilitating Monte Carlo experiments. Furthermore, the unif_stat_MC() function allows parallelizing them in a simple way. The asymptotic null distributions of the statistics are available through the function unif_stat_distr(). The core of 'sphunif' is coded in C++ by relying on the 'Rcpp' package. The package also provides several novel datasets and gives the replicability for the data applications/simulations in García-Portugués et al. (2021) , García-Portugués et al. (2023) , Fernández-de-Marcos and García-Portugués (2024) , and García-Portugués et al. (2025) . Package: r-cran-spiderbar Architecture: arm64 Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 381 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_arm64.deb Size: 105694 MD5sum: 98c2a0cef93286ae9b7747b224a1229f SHA1: bafdadf86f4e342f6b6e45227617a42dc0a652d6 SHA256: d6fcb6ee035f7525fd2dda9287015f04ab494344866078307b919985a4ce9fe0 SHA512: 12ad71ebdd4804d2db8c4f098aa7b74d583cda71ce121458e0213d50fe003858ed53640021f2c1ca5f1e9585b8b4cfaf818fe23c90d7aea0cd810b31a98b95da Homepage: https://cran.r-project.org/package=spiderbar Description: CRAN Package 'spiderbar' (Parse and Test Robots Exclusion Protocol Files and Rules) The 'Robots Exclusion Protocol' documents a set of standards for allowing or excluding robot/spider crawling of different areas of site content. 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Package: r-cran-spinbayes Architecture: arm64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 992 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 667276 MD5sum: 89c875f9fcae57567e619cabdcf08964 SHA1: edf8a55937ae8fa81a98c435250c4f4df5faecaf SHA256: 760dd0400c2b458cdd7d66f90b002ac2f0418a0dfa22601d073b664eb7b81804 SHA512: b3adbc5ccf4891bd4fab4c5610fa1d8782ff0b16b0fb204ef79d5664a7da4e0755ab50ee60dc2bfe8280297089cf2da9a650e724736f6bc69d0ccb024c41eff6 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: arm64 Version: 0.5.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2013 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 1074006 MD5sum: 796a26d7a77f07531fd37b1a097c9383 SHA1: cd96bcc22c4d3a0fa1f7d9f4ccb2289e7b04673e SHA256: d2b72efc0ccf74786a08856fdadb442f60d6b50a48a33177ab0ae735e9e3a54c SHA512: 50f3762660bf6821d3ddfe0cb88a3e8ae4828fbf1cb791249c1010204dd0f369b2bbc9c3e66a0555399a772848a9610fd9d94909d1e02f5d82798234cc64e744 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. It also contains a C++ head-only library integrated with Rcpp. See Wang and Yan (2021) for details. Package: r-cran-split Architecture: arm64 Version: 1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 340 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, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-split_1.3-1.ca2604.1_arm64.deb Size: 156980 MD5sum: 09456aa188779409ccc72f3d8c55ad67 SHA1: c644df5a070b98402b9585b9de460f6dee21d3ae SHA256: e0caba8c15bbf6970f2f6bd7c300d9383a7be9a7a0b9f4b847c125366cafb318 SHA512: 6c443cb8fbc666347bc190a4e3926502b50058e8af546389f424ff58aa47a9c8cdcc3a6fb3fe13ba0f2a05734dc01299e7d0d2bfb74aa5756ba9889d1967570b Homepage: https://cran.r-project.org/package=SPlit Description: CRAN Package 'SPlit' (Split a Dataset for Training and Testing) Procedure to optimally split a dataset for training and testing. '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: arm64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 475 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_arm64.deb Size: 174454 MD5sum: 4a5aa20cecabba61c2aaab0b4efbe2c5 SHA1: 3d506c756316bb4be04cd8bbb37635fa94c3ca2c SHA256: 73d1ec373741544d90c99ddf430ec82886253ea950653633b122baaeabbb3137 SHA512: 689dce228c6f1b1d3cf3d3de6971244845fa4755c9140c8e20bf9ca3b3123c18d390d8bd33678837bb7574dd9590db1056c14d738e6dd7cf664728e41046532a 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: arm64 Version: 0.8.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 762 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 388340 MD5sum: 3697463f87d9e1fe676befce69410d5f SHA1: 50d0bc997bd9ec10857097978677014fddedb1d2 SHA256: 671e87f710aa7c278bae97c80801a9ebd8ffc9afa9e28326749a262c591eb138 SHA512: a47bc112c6480a99228245f3ff44928f1e190bcab2698bd1a6690f9536c570cb5526dde27ed28ae9f9fadf04b5f3ffc152ca50482c8c6b698ab6906021a12715 Homepage: https://cran.r-project.org/package=splithalf Description: CRAN Package 'splithalf' (Calculate Task Split Half Reliability Estimates) Estimate the internal consistency of your tasks with a permutation based split-half reliability approach. Unofficial release name: "I eat stickers all the time, dude!". Package: r-cran-splitreg Architecture: arm64 Version: 1.0.3-1.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_arm64.deb Size: 97118 MD5sum: d92b7cd18fdf753c0a92763af6e390b5 SHA1: fae5109a952fb476db7ac7024e093d67a89251e4 SHA256: 47c2da6f40c13fde458580ad0ead8658175c7d6eb5280f8a5dc07fba93a5051b SHA512: a74251034a581721d2b45a9afcbbe19f24d5a2aab5970e3a026c265f30d90625a09e1ef024055fddd46e5e088bfe623bddbae68e29753209bebbfb666cfbaccf 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: arm64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 429 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_arm64.deb Size: 215650 MD5sum: da3398ce87f3671c48c29dc6e1cea46a SHA1: 89f60979c5206d6f3fdb5c18c6277e4a2109fe42 SHA256: 3ff0877b2012f26ec5b4d56cc18446b0131faac1157fffaa1c14eb40f1fa890c SHA512: c90a50673beffa8c2baaf289b1eec59b0c0280fd8a49fa1f98b66b95a7c3e73db31f35df96a730370a9e9ba72588f65bd1855f8cdc1be6a77fef47147dcada13 Homepage: https://cran.r-project.org/package=splmm Description: CRAN Package 'splmm' (Simultaneous Penalized Linear Mixed Effects Models) Contains functions that fit linear mixed-effects models for high-dimensional data (p>>n) with penalty for both the fixed effects and random effects for variable selection. The details of the algorithm can be found in Luoying Yang PhD thesis (Yang and Wu 2020). The algorithm implementation is based on the R package 'lmmlasso'. Reference: Yang L, Wu TT (2020). Model-Based Clustering of Longitudinal Data in High-Dimensionality. Unpublished thesis. Package: r-cran-splus2r Architecture: arm64 Version: 1.3-5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 511 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_arm64.deb Size: 328152 MD5sum: 3bd909d0491dd0dcbac4f9beb4229d6f SHA1: a31f32a0e9940ab6bc8508148c7e2260784b419b SHA256: 62dcc07d961054aaadd9985bff7de77c432255a12c844612a54e73891206619e SHA512: 44f2363e13ff4ad45acc1d7a2d3ca32fdd5c2cf6a6501558ce4862a7797a54732769e03ccd641146906d34f8596c6a7423116af6804f7b78e4bf91bfbdef3fb9 Homepage: https://cran.r-project.org/package=splus2R Description: CRAN Package 'splus2R' (Supplemental S-PLUS Functionality in R) Currently there are many functions in S-PLUS that are missing in R. To facilitate the conversion of S-PLUS packages to R packages, this package provides some missing S-PLUS functionality in R. Package: r-cran-splustimedate Architecture: arm64 Version: 2.5.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1018 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-splustimedate_2.5.10-1.ca2604.1_arm64.deb Size: 657854 MD5sum: 4c55ae92ec50bc1bf171b14d05d3bf4a SHA1: a1ff724504988611196a980db8eff4bfa8d59937 SHA256: a9831d1aaf93d8dc8417d3f2b74cdca9e554444eaaa875c777f1d6a930826741 SHA512: ca6256c420392929b1bf4f53e9fe9409d1f7cf0dca6b2bf43f26139f1a59bb21428bef662d8588406e45187222e8554315b34a3b762978a428c82e42511f00ef Homepage: https://cran.r-project.org/package=splusTimeDate Description: CRAN Package 'splusTimeDate' (Times and Dates from 'S-PLUS') A collection of classes and methods for working with times and dates. The code was originally available in 'S-PLUS'. Package: r-cran-splustimeseries Architecture: arm64 Version: 1.5.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1432 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_arm64.deb Size: 1062280 MD5sum: 54109649d43049597e1d9dbad4914bfc SHA1: 853a9b1a1d407c9597ab363924adf05065989d5e SHA256: 89dd6b3d2544d6c5b738bc4afa602634bde1faf2a6830b3c76c7d5c95b09b0df SHA512: 0414a57940954cdbf891870076cfed9815966f325a3a17fe309f3c4cb0003d49bb3bfc5a006e0ba9c2e45e4a9f03e6107eabdbd04063d7a042eff68c1dc27a4f Homepage: https://cran.r-project.org/package=splusTimeSeries Description: CRAN Package 'splusTimeSeries' (Time Series from 'S-PLUS') A collection of classes and methods for working with indexed rectangular data. The index values can be calendar (timeSeries class) or numeric (signalSeries class). Methods are included for aggregation, alignment, merging, and summaries. The code was originally available in 'S-PLUS'. Package: r-cran-spmc Architecture: arm64 Version: 0.3.15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 602 Depends: libc6 (>= 2.29), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-spmc_0.3.15-1.ca2604.1_arm64.deb Size: 420216 MD5sum: 56c91060be679db8a2f583afbabfe2f7 SHA1: 61e569d2b563acc2571c7d5a41c263f642108183 SHA256: 6cc4b00e97969b40a2ac5cea1a114026547164f44bda26a5801b9e6259ba6419 SHA512: 940efdb8d1ac04895b4853c07268c7cd9e12c90300545740ba0d68530baea6e59c79551bfff58234bc9a20436e5d956fcff42aa5062a0d7eecd9397c8b8fb90d Homepage: https://cran.r-project.org/package=spMC Description: CRAN Package 'spMC' (Continuous-Lag Spatial Markov Chains) A set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields. More details on the methodology are discussed in Sartore (2013) and Sartore et al. (2016) . Package: r-cran-spnetwork Architecture: arm64 Version: 0.4.4.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6013 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_arm64.deb Size: 4932278 MD5sum: 4f8457a610fa31cf856cd182fd35769a SHA1: 5549e425be6046c62fa579748b6ee025a483ab52 SHA256: ce59b353ace5ea6d550beb996e9983682949dea2ad6f723517d2aa8796977f1a SHA512: 52c605c8a43174b260dfd6ef5c0828e2f2d9e327bdfa39264045153487d46cb63c4c0799cf931b064adfde488c439b8de198c331bf219098eba79c2cab1c5740 Homepage: https://cran.r-project.org/package=spNetwork Description: CRAN Package 'spNetwork' (Spatial Analysis on Network) Perform spatial analysis on network. Implement several methods for spatial analysis on network: Network Kernel Density estimation, building of spatial matrices based on network distance ('listw' objects from 'spdep' package), K functions estimation for point pattern analysis on network, k nearest neighbours on network, reachable area calculation, and graph generation References: Okabe et al (2019) ; Okabe et al (2012, ISBN:978-0470770818);Baddeley et al (2015, ISBN:9781482210200). Package: r-cran-spnn Architecture: arm64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 195 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_arm64.deb Size: 68642 MD5sum: 9f1aca2ac1b58c96c280cd769c531400 SHA1: ef812a0d5b1ea58c4e07d2e62ae520b6512537af SHA256: b41ec1ab5fd308c8d5c4713470ae7d3b53f84c1a3a91e36465a99f9e2d19c18d SHA512: 6b81d90bee517bab834c7a943469b81f42af8ada447595cc7d5b84bd4247d7e9e41d8f3922c21a0d68304baa94cc812006f4da88e7ab7138e03cd6791a3f7a86 Homepage: https://cran.r-project.org/package=spnn Description: CRAN Package 'spnn' (Scale Invariant Probabilistic Neural Networks) Scale invariant version of the original PNN proposed by Specht (1990) with the added functionality of allowing for smoothing along multiple dimensions while accounting for covariances within the data set. It is written in the R statistical programming language. Given a data set with categorical variables, we use this algorithm to estimate the probabilities of a new observation vector belonging to a specific category. This type of neural network provides the benefits of fast training time relative to backpropagation and statistical generalization with only a small set of known observations. Package: r-cran-spnngp Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3443 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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_arm64.deb Size: 3391600 MD5sum: ff40241234326eef1782d476daf694e5 SHA1: 4fc0804e6e22109d4727cae9f41db6358458a167 SHA256: 4143a9bdd3cace5a17bf652b6db2220efaf34624589ff6ed8a34f6d66eed25ca SHA512: 59a84d88834f7a096041047bf1f374754d33c7f50bbfc25ef1f943be30eec5faad61f46f95cf030e362768ce855e9ff4f9166ba3794ba008d3d98d1264fc6bf6 Homepage: https://cran.r-project.org/package=spNNGP Description: CRAN Package 'spNNGP' (Spatial Regression Models for Large Datasets using NearestNeighbor Gaussian Processes) Fits univariate Bayesian spatial regression models for large datasets using Nearest Neighbor Gaussian Processes (NNGP) detailed in Finley, Datta, Banerjee (2022) , Finley, Datta, Cook, Morton, Andersen, and Banerjee (2019) , and Datta, Banerjee, Finley, and Gelfand (2016) . Package: r-cran-spoccupancy Architecture: arm64 Version: 0.8.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3892 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), 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_arm64.deb Size: 3439588 MD5sum: 4c5ffad27dabf641b1962a319f6e474f SHA1: 87b9204f23e2e09b4f64c3abf991b5c5f8a9e16e SHA256: bfd7674e4d4b12fd35e6b2dc84291af81aed81e5dc3c4c852199c0c28513c5f6 SHA512: 130c22663d833b2adebe83bf38d2131cb7b7fbe3d9a1dfcf6c1d33b9e73f6e72226a4dcfbaa2326cf069da957afd446b56ac74dadf909bfa84e4f28d6cb4d2cd Homepage: https://cran.r-project.org/package=spOccupancy Description: CRAN Package 'spOccupancy' (Single-Species, Multi-Species, and Integrated Spatial OccupancyModels) Fits single-species, multi-species, and integrated non-spatial and spatial occupancy models using Markov Chain Monte Carlo (MCMC). Models are fit using Polya-Gamma data augmentation detailed in Polson, Scott, and Windle (2013) . Spatial models are fit using either Gaussian processes or Nearest Neighbor Gaussian Processes (NNGP) for large spatial datasets. Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) and Finley, Datta, and Banerjee (2022) . Provides functionality for data integration of multiple single-species occupancy data sets using a joint likelihood framework. Details on data integration are given in Miller, Pacifici, Sanderlin, and Reich (2019) . Details on single-species and multi-species models are found in MacKenzie, Nichols, Lachman, Droege, Royle, and Langtimm (2002) and Dorazio and Royle , respectively. Package: r-cran-spopt Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2383 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-spdep, r-cran-matrix, r-cran-highs Suggests: r-cran-testthat, r-cran-terra, r-cran-dodgr, r-cran-r5r, r-cran-knitr, r-cran-rmarkdown, r-cran-quarto, r-cran-tidycensus, r-cran-tidyverse, r-cran-mapgl Filename: pool/dists/resolute/main/r-cran-spopt_0.1.2-1.ca2604.1_arm64.deb Size: 1829662 MD5sum: de0feee7a677351a3a1abdf26ace54ee SHA1: 57167641b2bcb2b5111c9bc5bfe2f09680d5782e SHA256: a213b24413111e99e0bcefdfd9f3775f4e838a6acfc7470440590a5598a9cfa9 SHA512: f7d92f9fdd48bbb9a82a549cf8e843f6ba3e16d1c4b66814219fdefb505714992749f93cf38d6869000bec7718a3539c1658d63fa078629d93aa259f966fb1a1 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: arm64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 809 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_arm64.deb Size: 474124 MD5sum: 6b5a179236250c0f205c34d71b0e7478 SHA1: beae0a2c72c69d1cd5892dd54ee633a2e1b11019 SHA256: e0861e5f83b9c78e1b579cb6930010145458a7191f3a7fddd604288449456b1d SHA512: 04779a29bedf742cea4bae7d8f8b9c1c36d2b548ae66010d283ceffd233723b4bfa616023228c950fdff42f4dbd10eb4b01a8cfde5209d03dc713c56ff58b875 Homepage: https://cran.r-project.org/package=sport Description: CRAN Package 'sport' (Sequential Pairwise Online Rating Techniques) Calculates ratings for two-player or multi-player challenges. Methods included in package such as are able to estimate ratings (players strengths) and their evolution in time, also able to predict output of challenge. Algorithms are based on Bayesian Approximation Method, and they don't involve any matrix inversions nor likelihood estimation. Parameters are updated sequentially, and computation doesn't require any additional RAM to make estimation feasible. Additionally, base of the package is written in C++ what makes sport computation even faster. Methods used in the package refer to Mark E. Glickman (1999) ; Mark E. Glickman (2001) ; Ruby C. Weng, Chih-Jen Lin (2011) ; W. Penny, Stephen J. Roberts (1999) . Package: r-cran-spotr Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 895 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_arm64.deb Size: 573262 MD5sum: 90e9d6b2423c7ab75a8e3b44c5dc0306 SHA1: 24ff9248c29552dd21e2840e5cc0eebf1e6ceeaf SHA256: 7e8dcf48ddd06b3f6891a456f6c2289f846382e865ec499cc2669de6c80cfe60 SHA512: ef8d0dacba96cb5100a16f0e36b98e9851fe770c6bf8b8ac09edc75439c5610051ba438f26f852cfe6d830ae634b1ed3b734155848618481ed28e147ff901799 Homepage: https://cran.r-project.org/package=spotr Description: CRAN Package 'spotr' (Estimate Spatial Population Indices from Ecological AbundanceData) Compute relative or absolute population trends across space and time using predictions from models fitted to ecological population abundance data, as described in Knape (2025) . The package supports models fitted by 'mgcv' or 'brms', and draws from posterior predictive distributions. Package: r-cran-spray Architecture: arm64 Version: 1.0-27-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 578 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 341036 MD5sum: 4fb4f53331abc64022f99d27fc7aa3b7 SHA1: 8714ed2476512a98aeecc282d62532eb5c930086 SHA256: d4841d873e40caec36d6965c09afb8f12d5425d63fe99d25aac6ec78c815fc2b SHA512: 9eea80ec89959046f01087566b872f19b60b9827464798749836cdd9ae56b2c553fe9e8475090956309e6dc486bb80c3f9cdca8301d3f7daffbc054fdce8fc3f 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: arm64 Version: 0.1.9-1.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), 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_arm64.deb Size: 151580 MD5sum: c66189538c28046fc658fd10edf87bec SHA1: 10cf2b546ca52811eae512cc2ef9aa8d713cd85a SHA256: f68f55db572a592ee3b39bc549e018f2009469575cec6a826411eb8f7f0c30c6 SHA512: 2376685a1885cbad49a72bb7afd5b33c52b9e466bc0abc6416f8c2190faec6d3210bdf718b453507844cc6a3d4e3cbc2debff00f1792c0870f64a3d219906bec 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: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 891 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 818800 MD5sum: 11ebc9558deca29341da5449130077f9 SHA1: 017b7933b013dd38d5803d3cd797fa4f8fc0d663 SHA256: 81baf1137c80fbc77ba5935c645d4d75e86d6bd62586cc34e805fd8e32196d60 SHA512: 0b5bb4ddbd59e37d5f911b7ff6bdaa63c67c7a7b12ecde54f8a109fa7f005cd6400e9390d3863bb67622bab053bb80730c6db859436b03e456b9ebba62489315 Homepage: https://cran.r-project.org/package=SPSP Description: CRAN Package 'SPSP' (Selection by Partitioning the Solution Paths) An implementation of the feature Selection procedure by Partitioning the entire Solution Paths (namely SPSP) to identify the relevant features rather than using a single tuning parameter. By utilizing the entire solution paths, this procedure can obtain better selection accuracy than the commonly used approach of selecting only one tuning parameter based on existing criteria, cross-validation (CV), generalized CV, AIC, BIC, and extended BIC (Liu, Y., & Wang, P. (2018) ). It is more stable and accurate (low false positive and false negative rates) than other variable selection approaches. In addition, it can be flexibly coupled with the solution paths of Lasso, adaptive Lasso, ridge regression, and other penalized estimators. Package: r-cran-spstack Architecture: arm64 Version: 1.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2007 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cvxr, r-cran-future, r-cran-future.apply, r-cran-ggplot2, r-cran-loo, r-cran-mba, r-cran-rstudioapi Suggests: r-cran-dplyr, r-cran-knitr, r-cran-patchwork, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-spstack_1.1.3-1.ca2604.1_arm64.deb Size: 1236920 MD5sum: 02843a5e9f2a32142bd090dd56696a3f SHA1: e2fdb300f0047c034563549f12f9bd78f281b257 SHA256: da370784790038ff7b961e07037e350a8c50e99948eaa2b83a658d3a21b9809a SHA512: 0f835220d8c9f97219c299f09a9361b7e7120632cb0c38eee99d7b1c0895cca4dae2b393c4b0846fdb55f395699c3118598abc1397fd904851eeadbfb07a3091 Homepage: https://cran.r-project.org/package=spStack Description: CRAN Package 'spStack' (Bayesian Geostatistics Using Predictive Stacking) Fits Bayesian hierarchical spatial and spatial-temporal process models for point-referenced Gaussian, Poisson, binomial, and binary data using stacking of predictive densities. It involves sampling from analytically available posterior distributions conditional upon candidate values of the spatial process parameters and, subsequently assimilate inference from these individual posterior distributions using Bayesian predictive stacking. Our algorithm is highly parallelizable and hence, much faster than traditional Markov chain Monte Carlo algorithms while delivering competitive predictive performance. See Zhang, Tang, and Banerjee (2025) , and, Pan, Zhang, Bradley, and Banerjee (2025) for details. Package: r-cran-spsurv Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4243 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-loo, r-cran-coda, r-cran-mass, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-devtools, r-cran-roxygen2, r-cran-testthat, r-cran-kmsurv Filename: pool/dists/resolute/main/r-cran-spsurv_1.0.0-1.ca2604.1_arm64.deb Size: 1536346 MD5sum: 4e9b557564fd1ab7b48dd1aa0ee5f78c SHA1: 27ab575cbafaadc6fec4bf52791498f2306eb7b4 SHA256: 93ff28a4850d84891275d048c6e252c5cdb65b70fbb38875ca3135aaab749d68 SHA512: c34537f2931e7e1547fc3b399248bdb4312b95d45594d0ea61a8fd332b64d384b31613a9be14a38bc51f9fd10f104374e5fb78b03707e3808359aed7dd88fb95 Homepage: https://cran.r-project.org/package=spsurv Description: CRAN Package 'spsurv' (Bernstein Polynomial Based Semiparametric Survival Analysis) A set of reliable routines to ease semiparametric survival regression modeling based on Bernstein polynomials. 'spsurv' includes proportional hazards, proportional odds and accelerated failure time frameworks for right-censored data. RV Panaro (2020) . 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Bakar et al., (2016). Bakar et al., (2015). Package: r-cran-spte2m Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1843 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_arm64.deb Size: 1544370 MD5sum: 8b393001f61e9f0a7cde24fe11959029 SHA1: 540dbe327c0d45ac3cbb2ca76b059526f8edb403 SHA256: 6c2e28d1791e15971a2c0b4829840ae73465ac0c8ad0bf102aede5ab817ea23f SHA512: 477cdaf0cbd262ecec4be2bf1c4383be28642695df5bb8fb2b2a5319c5bb5ef3de729232cf957128b8534b7b63b1b4137949310198a1919de72281f39f6fade0 Homepage: https://cran.r-project.org/package=SpTe2M Description: CRAN Package 'SpTe2M' (Nonparametric Modeling and Monitoring of Spatio-Temporal Data) Spatio-temporal data have become increasingly popular in many research fields. Such data often have complex structures that are difficult to describe and estimate. This package provides reliable tools for modeling complicated spatio-temporal data. It also includes tools of online process monitoring to detect possible change-points in a spatio-temporal process over time. More specifically, the package implements the spatio-temporal mean estimation procedure described in Yang and Qiu (2018) , the spatio-temporal covariance estimation procedure discussed in Yang and Qiu (2019) , the three-step method for the joint estimation of spatio-temporal mean and covariance functions suggested by Yang and Qiu (2022) , the spatio-temporal disease surveillance method discussed in Qiu and Yang (2021) that can accommodate the covariate effect, the spatial-LASSO-based process monitoring method proposed by Qiu and Yang (2023) , and the online spatio-temporal disease surveillance method described in Yang and Qiu (2020) . Package: r-cran-sptimer Architecture: arm64 Version: 3.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 835 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-coda, r-cran-sp, r-cran-spacetime, r-cran-extradistr Filename: pool/dists/resolute/main/r-cran-sptimer_3.3.4-1.ca2604.1_arm64.deb Size: 677898 MD5sum: cebfa5f4d10d4115e63bdad7b7f21da7 SHA1: 51aab7d0a689910ed835255285496e290e77c8c2 SHA256: d6293b47e7e31c0cad7bb0aa5255683a0ffd354d882d4aa48d110de6856335de SHA512: 325bbddfcca863655f49dfe4f5170af9b61875dbb97837ecb503a37fca73abffe7e055b5b7d4b49e5450d10efb3bebfb07102490c2b4a2e705a2822be53f9b60 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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It acts as a wrapper around the 'sql_format' Rust crate. The package allows you to format 'SQL' code with customizable options, including indentation, case formatting, and more, ensuring your 'SQL' queries are clean, readable, and consistent. 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It relies on pre-existing quaternion data structure provided by the 'Eigen' 'C++' library. Package: r-cran-srm Architecture: arm64 Version: 0.4-26-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 663 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 397736 MD5sum: 4b0bf2c06d315bda77222b24a03d7869 SHA1: 0780156dda04e5b4a5750f4dd4cf05f10b2e1559 SHA256: 3a52d9bff5403f22f07a7295a8a84011a7c1ca8f691741b8087bdb52b5eb5eb5 SHA512: b8e6265fad70ea7ed2714b6fc323d1458225cd008d8ee1c034a6358de6a825c92a08ced83aa29068bdb224fdf055aa9c70ce1a73799b970b4d7d090a07e237f8 Homepage: https://cran.r-project.org/package=srm Description: CRAN Package 'srm' (Structural Equation Modeling for the Social Relations Model) Provides functionality for structural equation modeling for the social relations model (Kenny & La Voie, 1984; ; Warner, Kenny, & Soto, 1979, ). Maximum likelihood estimation (Gill & Swartz, 2001, ; Nestler, 2018, ) and least squares estimation is supported (Bond & Malloy, 2018, ). Package: r-cran-ssdtools Architecture: arm64 Version: 2.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2722 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_arm64.deb Size: 1766628 MD5sum: 133d4b4f68af9ce8a55d288b7ac2bf26 SHA1: 49f0093ad492a5335164f529eac03fbb7d5072ee SHA256: 137b53b60beac0cafd490ad13ba0be01383dfe183bd0c49d32f05f45a795ffec SHA512: 8f8fef0956515af2fa0afd62b3681ed7143c5759a20766b9023a845af3feb97cd2949a240e04df55a012e18a2dacd16fef1881b9a63865e25dcf7adea22b626f Homepage: https://cran.r-project.org/package=ssdtools Description: CRAN Package 'ssdtools' (Species Sensitivity Distributions) Species sensitivity distributions are cumulative probability distributions which are fitted to toxicity concentrations for different species as described by Posthuma et al.(2001) . The ssdtools package uses Maximum Likelihood to fit distributions such as the gamma, log-logistic, log-normal and log-normal log-normal mixture. Multiple distributions can be averaged using Akaike Information Criteria. Confidence intervals on hazard concentrations and proportions are produced by bootstrapping. Package: r-cran-ssgl Architecture: arm64 Version: 2.0-1.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_arm64.deb Size: 68742 MD5sum: 0187c52cbd31249b2c5e6010b3142db8 SHA1: 12588dd3f03df46b200583f84d6630e97a69ce08 SHA256: d858810fc3e8425ea0176acbd4fc9b45a5bd76d3d42f07460fd3f21b5532627b SHA512: 5d0e6a8bb98fcff1a6c0a561e58ddc629e7dffabb89dc960b2d099521842191011d2131e30d56c5295e7a8f05fdac9a076bf81a0b6b5c1b17f8f60f887946824 Homepage: https://cran.r-project.org/package=SSGL Description: CRAN Package 'SSGL' (Spike-and-Slab Group Lasso for Group-Regularized GeneralizedLinear Models) Fits group-regularized generalized linear models (GLMs) using the spike-and-slab group lasso (SSGL) prior of Bai et al. (2022) and extended to GLMs by Bai (2023) . This package supports fitting the SSGL model for the following GLMs with group sparsity: Gaussian linear regression, binary logistic regression, and Poisson regression. Package: r-cran-ssgraph Architecture: arm64 Version: 1.16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 417 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-bdgraph Suggests: r-cran-skimr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ssgraph_1.16-1.ca2604.1_arm64.deb Size: 246192 MD5sum: a3812d6193952133cce48615bba192a9 SHA1: cab26da532c15a2958bac0c1f67e6826ba12a990 SHA256: 7b233dd26ca0100ce7c29ae9e874ec5f77b095f4197a5718936e458cf21d3e3a SHA512: 30a60e5e119c29fb0c9dcbc09c9f65c7889de03492f14915c7aa7096e28bac06abbefe05ddc85e7638d3b2273f92f31a24da2783bd345b00ec630ef001a5ea79 Homepage: https://cran.r-project.org/package=ssgraph Description: CRAN Package 'ssgraph' (Bayesian Graph Structure Learning using Spike-and-Slab Priors) Bayesian estimation for undirected graphical models using spike-and-slab priors. 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Package: r-cran-ssh Architecture: arm64 Version: 0.9.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1058 Depends: libc6 (>= 2.33), libssh-4 (>= 0.8.0), r-base-core (>= 4.5.0), r-api-4.0, r-cran-credentials, r-cran-askpass Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-sys, r-cran-testthat, r-cran-mongolite Filename: pool/dists/resolute/main/r-cran-ssh_0.9.4-1.ca2604.1_arm64.deb Size: 320082 MD5sum: 9fac6170893d140893710dbb4df5de82 SHA1: 8f734898e82e4557bf9bfe43e5f3bd209d75a4b7 SHA256: 2838c0ade6875e383003da2fad58174e2768e18eac85079eea5b0bad1a3e5ed2 SHA512: e2e784991de5bea377a88eed9918020560d4c947e24b9dbfd5bfeaf1f1160c07ef4fdfc33e5aef2c5e7d6ca45e3b7186a89dff3acb55593dea95388b549365f6 Homepage: https://cran.r-project.org/package=ssh Description: CRAN Package 'ssh' (Secure Shell (SSH) Client for R) Connect to a remote server over SSH to transfer files via SCP, setup a secure tunnel, or run a command or script on the host while streaming stdout and stderr directly to the client. 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Information consistency-based methods provide a rigorous approach to quantify SSH and evaluate its role in spatial processes, grounded in principles of geographical stratification and information theory (Bai, H. et al. (2023) ; Wang, J. et al. (2024) ). Package: r-cran-sshist Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 164946 MD5sum: c65588da3e7ea5e10399999815f75152 SHA1: 11b2a8274168a643c713a3a1393a1e39966d62bd SHA256: 8edbb94f5dcebe9cf617e3164d5c7c4fe0471437049f5b8d8857a55cfc4ddf9d SHA512: 18eb7fd07b94704cd39741a94aac67d4af387200c9506f3d52b5243f6ac52c60c6f635218abc20aaea083e0e498cafe57755e37b8b391352b8e15b446a3a7c44 Homepage: https://cran.r-project.org/package=sshist Description: CRAN Package 'sshist' (Optimal Histogram Binning Using Shimazaki-Shinomoto Method) Implements the Shimazaki-Shinomoto method for optimizing the bin width of a histogram. This method minimizes the mean integrated squared error (MISE) and features a 'C++' backend for high performance and shift-averaging to remove edge-position bias. Ideally suits for time-dependent rate estimation and identifying intrinsic data structures. Supports both 1D and 2D data distributions. For more details see Shimazaki and Shinomoto (2007) "A Method for Selecting the Bin Size of a Time Histogram" . Package: r-cran-sslasso Architecture: arm64 Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 125 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-sslasso_1.2.3-1.ca2604.1_arm64.deb Size: 33540 MD5sum: 29317411e704b946bedd0d97ebdaef6e SHA1: 8026b46fc038f54900708774a3c4bf3b7b6e489d SHA256: 77224f8aa55566cddf5083ce929255b8c744065b1fb2ebcd25efc5cd38a18ecf SHA512: 970ade4b16389ea1af2e5b7eed4d7d055bdb74b011a6e894cf11564069e96ac07c1f6038b3185a1649ccb5ab1b701778c1a6f6a05d3e4947d3204847cd9c8aff 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: arm64 Version: 1.1.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6101 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_arm64.deb Size: 1364918 MD5sum: 10023288328f2cccdf80c3a310a0702b SHA1: 0c2e91bdf51265e674443c423d3b21d2eb2ae2c6 SHA256: df220862108c4657041c508304578006a16a14681d4da1b0672b6829645eece4 SHA512: c8433fdebef1652eaaad1dd9ba31bb88886db4ebd8b317cf5760755c3ee7f6488e1fd42dc70e626c0396e62fdf9419b0ac819fedc7b118b9265df501ec4c79ec Homepage: https://cran.r-project.org/package=ssMousetrack Description: CRAN Package 'ssMousetrack' (Bayesian State-Space Modeling of Mouse-Tracking Experiments viaStan) Estimates previously compiled state-space modeling for mouse-tracking experiments using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Package: r-cran-ssmrcd Architecture: arm64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1806 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1306500 MD5sum: 70d3a7e5b3a41ba06c85e7e9a499f26d SHA1: 79a0a1d4f0041e848cfa45e761b2442bb796967a SHA256: 5572c3c3666979d6f3ef207b1bd2fd37085a2227c371578b0ffc08cbdc563ad7 SHA512: c14e56e9ed06332705bd4c8024a21c6634c81cf7091f32544d5a78df212e7b4d863c5ea3d60773a79a518bad29ac8f31a0707a42cc2c879f83d984390de1e498 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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Package: r-cran-ssn2 Architecture: arm64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2918 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-sf, r-cran-matrix, r-cran-generics, r-cran-tibble, r-cran-spmodel, r-cran-rsqlite, r-cran-withr, r-cran-doparallel, r-cran-filematrix, r-cran-foreach, r-cran-itertools, r-cran-iterators Suggests: r-cran-rmarkdown, r-cran-knitr, r-cran-testthat, r-cran-ggplot2, r-cran-sp, r-cran-statmod, r-cran-proc, r-cran-emmeans, r-cran-estimability Filename: pool/dists/resolute/main/r-cran-ssn2_0.4.0-1.ca2604.1_arm64.deb Size: 1718610 MD5sum: 0f3868bc447aa88f934008be6f3fbe23 SHA1: 2e29833c18f41165b948ba236b56fc8c657124a0 SHA256: 920fb4b78e8cbfae3c18d1224a4315e83e41b94c51af3c69cf74926cfe041940 SHA512: 6ac55e6d898f38d3ba1bc5e7880c2c1dc371239b363e7fb191ab608d329ff352c27ad337787cb209807ee10ec8639459886163576ca366e2a0491af8fe9a63c0 Homepage: https://cran.r-project.org/package=SSN2 Description: CRAN Package 'SSN2' (Spatial Modeling on Stream Networks) Spatial statistical modeling and prediction for data on stream networks, including models based on in-stream distance (Ver Hoef, J.M. and Peterson, E.E., (2010) .) 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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: arm64 Version: 1.1.0-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2090 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rds, r-cran-kernsmooth, r-cran-scam, r-cran-coda Suggests: r-cran-testthat, r-cran-densestbayes Filename: pool/dists/resolute/main/r-cran-sspse_1.1.0-6-1.ca2604.1_arm64.deb Size: 1729068 MD5sum: 7634162b32b2c8062f17bf2872919f3d SHA1: 989d4a0c71d2a41101c4bb1608eb849ef935ff31 SHA256: 4aa1344ff0149b0ca1baba0fdba6afbd0b65d366900e8eaba678966c5813c2c9 SHA512: a70ef3230bbfb187febde2856b088a23d52a8609c76be9aa3a072088d124723ca488dc44db8dcae5b9040caf6928b812c8071f7529a417a5e77d1673bbe0a57a 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: arm64 Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2491 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_arm64.deb Size: 2142498 MD5sum: 924723f0f6b8ff06eba0a642c46fae48 SHA1: 563349a2d3c93a062f92de89c189410abb06844b SHA256: ae3b76751329ba50f82f532298df6252c0c30895ed3aaa3e1e3993fa28e5237a SHA512: ffc2258a68a06c7b732ba24ac86a9f12f2a8b984d8bfe827ddeda813503acac9bd971973af769dace9d4e3d7912b8cf025ac5b9c95179fa2ff4779dcceb755e4 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. 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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-stochblock Architecture: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 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_arm64.deb Size: 168186 MD5sum: 4dc7762ae7efdb18e18eb7ed79b816db SHA1: 6c18f6e3a61281c8acc46ad41a1a2b83e5d49a40 SHA256: 13975d5c923b38f07f7f701b04340032b08f01a0afcbe97c8fa3a7e7b19b73c3 SHA512: 8a03a4d6c4819d57b98b81f4591d8527d9350a64aea87d616ff45ed5e7435a72ab1200ae3ec770b86afbaa40930773c8dc769b7f65c6b83de9f3e40e1322c3cb Homepage: https://cran.r-project.org/package=StochBlock Description: CRAN Package 'StochBlock' (Stochastic Blockmodeling of One-Mode and Linked Networks) Stochastic blockmodeling of one-mode and linked networks as presented in Škulj and Žiberna (2022) . The optimization is done via CEM (Classification Expectation Maximization) algorithm that can be initialized by random partitions or the results of k-means algorithm. The development of this package is financially supported by the Slovenian Research Agency () within the research programs P5-0168 and the research projects J7-8279 (Blockmodeling multilevel and temporal networks) and J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks). Package: r-cran-stochcorr Architecture: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 339 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 160466 MD5sum: 8f5021ac71051c6ad4b955d5c12f9a55 SHA1: 1b2f9eed643926cc9d136a16b09050f44e053819 SHA256: 385472068c9c7a872be51a795f9b3d681cc309e7f8c62179d3f39c8a2280d224 SHA512: 08e95b7e34df884f211ba480071d97ec58b005cd8cdeefd74175595893a9607e214a602f50604707c7adbb43ce1444b903112ef96cff34696b19349934dcbb40 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: arm64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2586 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_arm64.deb Size: 1419528 MD5sum: e39c8f5ab63b77729418c670c1979e2d SHA1: bc46cf83c3e106bbba3722255f657d7dc063d104 SHA256: 898b6d28fc964ee7e3d082a3679db83ddb82827e702b9faab9454d9fef1904d4 SHA512: fb7c014d1391a76b5707aba3e8f85548e2638d986415f5bb01882cb17f7f262f890e45eaf59f1d459a6db7618ba12a1688cb576ebac24e6551a972fad2851fce 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. 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Package: r-cran-stochvol Architecture: arm64 Version: 3.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3145 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 2275336 MD5sum: 1d98a4664cbbca5fa5e50e88918a8189 SHA1: 899ff83f56e30b188fc7e0cf51c0c98c7b680e88 SHA256: ed74065a2758bd007114a89ff97451871bb0741c3a77941efb84535b55e98b18 SHA512: bc22a478c575dd668ad6fd186167074d97dc900e771563d03dea7646be142b292d49ad58c99c4eef07e2a5fe5428f9888e1248826a6ddb7503b71f61e632ce91 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. 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Package: r-cran-stormr Architecture: arm64 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_arm64.deb Size: 1313888 MD5sum: 446a4618a18e49b22bf9f788a3472253 SHA1: 76f8159e031a3434795fef6fce431a65dc8a2306 SHA256: 381f9d148ee641818260bc054a11ca97691b6dec3b7cf7ccbd5c8b37c81a5909 SHA512: 17d11a19153f3f71fee698c8245f1fde088f83affc86c22486101b1a776b5d5884681a505fcb6048a0712d1b225522154e9ef9572492828170c0398c26d304f1 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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The package is described in: Heath, M.R., Speirs, D.C., Thurlbeck, I. and Wilson, R.J. (2020) StrathE2E2: An R package for modelling the dynamics of marine food webs and fisheries. 8pp. Package: r-cran-stratification Architecture: arm64 Version: 2.2-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 750 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-stratification_2.2-7-1.ca2604.1_arm64.deb Size: 625192 MD5sum: c71042a6d6776d47f5edcb6036250d83 SHA1: 83237faf18a77fa38bab56592ab21881289d4a7d SHA256: 9c0a3e766dd33d8c069c415d3cbef2b5e87e1d2bd122d3e31c70e216beef509d SHA512: f06d96cf0607c5cc2ebe8562bc17723e6f1b7b84e193ae275a07e6f6a6c360cc7f1222dbbafa22ab57ceb84140da2a42faf2feb64f814dbe76289681fc7ec228 Homepage: https://cran.r-project.org/package=stratification Description: CRAN Package 'stratification' (Univariate Stratification of Survey Populations) Univariate stratification of survey populations with a generalization of the Lavallee-Hidiroglou method of stratum construction. The generalized method takes into account a discrepancy between the stratification variable and the survey variable. The determination of the optimal boundaries also incorporate, if desired, an anticipated non-response, a take-all stratum for large units, a take-none stratum for small units, and a certainty stratum to ensure that some specific units are in the sample. The well known cumulative root frequency rule of Dalenius and Hodges and the geometric rule of Gunning and Horgan are also implemented. Package: r-cran-stratifiedsampling Architecture: arm64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 686 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 324774 MD5sum: e6cc1e58eb124047879be66b388b31b5 SHA1: 0bfd4f27e30d797094d69be0d0c92dcf5efb944d SHA256: 217a106008ddfd7844221d8045b10ecbad74ea2d94db162b49ddd950149549fc SHA512: a030f5503dfbba4fb0d514e1ca3bec9e9b44bb0f1d137c7d8680477bc24306909067d75cbdfddcdb2c9e43fd6aae942efac4ff3533523b0e2689b9696281555a 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: arm64 Version: 0.0.92-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1000 Depends: libc6 (>= 2.38), libcurl4t64 (>= 7.16.2), libgcc-s1 (>= 3.0), libstdc++6 (>= 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_arm64.deb Size: 814098 MD5sum: 36b9ad85d413573dc0d864b2e7565fb8 SHA1: 0d48ee3e467e64b82573c5aa1c1a51a1cd2cfc0e SHA256: bd816e234107476457ceea3a8ea974c1bd9f0d74cf5d35647be1784f8f7ab69b SHA512: e91dce4da7e74fa01042d1e0f468e770dfd7d07c38c53df6f0c81fd6dbbfa701567d3cbe77c0095238d13182be991cd3cc6a022ef0479413ad52023d8f929bdf 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: arm64 Version: 2.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3879 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_arm64.deb Size: 2826512 MD5sum: 37e3056cb7a7dbea78f332c8d7e6a3a8 SHA1: f1ce428ac1121566168614dcb7eeac82a6fa6f03 SHA256: 33fb277598219f09119bd4e619db549cd490dd9cfc409f93712d6ad65f0a2fd9 SHA512: 0f38e32715c393b3f758c645748fad7b804de055c6a173c06107df71e576aee116d56e4ca238d9ff1d10c27dedbf3d4d4504bb8276277ffc86cb6d933c58c726 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: arm64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 483 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 301264 MD5sum: a169a6e9e5dcae7154571bae4cf180ae SHA1: 78504360c50b749d278508c3a2550af86f1804ba SHA256: d2384ad950be08b7e3e0be6ee82bed3ab93daead02c6e9cd8a0ae9880fa86c18 SHA512: 4f32b59bc735c9761a80dfcaf57db73ca2549e9c7982e6d0a39049d79460ff43e8b8dfdb3538eddd78feab615fac0c0e9682a42c9514e629f50118bc8ba5c0c1 Homepage: https://cran.r-project.org/package=streambugs Description: CRAN Package 'streambugs' (Parametric Ordinary Differential Equations Model of Growth,Death, and Respiration of Macroinvertebrate and Algae Taxa) Numerically solve and plot solutions of a parametric ordinary differential equations model of growth, death, and respiration of macroinvertebrate and algae taxa dependent on pre-defined environmental factors. The model (version 1.0) is introduced in Schuwirth, N. and Reichert, P., (2013) . This package includes model extensions and the core functions introduced and used in Schuwirth, N. et al. (2016) , Kattwinkel, M. et al. (2016) , Mondy, C. P., and Schuwirth, N. (2017) , and Paillex, A. et al. (2017) . Package: r-cran-strex Architecture: arm64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 538 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 256952 MD5sum: e99eeb3518ba25b5337fe800e6f6518d SHA1: ba78e08407b8803341965403b5c29f611d118129 SHA256: 14dcf55a0040ee228ff471dfb7feb12c722ea262e9ca81670c7136aee2267bc1 SHA512: 420cc9a22979fb6ef2a81b1b4a269f8ab23139cb229bb9593df629e457df26b67f7ddb62c1ffaa405b7bc1d2fa9558c6461a78abea2735df8ddd5cb600cb6b18 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: arm64 Version: 0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 5512 Depends: libc6 (>= 2.39), libfontconfig1 (>= 2.12.6), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tibble, r-cran-cli Suggests: r-cran-testthat, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-string2path_0.3.1-1.ca2604.1_arm64.deb Size: 1548646 MD5sum: 4dcc66b086551a626c066d807ab2b761 SHA1: c96efeaa986e7090115fb27e4ec319dd8270de94 SHA256: f28c5a08ad23febcc3118120429a9884a1dcdefba12283f01e0201cc7caf7054 SHA512: 91f2f2ce4106282ff668449b61b3481d7fc8f6eabd9407498794daf518762d31ae03ab3dc7b138161f5bea5219601509333352083cd6aec9d06023a4633dd29d Homepage: https://cran.r-project.org/package=string2path Description: CRAN Package 'string2path' (Rendering Font into 'data.frame') Extract glyph information from font data, and translate the outline curves to flattened paths or tessellated polygons. The converted data is returned as a 'data.frame' in easy-to-plot format. Package: r-cran-stringdist Architecture: arm64 Version: 0.9.17-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 814 Depends: libc6 (>= 2.17), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-stringdist_0.9.17-1.ca2604.1_arm64.deb Size: 582000 MD5sum: 426d33ec3ce3c8761806ca728ee41894 SHA1: a4bd279934d3904a39dd1ac3fab78d88c8144da6 SHA256: 863b5dbcf30b55f9751659b7268a77c92dd54a7d4409062eafde4c0ffc8ec6d0 SHA512: 911c01521668fd3a45ec61eb9c2352c61e26ab5c50eadcf68a093e2f32d9eb3a26faa965720b522dfbe075f8939bd4ad5b92faaf6ef493ddc23f0b615b36faad 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: arm64 Version: 0.19.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 831 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_arm64.deb Size: 374094 MD5sum: aa8f8989ea3e55416deea40801d33e6d SHA1: 9703bb7cc8a94e78d02291acba65c9f631013536 SHA256: 1a35717383b0f82e74587d69a5fb0f471ba717016cc43ebcfda6139246adb78b SHA512: 786bff0d9d9e6fd84aad8b8922059e64f3ca57d171ebb9a6e94ae5e9cde7278a349ffdb9d78c2251352758c4d0683f4be1cae91623c24bc0de96274f33e318c5 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: arm64 Version: 1.8.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1478 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_arm64.deb Size: 901486 MD5sum: 53c57d59bb9d63f9588d8b5ac9c5b17e SHA1: 3ceda60d80c9841c24bf37cf5eec5ade87fd35d6 SHA256: 47f9773629c7d4613438f6db36797ba5fabe2a0004dded58437dd2fd06d45bc5 SHA512: 62c0cb60b21315e89c398c045d46f655f68e915bbd876d3085052a805664fdc650c594bf96a78a1a67b589bb71a109625a9092c939722018d644176223c4fb02 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: arm64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8381 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_arm64.deb Size: 1528634 MD5sum: 2f3ef14d74db79b7fa6a0804e3920442 SHA1: 0054e444a9239744873b2ea58931f90850d27ea2 SHA256: 097d13efc2fcc32de529a3f995069d02fad33cf2f66d4c6a74678c6f8ae38cfc SHA512: 7fb71f76cfdde7bd52dd93d56ef31f8db72f5745307a1b6f1cc949a8e84928961c94a5340da37d8b84cf76a3877ac64c5f0c68bd7913730796b3c253ee1febf2 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: arm64 Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 660 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 249852 MD5sum: 888d2a311675b09d0cf213426c9b787c SHA1: 1cee082a0eaff45a8f17c305cb08112f1d85f64f SHA256: 205a5725a7d824c2b30c1803a3c5b8cc24c20c989ccf73066742c59567260c0d SHA512: 63c4be3ffa939021ba69c5eb9dc5deb710042d58b960542a30a585e219d9308e78e8a3d28ae9ec5f47330ba572703759b91c6a0a5cad276d6679efcbad78463f 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: arm64 Version: 1.5-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1104 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_arm64.deb Size: 943854 MD5sum: 98453593360a55497820df2273c738bc SHA1: 5df1eda419ed56d7228ef648faaf5f223270e857 SHA256: b53927d220d9dd38154149fddee39964d2cc7b889312ee80bce8ac85ad1d6c90 SHA512: 974ba7a9f1385360851b1086cd701163daa64e7cf706d94048a8c6eac981c2e365dd3583d29f5381105227a0ef60f66273983d13ab96c2ac0cd8d566cffc4bbf 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: arm64 Version: 1.5-4-1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1417 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_arm64.deb Size: 1105572 MD5sum: 17ef9ccde07590de2f716b48e4c2e198 SHA1: 23f92e6bb1f73be09fcdc5f11bddaa117f109359 SHA256: 478a1e2f7d5260cf0f36b87b3650b2d817ce1adb4569c5e9ff6ee719bc2960e3 SHA512: 98e097d3d6ad189ad4b0106a26996d771e7b1638eb4befa9b12ebf73a7062218c0e3cf9e0339cd03113aaff7ab1c17ec821b756d95c973c13c613b06d9c5cbd3 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: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 24172 MD5sum: ce8f7e3832ba9036adffdb97faebec6d SHA1: 8c33ce0901dc1c043ae87052201ea22c000d77cc SHA256: 9850554b57468b48e600a2fa33672703e743bdaff321c7e48b5561ad0939eb9a SHA512: 3ef988beece681182fe797342bc1e2613e4821904302549ed60bfb1c32b94946a545d1a4caf8a28bbcb13a3f30b5c9009af694a6c01d06cc1baffbdd9cee91d6 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: arm64 Version: 1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 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_arm64.deb Size: 368738 MD5sum: 3469d209d95be60b69bfd0743922b049 SHA1: 483ba7461ebf7feb80ad8311721f97c44589c899 SHA256: 134c4958702c5dc390bab5a2280d1717c86b8bc9962583a299bb8284e98efe32 SHA512: 8a395e6061c1cc9597f60b5733d72378ef2bb5f592fafb045ab37e02ffe31b0af66f3a8b10c4eb16c8f90af3d624e1686ecedfa95a1b4af43517174650e158c3 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-subplex Architecture: arm64 Version: 1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 156 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tinytest Filename: pool/dists/resolute/main/r-cran-subplex_1.9-1.ca2604.1_arm64.deb Size: 37928 MD5sum: b84fd50c756b7d46ce1f169470cc612e SHA1: 6cfca6fae11bff748cfe036182b55d0b207c4004 SHA256: d2d03f03ff805589b05b7846e6061bc22d77aa773874ee8e84375cc3d92109ff SHA512: c35730114784909fd3e6b16c16b5181283f6de2e8a576eda65c042e3a4364d35ed51d5f55b097123847aaf5c56938eb920d0053f7f31250e49170fc6c327da91 Homepage: https://cran.r-project.org/package=subplex Description: CRAN Package 'subplex' (Unconstrained Optimization using the Subplex Algorithm) The subplex algorithm for unconstrained optimization, developed by Tom Rowan. 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Package: r-cran-subtite Architecture: arm64 Version: 4.0.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 373 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), 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_arm64.deb Size: 165404 MD5sum: 2338cb61af1537aaef5746dbf52fbd3e SHA1: f78311aaa5582012c5e049b4546d79099697b807 SHA256: 6be117a610515af2c1984c163ff3b1f886d4c93004b42d5a34b3a27f0527c866 SHA512: a3db13b0f912e79ad9bf516794a9a980b084b5991a6f8b142a91b4613e91fd0d9657656a7997be2f30980ab0df699ada965b4aa4b23a9323b5f318bd0c2b1ba0 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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This package works on spatial data with one variable (e.g., continuous raster), many variables (e.g., RGB rasters), and spatial patterns (e.g., areas in categorical rasters). It is based on the SLIC algorithm (Achanta et al. (2012) ), and readapts it to work with arbitrary dissimilarity measures. Package: r-cran-superexacttest Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 335 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-superexacttest_1.1.0-1.ca2604.1_arm64.deb Size: 192058 MD5sum: 48e1d97bd74018203c355b210a36f486 SHA1: 0a2a13e56daccb90eee268dd014d1477aed36bb3 SHA256: 2a4a038dad1ea3e8c08117c5fca9e159268b251a0fac570c36d65c87bd60baaa SHA512: 14c70eab3560c82f22e0245fe183ac3600256294c95f2bab7364023b7b3c1c5afbb50c5ce970c9e1770f98baad1d8d2d990b5a3986c52c504408e1aae4086dd3 Homepage: https://cran.r-project.org/package=SuperExactTest Description: CRAN Package 'SuperExactTest' (Exact Test and Visualization of Multi-Set Intersections) Identification of sets of objects with shared features is a common operation in all disciplines. 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Package: r-cran-superpixelimagesegmentation Architecture: arm64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 676 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 364022 MD5sum: 1f7f01388cf60b08bb11f111b3e1a3c2 SHA1: d5c86d3ed4ab6fae4fe4f0e60c1ea1546125ead3 SHA256: f2db5c99fd126deeb320871b2c68bfa7e6290fd13b7125614ef77424006c941c SHA512: ee875ef0a4ffaaa7c9376970d5f631449d553434ab8a678b8a0d24fd285174bdb4db7e8900199eb49759a8d1e643e6fe44c7a89766c97ebb20f87626f2ea8650 Homepage: https://cran.r-project.org/package=SuperpixelImageSegmentation Description: CRAN Package 'SuperpixelImageSegmentation' (Superpixel Image Segmentation) Image Segmentation using Superpixels, Affinity Propagation and Kmeans Clustering. The R code is based primarily on the article "Image Segmentation using SLIC Superpixels and Affinity Propagation Clustering, Bao Zhou, International Journal of Science and Research (IJSR), 2013" . Package: r-cran-superranker Architecture: arm64 Version: 1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 270 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 100064 MD5sum: 69dd37469d6e39a733f83d9cf889da4d SHA1: 38518b00cbcca40701402f4a2f1e35e72aacb707 SHA256: 54c1a0be1c14a053720de87e17d0a36659d133939c6fa5cbebee54834c0394dc SHA512: 304f36de73d4415f88cd982a2cdd679b095d24650d2b4cdae0be123f477bd330059d8c00ebfe6a1964e056389d0047e5e21128264bf4dbbe16937b7ed5c7fe03 Homepage: https://cran.r-project.org/package=SuperRanker Description: CRAN Package 'SuperRanker' (Sequential Rank Agreement) Tools for analysing the agreement of two or more rankings of the same items. Examples are importance rankings of predictor variables and risk predictions of subjects. Benchmarks for agreement are computed based on random permutation and bootstrap. See Ekstrøm CT, Gerds TA, Jensen, AK (2018). "Sequential rank agreement methods for comparison of ranked lists." _Biostatistics_, *20*(4), 582-598 for more information. Package: r-cran-suppdists Architecture: arm64 Version: 1.1-9.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 400 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_arm64.deb Size: 218260 MD5sum: 29c5f2cfae6b2fb72bbaed5e26440b3e SHA1: a9fba7de197978cdab2506a05ed81514539b4688 SHA256: 5ab76af85ab97be40b2ad187bef61d109900c0a82e4a14ec9db9e50ea19056ac SHA512: 0f67e786e006aa14fce96dead383dc3cbbce7dedb28a1daca3729d55de1cf77488522e67a83791e0d2f9e5f023cfe1165a8188cab96bfd5838320e9ac1353ccb 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: arm64 Version: 0.1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 273 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 123042 MD5sum: d49ef50d90210f7e973788efe8a2340f SHA1: 3eef7609d107cac4586146b9c557d2e9a18ae664 SHA256: 6892f9f1f9d3b90a59bca69710b75f13bfed89065b382d9882678bad644218c4 SHA512: 6b35a65c73114892077c08ddbe19e3a47c785656d8ff2be143c886b3790ed62fb4de95caf29aaa9b2f37a7f7c16882ba96422e2573f40e1e2858eb95fecfc14f Homepage: https://cran.r-project.org/package=sureLDA Description: CRAN Package 'sureLDA' (A Novel Multi-Disease Automated Phenotyping Method for the EHR) A statistical learning method to simultaneously predict a range of target phenotypes using codified and natural language processing (NLP)-derived Electronic Health Record (EHR) data. See Ahuja et al (2020) JAMIA for details. Package: r-cran-surfrough Architecture: arm64 Version: 0.0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4664 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 4163354 MD5sum: 6014acff80ae40461feb23f592c2be32 SHA1: d8a75dfd90ddaa520cc20353507a34c6e498e7de SHA256: 3ba361761da8f58dab938829ddcf1a4ca75a1221d41d56cba6de3c98cd8650c0 SHA512: 962431afde7d1765893f609141a85f578571aae1c4da96feec8ccf2d8b71dc4e53170f56ffb4ca2ad52ce7bb7ccb0bfd6296f391eff01463b74435e948063a6e Homepage: https://cran.r-project.org/package=SurfRough Description: CRAN Package 'SurfRough' (Calculate Surface/Image Texture Indexes) Methods for the computation of surface/image texture indices using a geostatistical based approach (Trevisani et al. (2023) and Trevisani and Guth (2025) ). It provides various functions for the computation of surface texture indices (e.g., omnidirectional roughness and roughness anisotropy), including the ones based on the robust MAD estimator. The kernels included in the software permit also to calculate the surface/image texture indices directly from the input surface (i.e., without de-trending) using increments of order 2 and of order 4. It also provides the new radial roughness index (RRI), representing the improvement of the popular topographic roughness index (TRI). The framework can be easily extended with ad-hoc surface/image texture indices. Package: r-cran-surrogatebma Architecture: arm64 Version: 1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 289 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_arm64.deb Size: 108246 MD5sum: d8992314fafddf670e66694c622870b6 SHA1: 20e9a6c9ca6a66e6b57ec8d889c1826089298850 SHA256: 1f7f1a1a0e0a9854de08560510b3e76f05181449b52c8a2c7f2136dfb5e303d0 SHA512: 0ece6aaa9602f1d046c9a98fe6cc9cc849f773033ff8c564706baeb20f3de59fb61892cefe964aaf0f0d94abce22cf8a363fb5891102a0383bbc11952ceeb934 Homepage: https://cran.r-project.org/package=SurrogateBMA Description: CRAN Package 'SurrogateBMA' (Flexible Evaluation of Surrogate Markers with Bayesian ModelAveraging) Provides functions to estimate the proportion of treatment effect explained by the surrogate marker using a Bayesian Model Averaging approach. Duan and Parast (2023) . Package: r-cran-surrogateparadoxtest Architecture: arm64 Version: 2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 311 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 186882 MD5sum: 2bfa09a0728d4b57d090c071e3d20e49 SHA1: f0c5a1a76f3e787f924547f5638974d29487ad69 SHA256: 191349990446dccd1941cbb6cf13cb751bcf8759422ea866c8971f0c90c0e2d7 SHA512: 97becafb922da84d73ef393a604c3cb45b2ad8eecb4bc3ec774c4ecb9123d3546dd4db4c859dee697cbad8482504a8bec91155ea4b9c4abf77c10be49300cd75 Homepage: https://cran.r-project.org/package=SurrogateParadoxTest Description: CRAN Package 'SurrogateParadoxTest' (Empirical Testing of Surrogate Paradox Assumptions) Provides functions to nonparametrically assess assumptions sufficient to prevent the surrogate paradox through hypothesis tests of stochastic dominance, monotonicity of conditional mean functions, and non-negative residual treatment effect. Details are described in: Hsiao E, Tian L, and Parast L (2026). "Avoiding the surrogate paradox: an empirical framework for assessing assumptions." Journal of Nonparametric Statistics . There are also functions to assess resilience to the surrogate paradox via calculation of the resilience probability, the resilience bound, and the resilience set. Details will be available in Hsiao E, Tian L, and Parast L, "Resilience Measures for the Surrogate Paradox" (Under Review). Lastly, there is a function to assess resilience to the surrogate paradox in the met-analytic setting, described in Hsiao E and Parast L, "A Functional-Class Meta-Analytic Framework for Quantifying Surrogate Resilience" (Under Review). A tutorial for this package can be found at . Package: r-cran-surrogateregression Architecture: arm64 Version: 0.6.0.1-1.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), 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_arm64.deb Size: 531356 MD5sum: 9c764c6b029f4c9be7afc7dea9feee24 SHA1: b239a753002c40a5dbf4320138fca234f617bcbe SHA256: a4520c307a87d8ffd050066f4bf4c3b2850695a826416610ddbf1a708cb8b540 SHA512: ebeaba6b27c1e0907465d5bb9a8fba2813b1febd0649a0a77c2ae36e4600c9ca6144eb00b6340ef6ccbca865e3b43e8ec0d4404bc20c1d269f6f0e171b522b6e Homepage: https://cran.r-project.org/package=SurrogateRegression Description: CRAN Package 'SurrogateRegression' (Surrogate Outcome Regression Analysis) Performs estimation and inference on a partially missing target outcome (e.g. gene expression in an inaccessible tissue) while borrowing information from a correlated surrogate outcome (e.g. gene expression in an accessible tissue). Rather than regarding the surrogate outcome as a proxy for the target outcome, this package jointly models the target and surrogate outcomes within a bivariate regression framework. Unobserved values of either outcome are treated as missing data. In contrast to imputation-based inference, no assumptions are required regarding the relationship between the target and surrogate outcomes. Estimation in the presence of bilateral outcome missingness is performed via an expectation conditional maximization either algorithm. In the case of unilateral target missingness, estimation is performed using an accelerated least squares procedure. A flexible association test is provided for evaluating hypotheses about the target regression parameters. For additional details, see: McCaw ZR, Gaynor SM, Sun R, Lin X: "Leveraging a surrogate outcome to improve inference on a partially missing target outcome" . Package: r-cran-surtvep Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1203 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_arm64.deb Size: 795920 MD5sum: 4b2279965fdaf24e70bf8ebd7de08c84 SHA1: f4e6f658c1df37f533878f34f9682c7c423a9769 SHA256: 9ba4900d06b945e5a6102da9a06f0547b9345a10d688bcf76f863ee9c68d79e8 SHA512: 7655a8bfd0ef445f6f12f3c3ba3a087db7ad5236423cb1f2fb8520121ffa91209889a8b44dcb33bd02ebaa322878faafdfde04c9d47a1d643bc332f650ff1760 Homepage: https://cran.r-project.org/package=surtvep Description: CRAN Package 'surtvep' (Cox Non-Proportional Hazards Model with Time-VaryingCoefficients) Fit Cox non-proportional hazards models with time-varying coefficients. Both unpenalized procedures (Newton and proximal Newton) and penalized procedures (P-splines and smoothing splines) are included using B-spline basis functions for estimating time-varying coefficients. For penalized procedures, cross validations, mAIC, TIC or GIC are implemented to select tuning parameters. Utilities for carrying out post-estimation visualization, summarization, point-wise confidence interval and hypothesis testing are also provided. For more information, see Wu et al. (2022) and Luo et al. (2023) . Package: r-cran-survauc Architecture: arm64 Version: 1.4-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 365 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-rms Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-survauc_1.4-0-1.ca2604.1_arm64.deb Size: 190542 MD5sum: 61cf77503c51febdf9b8178c6cb7a8ea SHA1: 425240e2af779b273b85be69adf194429ac21d9f SHA256: 0df9eac01b2343aad48c8ad5dda460d581ee10a5178ca911c4cb957b0fb0ba8d SHA512: 7fe4a465604f4eb3482b4d00f77e2538a8624f71287a9e35981299490a2e29657c22e1b7e9f614fbedda1be29907f3d3514537df7fecbb72d344bf9dd65094be Homepage: https://cran.r-project.org/package=survAUC Description: CRAN Package 'survAUC' (Estimators of Prediction Accuracy for Time-to-Event Data) Provides a variety of functions to estimate time-dependent true/false positive rates and AUC curves from a set of censored survival data. Package: r-cran-survc1 Architecture: arm64 Version: 1.0-3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 147 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_arm64.deb Size: 53158 MD5sum: 15f109a2b08711c0c6969ad02cf397db SHA1: fa6a6522ee8f86a2168c07f593c8490b2f7f4955 SHA256: b62188131603f3124d178e4e14bb3cd5ade22e8acedee70aa137b0e8964c9d9d SHA512: 55b8fd69f4cca324b5464bc2d39f236e91b200e944169d00fb012f29c4ba34ac10be3b5529ab25fd4d04219aad5f0ebf41bd0bae52212f78f84a3daad508688e Homepage: https://cran.r-project.org/package=survC1 Description: CRAN Package 'survC1' (C-Statistics for Risk Prediction Models with Censored SurvivalData) Performs inference for C of risk prediction models with censored survival data, using the method proposed by Uno et al. (2011) . Inference for the difference in C between two competing prediction models is also implemented. Package: r-cran-survdistr Architecture: arm64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 271 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_arm64.deb Size: 149590 MD5sum: 80a52eab09fbebb6bb581e923ae0b387 SHA1: f4e82caf58c93a937209c18ed8123afea4729587 SHA256: e6402d9c2e99b22f433d30c676fd1ace47088c1b8d6ef81ab9a8519813564039 SHA512: 45868cf1dc39e8dbd01ff2c0e446ed90908eef33c203fd2e12612a8bf0635e2624d24e278a736c6a694a6de8faabcf2b220678d1f1b84b304c3f7e867ab34c20 Homepage: https://cran.r-project.org/package=survdistr Description: CRAN Package 'survdistr' (Survival Distribution Container with Flexible InterpolationMethods) Efficient containers for storing and managing prediction outputs from survival models, including Cox proportional hazards, random survival forests, and modern machine learning estimators. Provides fast C++ methods to evaluate survival probabilities, hazards, probability densities, and related quantities at arbitrary time points, with support for multiple interpolation methods via 'Rcpp'. Package: r-cran-surveil Architecture: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3148 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_arm64.deb Size: 1341038 MD5sum: ef015db47850c85656ea25486806005b SHA1: 39ddb807917e20f7e288373c2192ec1ca8bac34d SHA256: cbfbf19d27d3673f245c3f6fcf7103f3d92fa030b2c039a61ccb6f30b510af0c SHA512: 08f95428c43810fe0725dc3d5abe0e609db71e3053a1c8f98d11275978c61db88324fe3fa5d6c7a4ccb0dbf9fdcc321d56db504fae6ef61a2c798a991ad717ee Homepage: https://cran.r-project.org/package=surveil Description: CRAN Package 'surveil' (Time Series Models for Disease Surveillance) Fits time trend models for routine disease surveillance tasks and returns probability distributions for a variety of quantities of interest, including age-standardized rates, period and cumulative percent change, and measures of health inequality. The models are appropriate for count data such as disease incidence and mortality data, employing a Poisson or binomial likelihood and the first-difference (random-walk) prior for unknown risk. Optionally add a covariance matrix for multiple, correlated time series models. Inference is completed using Markov chain Monte Carlo via the Stan modeling language. References: Donegan, Hughes, and Lee (2022) ; Stan Development Team (2021) ; Theil (1972, ISBN:0-444-10378-3). Package: r-cran-surveillance Architecture: arm64 Version: 1.25.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6382 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_arm64.deb Size: 5468516 MD5sum: 9c63dce3c3040dd05ed1c7d425995ca1 SHA1: 9434097b04a20fe836edf64529e9ad63bd80a98e SHA256: 126ecaf52ba624ba529405583106042d032f239ea6f9b53b1fdee5a9ed312635 SHA512: c2d56d7476846c30eac1af00771820f06bd3865b15a57d578a68eef3671ce0ead7551a439cdcc29338ef1fc598d7b7bd70240cba09cb6bc95e974675a290d150 Homepage: https://cran.r-project.org/package=surveillance Description: CRAN Package 'surveillance' (Temporal and Spatio-Temporal Modeling and Monitoring of EpidemicPhenomena) Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Hoehle and Paul (2008) . A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) . For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. hhh4() estimates models for (multivariate) count time series following Paul and Held (2011) and Meyer and Held (2014) . twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Hoehle (2009) . twinstim() estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) . A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) . Package: r-cran-surveval Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 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_arm64.deb Size: 48256 MD5sum: 01257b2ee1ea2d26267583477b639ea9 SHA1: 195214fbcbcb8469192e4d85a512560d6e0d6b0b SHA256: 1559a9546d5debd7cc25a5d951a81968bdae4bf8380acec2a7f95b4af7c0ebac SHA512: 0a113a67ee5a7593bccb7244d37a21741fecc4f04ec6a26fb73c376497a049c1968232fcdb60fa3165c81e44a9844e15870390888c1be61cdb76be5955590bd5 Homepage: https://cran.r-project.org/package=SurvEval Description: CRAN Package 'SurvEval' (Methods for the Evaluation of Survival Models) Provides predictive accuracy tools to evaluate time-to-event survival models. This includes calculating the concordance probability estimate that incorporates the follow-up time for a particular study developed by Devlin, Gonen, Heller (2020). It also evaluates the concordance probability estimate for nested Cox proportional hazards models using a projection-based approach by Heller and Devlin (under review). Package: r-cran-survextrap Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6500 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_arm64.deb Size: 1963934 MD5sum: bfbe993b2b50ed8a2facc932739bbef3 SHA1: 448012ec2c2fa8c51663efb8e7a3c89e39094dec SHA256: a7529dbfd4c9413909b739656a2045370204aa274ef0c9d1e2b3f630797d940c SHA512: 3f923b662cce4bfe1aa424dfd48bd55db0cb0d58109497e8617c9e8467fd933f8ce15c719898fe5192d4700b08ac0dac9f13a41c221bfb79847914e1c713c0da Homepage: https://cran.r-project.org/package=survextrap Description: CRAN Package 'survextrap' (Bayesian Flexible Parametric Survival Modelling andExtrapolation) Survival analysis using a flexible Bayesian model for individual-level right-censored data, optionally combined with aggregate data on counts of survivors in different periods of time. An M-spline is used to describe the hazard function, with a prior on the coefficients that controls over-fitting. Proportional hazards or flexible non-proportional hazards models can be used to relate survival to predictors. Additive hazards (relative survival) models, waning treatment effects, and mixture cure models are also supported. Priors can be customised and calibrated to substantive beliefs. Posterior distributions are estimated using 'Stan', and outputs are arranged in a tidy format. See Jackson (2023) . Package: r-cran-survey Architecture: arm64 Version: 4.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4224 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 3442026 MD5sum: ab2206c361f48932c61917543df3524b SHA1: 07359738b8b4e488f37241c1d36baa32e06701d5 SHA256: 74f4417496157f707117c93f3428ee554ffaed976e09b42321d967ce6dda7898 SHA512: 9b20cdccd29ab5d8380df7ea88d78a5b5a991b4c7fe7d949ae019e7446a1d253f806ce07a0f2028317b3b52de9d379763d95f09c2216520b2c8da3fefa396ed6 Homepage: https://cran.r-project.org/package=survey Description: CRAN Package 'survey' (Analysis of Complex Survey Samples) Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase and multiphase subsampling designs. Graphics. PPS sampling without replacement. Small-area estimation. Dual-frame designs. Package: r-cran-surveybootstrap Architecture: arm64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 290 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 162560 MD5sum: 2bb4b54669496cecb3b7923924ad1860 SHA1: 31dcef42b98a17c7e200023b5e1bfccd2fd9860c SHA256: 7c50cdd210a27c59e063b87a22515ed53d7c6d532b9e91e1972e78544461419a SHA512: 7b5b27c733edfda4bfc5265fbd058b0b983bb567796e4ad59c74070e6ecb7260dc9dd485e4c0ca4655690593841d434ae496fd22d912e49c9e15f3d45ecd5eae Homepage: https://cran.r-project.org/package=surveybootstrap Description: CRAN Package 'surveybootstrap' (Bootstrap with Survey Data) Implements different kinds of bootstraps to estimate sampling variation from survey data with complex designs. Includes the rescaled bootstrap described in Rust and Rao (1996) and Rao and Wu (1988) . Package: r-cran-surveygraph Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 549 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-surveygraph_1.0.0-1.ca2604.1_arm64.deb Size: 437762 MD5sum: f098d9da7367cfb0a084b6aeeb327c0b SHA1: 201cf83d0a07b8e81918ae5e0c01b28e504b5067 SHA256: 5df8dbb5e159acc88eb55db15b2c7b60a4d1677b4b3679ce0afac7c142248041 SHA512: 0ab64342e5823391edff1e6e016755f058955d32ecea3e9005db750a9b512819d691bb61a7b0e59302a4aac0284fc5c00ff7e0252d263ad7bfe503b96556cbb0 Homepage: https://cran.r-project.org/package=surveygraph Description: CRAN Package 'surveygraph' (Network Representations of Attitudes) A tool for computing network representations of attitudes, extracted from tabular data such as sociological surveys. Development of surveygraph software and training materials was initially funded by the European Union under the ERC Proof-of-concept programme (ERC, Attitude-Maps-4-All, project number: 101069264). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. Package: r-cran-surveyplanning Architecture: arm64 Version: 4.0-1.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_arm64.deb Size: 110710 MD5sum: 8b4bb498316f3e155cf31d9785a7a398 SHA1: b751ef982b8d2724784110f4293a73fb914fcce8 SHA256: 5b1ab2dba87e6732effdeecdf76cdef203dd96861eec12a5fe66f05ba6cf5343 SHA512: e1891771bfcfda804cfa85164b71c941f518ef7da97743b58def9f10da605437b894d1ad5eb7107414933a307eea9a7682944d8073db9d0bd9ed45d46d67a1c1 Homepage: https://cran.r-project.org/package=surveyplanning Description: CRAN Package 'surveyplanning' (Survey Planning Tools) Tools for sample survey planning, including sample size calculation, estimation of expected precision for the estimates of totals, and calculation of optimal sample size allocation. Package: r-cran-surveysd Architecture: arm64 Version: 2.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 916 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_arm64.deb Size: 510428 MD5sum: 817f958f6d8b6c8193f8def6390962f7 SHA1: d71a86bf796b89cbc7c50c814c59a3554bdabc18 SHA256: e225c00cda249843d355a61bf8f20e5d195c324fc784d07fd237274859e89055 SHA512: 996beb615515909cd0459c2573345708635e3ac11b4f55aedefd91fc152cd12cc53a320fa0a567629da717c38ea7cdf38dd0c4235b1a5c1cd0713dcbae1fc4ee Homepage: https://cran.r-project.org/package=surveysd Description: CRAN Package 'surveysd' (Survey Standard Error Estimation for Cumulated Estimates andtheir Differences in Complex Panel Designs) Calculate point estimates and their standard errors in complex household surveys using bootstrap replicates. Bootstrapping considers survey design with a rotating panel. A comprehensive description of the methodology can be found under . Package: r-cran-surveyvoi Architecture: arm64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1093 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgmp10 (>= 2:6.3.0+dfsg), libmpfr6 (>= 3.1.3), libstdc++6 (>= 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_arm64.deb Size: 679258 MD5sum: b89518bba121f6318ec6fec2a2887ffe SHA1: 0bf92a8b4bb37179865e7f8aef72986da44f5008 SHA256: 4039f27def4e357e60a25ef44fce485efc368529f50f82f8899847ce3333e92a SHA512: 60a6bf4bdc920a64318369efc7b0a0ad083c051e4124f4e7de1ffaf500b0c3d0c3f6059f3a47001a181033031769806cf9d93be762146e644c5dc08f1eff8dcc 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: arm64 Version: 1.1-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 144 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_arm64.deb Size: 49272 MD5sum: c656982c8b45c7cad7f294f075ebdb6d SHA1: 173d0d3188b251d159299fae34b59c09c51ea50f SHA256: 610e4b6f1fb68a8bc98080c7da996f5df79994ceaa415220afc8e9ecda8c913e SHA512: 1cc0c8c1f1ce16fb51f08cfeaa3f3e05c0a3e1efc37172c7e06ae59f0cd49b86d0d7eefe3bf9e46beda0e1aba31de376a07048a72925b06cc8b8099de2618f93 Homepage: https://cran.r-project.org/package=survIDINRI Description: CRAN Package 'survIDINRI' (IDI and NRI for Comparing Competing Risk Prediction Models withCensored Survival Data) Performs inference for a class of measures to compare competing risk prediction models with censored survival data. The class includes the integrated discrimination improvement index (IDI) and category-less net reclassification index (NRI). Package: r-cran-survidm Architecture: arm64 Version: 1.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 447 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 360984 MD5sum: 550691aef384b53db3de5a21ccf1a614 SHA1: 40cae67e1ccbb88710cefbd25e0871ccdb8a19f6 SHA256: 73c67a041ace660aa9960dcb1a6bbb1c72fa0db6aca1b017e45271cbcab49f9e SHA512: 043ad280b130ed46386fb4d91f4f3d90c0c0f649cab08f936953b1dbf31b6e48bccfc85deb81c32f8c89c9fe3114d9c31b6e041f4737d8de21c7ff8f259323b8 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: arm64 Version: 3.8-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9579 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 8306524 MD5sum: 8c948043935f6271a9571ad65e874158 SHA1: c4b1a2e74d1a231ee2c67be7e514011d4174d9fd SHA256: 0d6f2b5a4480b318e0db5102f2971e47e7d46f442fa7c2109de98224d1c6cfa2 SHA512: 7c09693979ee357810e878a67cbe9d51d8ead29181c6ae960a4c71ce396a1cbea3065b5e9625d4d981dcff7556a9c6d580103f97faf88424efed895d74b32f29 Homepage: https://cran.r-project.org/package=survival Description: CRAN Package 'survival' (Survival Analysis) Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models. Package: r-cran-survivalclusteringtree Architecture: arm64 Version: 1.1.3-1.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), 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_arm64.deb Size: 271358 MD5sum: d8454e54fa7006e527289e536ee19a7a SHA1: c1d3ffa239634a5864237bcdd5292d788522dda8 SHA256: 694ba6834a7605f797828e244221b8fe490f6b5fa9936acab9b3be2f8d61bf9e SHA512: 93eb9aaa5806a6aab918d19fd493e89da8f7a431ebc4c7f908ed2f6abcad0f82bd1d73502fc86095dedceb62c2406ce99592df370a3d99e20cf47aa3daff420f Homepage: https://cran.r-project.org/package=SurvivalClusteringTree Description: CRAN Package 'SurvivalClusteringTree' (Clustering Analysis Using Survival Tree and Forest Algorithms) An outcome-guided algorithm is developed to identify clusters of samples with similar characteristics and survival rate. The algorithm first builds a random forest and then defines distances between samples based on the fitted random forest. Given the distances, we can apply hierarchical clustering algorithms to define clusters. Details about this method is described in . Package: r-cran-survivalmodels Architecture: arm64 Version: 0.1.191-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 182976 MD5sum: c158d92f50afbbbeb207c27766ca4526 SHA1: eadd7ffda4856db9634ed45f591841ecb2ee7524 SHA256: 613ff74fe141aeb1fe707d284b530b5b0681186291b4d922bb1e7bf4d5994647 SHA512: f58f64bad7d7efd3c3ee0366f6e352569d2384e902af68a67a8ed944038b208830332eb6281d024a7632ebce75bcc4143f7edf16bdda56a071455e7256a407b2 Homepage: https://cran.r-project.org/package=survivalmodels Description: CRAN Package 'survivalmodels' (Models for Survival Analysis) Implementations of classical and machine learning models for survival analysis, including deep neural networks via 'keras' and 'tensorflow'. Each model includes a separated fit and predict interface with consistent prediction types for predicting risk or survival probabilities. Models are either implemented from 'Python' via 'reticulate' , from code in GitHub packages, or novel implementations using 'Rcpp' . Neural networks are implemented from the 'Python' package 'pycox' . Package: r-cran-survivalrec Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 284 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 133594 MD5sum: 614c5136232b766dacf440ec36d54634 SHA1: 458ab08d71efc00d4d50aba402d4030bd96ca4b5 SHA256: 49141a40706845d434dd5ef27ead1d445c27558a848b23f6ae3280f7d563252d SHA512: 25d7b85776be746c4b1812d9385484baba3c15925e97bc0a93dab0af2c9bde37f995fadcedf400255cce92d8e27a18fbc6c414bd7e1cc1ec382bfbf2427fff8c Homepage: https://cran.r-project.org/package=survivalREC Description: CRAN Package 'survivalREC' (Nonparametric Estimation of the Distribution of Gap Times forRecurrent Events) Provides estimates for the bivariate and trivariate distribution functions and bivariate and trivariate survival functions for censored gap times. Two approaches, using existing methodologies, are considered: (i) the Lin's estimator, which is based on the extension the Kaplan-Meier estimator of the distribution function for the first event time and the Inverse Probability of Censoring Weights for the second time (Lin DY, Sun W, Ying Z (1999) and (ii) another estimator based on Kaplan-Meier weights (Una-Alvarez J, Meira-Machado L (2008) ). The proposed methods are the landmark estimators based on subsampling approach, and the estimator based on weighted cumulative hazard estimator. The package also provides nonparametric estimator conditional to a given continuous covariate. All these methods have been submitted to be published. Package: r-cran-survivalroc Architecture: arm64 Version: 1.0.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 138 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 40550 MD5sum: bedd16af439cd6b0de7c9104241f6eed SHA1: 677c9847c4002bf96124ef9742146fd49896ce58 SHA256: a370265c9dff15bc64c5f35666c1fceb404871b5320de185d605e2d80c833735 SHA512: 08043ac698c1eb574a162b4b5f6220605fe09df55d6c7db516197879c39ad781ac1d774829bedac2d05eeb9e512f4ff050809458c43ae38344e34f2297e6285e Homepage: https://cran.r-project.org/package=survivalROC Description: CRAN Package 'survivalROC' (Time-Dependent ROC Curve Estimation from Censored Survival Data) Compute time-dependent ROC curve from censored survival data using Kaplan-Meier (KM) or Nearest Neighbor Estimation (NNE) method of Heagerty, Lumley & Pepe (Biometrics, Vol 56 No 2, 2000, PP 337-344). Package: r-cran-survkl Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1957 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1674566 MD5sum: f3b36c7c07002d632cab5f1b077396d3 SHA1: 9e1e44b5af5d44c91629559775defc3fe4aafc5c SHA256: 0dd589f37470088250eee51cadba4a9a38e12c4c6c303efd1c355a36223690ad SHA512: 08711eee830f565658b3283639aac33f0c1e43b5526d56f5275a220fa3c5f026bcd8f03d28c356a463dd0c829f0344687f9d7bc0617ff4a32ef490f4cf91a60e Homepage: https://cran.r-project.org/package=survkl Description: CRAN Package 'survkl' (Estimate Survival Data with Data Integration) Provides flexible and efficient tools for integrating external risk scores into Cox proportional hazards models while accounting for population heterogeneity. Enables robust estimation, improved predictive accuracy, and user-friendly workflows for modern survival analysis. For more information, see Wang et al. (2023) . Package: r-cran-survpen Architecture: arm64 Version: 2.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2290 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_arm64.deb Size: 1016734 MD5sum: bd52af1810e666f3517473931839a9e5 SHA1: bdccaaf0127a474bd1be3cbb620b1965f72c691b SHA256: 4e9d7ffdafbd85936e71ff13ad50566bb1f2d1e1e994c3553431faf9086cdf6a SHA512: 92b3f6d75d8ecf7d35dd590215b4370367705ac9f7d982a83235dea23891bb11e637003937baa0e38f7d310bef69b7ad02f1ed1509c34af6816664e931c3f548 Homepage: https://cran.r-project.org/package=survPen Description: CRAN Package 'survPen' (Multidimensional Penalized Splines for (Excess) Hazard Models,Relative Mortality Ratio Models and Marginal Intensity Models) Fits (excess) hazard, relative mortality ratio or marginal intensity models with multidimensional penalized splines allowing for time-dependent effects, non-linear effects and interactions between several continuous covariates. In survival and net survival analysis, in addition to modelling the effect of time (via the baseline hazard), one has often to deal with several continuous covariates and model their functional forms, their time-dependent effects, and their interactions. Model specification becomes therefore a complex problem and penalized regression splines represent an appealing solution to that problem as splines offer the required flexibility while penalization limits overfitting issues. Current implementations of penalized survival models can be slow or unstable and sometimes lack some key features like taking into account expected mortality to provide net survival and excess hazard estimates. In contrast, survPen provides an automated, fast, and stable implementation (thanks to explicit calculation of the derivatives of the likelihood) and offers a unified framework for multidimensional penalized hazard and excess hazard models. Later versions (>2.0.0) include penalized models for relative mortality ratio, and marginal intensity in recurrent event setting. survPen may be of interest to those who 1) analyse any kind of time-to-event data: mortality, disease relapse, machinery breakdown, unemployment, etc 2) wish to describe the associated hazard and to understand which predictors impact its dynamics, 3) wish to model the relative mortality ratio between a cohort and a reference population, 4) wish to describe the marginal intensity for recurrent event data. See Fauvernier et al. (2019a) for an overview of the package and Fauvernier et al. (2019b) for the method. Package: r-cran-survpresmooth Architecture: arm64 Version: 1.1-12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 175 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-survpresmooth_1.1-12-1.ca2604.1_arm64.deb Size: 89370 MD5sum: 67ab00dbf1be8e8652ec4524862ffce3 SHA1: 6892c4dc253ba89100a2d44ae7864fac66acf943 SHA256: 28bb400d4e9c4a76f816c1fec80c71fb716c9ccbb90c2de0c35d25f9a9683889 SHA512: ec0a2dc08993b94a292d4eed255217d204b1f92d9423c1f2b49a629f1409de2d7ae26353d779604502664c6a7a230f6de0aada1002fc62c9991995c0d7f2cc73 Homepage: https://cran.r-project.org/package=survPresmooth Description: CRAN Package 'survPresmooth' (Presmoothed Estimation in Survival Analysis) Presmoothed estimators of survival, density, cumulative and non-cumulative hazard functions with right-censored survival data. For details, see Lopez-de-Ullibarri and Jacome (2013) . Package: r-cran-survsnp Architecture: arm64 Version: 0.26-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 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_arm64.deb Size: 181926 MD5sum: cfb26bcdad0528ff3c8fb7221a8344ea SHA1: 4bd703a288e6134f2a6cec4c0454665bdd89d5c5 SHA256: bf3fd3348690f089efc78167c2577c49d5cc5e2473554039516799aa5f54679b SHA512: 55ee3647289ec0abf80eb2825d5f1c439e5f6aa496a9776d73ab5b1e3b4713af959f715fd6c8be9cfa5556c151094db8b0d01a5de28c711ba7406e187b7f1d48 Homepage: https://cran.r-project.org/package=survSNP Description: CRAN Package 'survSNP' (Power Calculations for SNP Studies with Censored Outcomes) Conduct asymptotic and empirical power and sample size calculations for Single-Nucleotide Polymorphism (SNP) association studies with right censored time to event outcomes. Package: r-cran-survstan Architecture: arm64 Version: 0.0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2246 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 890716 MD5sum: 69999e55a695fc69b6add7ef3e658dc1 SHA1: 31dc070b2d6ab248b0ae8ea3c43d2bce282f78eb SHA256: 6c0ffae3735f91d4bc081c02d69d95bd2fa9295e16970de8598aefaafd42d787 SHA512: d4e53b77a08284516f63d69bf22a6c59d5f661117440c4e2bcfc3c06cfbf6eee2f3cf0db717f8b676a93eba167f904d6162a8dd42bd33755831126679ce03118 Homepage: https://cran.r-project.org/package=survstan Description: CRAN Package 'survstan' (Fitting Survival Regression Models via 'Stan') Parametric survival regression models under the maximum likelihood approach via 'Stan'. Implemented regression models include accelerated failure time models, proportional hazards models, proportional odds models, accelerated hazard models, Yang and Prentice models, and extended hazard models. Available baseline survival distributions include exponential, Weibull, log-normal, log-logistic, gamma, generalized gamma, rayleigh, Gompertz and fatigue (Birnbaum-Saunders) distributions. References: Lawless (2002) ; Bennett (1982) ; Chen and Wang(2000) ; Demarqui and Mayrink (2021) . 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(2021) . Based on an existing VAR model object (provided by e.g. VAR() from the 'vars' package), the structural impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) ), patterns of GARCH (Normadin, M., Phaneuf, L. (2004) ), independent component analysis (Matteson, D. S, Tsay, R. S., (2013) ), least dependent innovations (Herwartz, H., Ploedt, M., (2016) ), smooth transition in variances (Luetkepohl, H., Netsunajev, A. (2017) ) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) )). Package: r-cran-svd Architecture: arm64 Version: 0.5.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 325 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgfortran5 (>= 8), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-svd_0.5.8-1.ca2604.1_arm64.deb Size: 154168 MD5sum: 76e3f40fb917e970f3869c3c6f58c4d2 SHA1: cf769e81faeb32e612b776ef6f06e65db1597905 SHA256: e507c41eab56eae1756c51305c4fcef41132634e3bb2cfafa82d51b956e2b73e SHA512: 8748b4d7ded8ea973995c6480c24b4fafe9b40878be7dd7f96767ab4c975d45af23c89026c28b7c98d953036e6eb469a332dc5430f3fcf256fd607e72e422683 Homepage: https://cran.r-project.org/package=svd Description: CRAN Package 'svd' (Interfaces to Various State-of-Art SVD and Eigensolvers) R bindings to SVD and eigensolvers (PROPACK, nuTRLan). Package: r-cran-svdnf Architecture: arm64 Version: 0.1.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1114 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_arm64.deb Size: 975382 MD5sum: d5e3e96f79ffd61171e0f49b3a939614 SHA1: 8619cb61d93500185356156fae4149369223152b SHA256: 1ed682e7a001b8cab158bd3df682f280e5db6ac45cf5d3a6925b9b288b62fd3b SHA512: f8b2a4701989051f49df4e937cf4698f402d7a05d2a4eb627df97bbb4815bfd87b2e16dc8da0b26476b355516c05ec60a99041fe9da5491c4c81c7ce2886f3eb 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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(2022) . Package: r-cran-svycoxme Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 643 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survey, r-cran-coxme, r-cran-survival, r-cran-rcpp, r-cran-lme4, r-cran-matrix, r-cran-future, r-cran-parallelly Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-future.apply, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-svycoxme_1.0.0-1.ca2604.1_arm64.deb Size: 480190 MD5sum: 7f3a89434fed063be4a0783999361e53 SHA1: f5ae3a4f9c5c1b454d150d81e1be98df7dc0a4a2 SHA256: 1c7589b490a8448241a1b383caa5c9ba970aeff8fe5ee6599ea27dd846794f9d SHA512: f99118433c5c0cd6bf3ff15a29dd95546c028cfb755ae72110c16b549758fa1291ae060310383ed954cba820f3725bdba93b5a36a040a7d2ef252a5cfc782703 Homepage: https://cran.r-project.org/package=svycoxme Description: CRAN Package 'svycoxme' (Mixed-Effects Cox Models for Complex Samples) Mixed-effect proportional hazards models for multistage stratified, cluster-sampled, unequally weighted survey samples. Provides variance estimation by Taylor series linearisation or replicate weights. Package: r-cran-svytest Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1153 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-survey, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-mass, r-cran-rmarkdown, r-cran-sampling, r-cran-dplyr, r-cran-tidyr, r-cran-tibble, r-cran-future.apply, r-cran-broom, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-svytest_1.1.0-1.ca2604.1_arm64.deb Size: 973276 MD5sum: e91ed3b8aa71f9089df27e1d4482347f SHA1: 1b58e9c1ae0b594175cf350b1fe4bf3ff4ea2695 SHA256: b6b12192dad3bc2be07595e1477ba338f0ab6992ac37ad39ece9dcbe4b6c741f SHA512: 813ff22676b2c306cc906c2f6c81d7f8f1da908eb226895ce19c2675d4c2482c53ef27b8f7d4e0c312a9877e4328d1fffb091b85da0574c6afe8595404e07d2e Homepage: https://cran.r-project.org/package=svytest Description: CRAN Package 'svytest' (Survey Weight Diagnostic Tests) Provides diagnostic tests for assessing the informativeness of survey weights in regression models. Implements difference-in-coefficients tests (Hausman 1978 ; Pfeffermann 1993 ), weight-association tests (DuMouchel and Duncan 1983 ; Pfeffermann and Sverchkov 1999 ; Pfeffermann and Sverchkov 2003 ; Wu and Fuller 2005 ), estimating equations tests (Pfeffermann and Sverchkov 2003 ), and non-parametric permutation tests. Includes simulation utilities replicating Wang et al. (2023 ) and extensions. Package: r-cran-swaglm Architecture: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 794 Depends: libc6 (>= 2.17), 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-igraph, r-cran-gdata, r-cran-plyr, r-cran-progress, r-cran-desctools, r-cran-scales, r-cran-fields, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-mass, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-swaglm_0.0.1-1.ca2604.1_arm64.deb Size: 440246 MD5sum: c1b0e739231cf32129379c003a994f33 SHA1: f09c7655f3a97302aee198b6f786833942e7b512 SHA256: 6f25c9cf328e80afa4058753b193280dde9291de0be19bae8203af8dcc3653e7 SHA512: e335e4614d76db2c06a45f9ce42f0ad400516960077652dcf162b74bab0cca5b5a0ef43959e5b1554e8ba5fdca2d04b875d2d02ce8e144e7481c55a5d94ab923 Homepage: https://cran.r-project.org/package=swaglm Description: CRAN Package 'swaglm' (Fast Sparse Wrapper Algorithm for Generalized Linear Models andTesting Procedures for Network of Highly Predictive Variables) Provides a fast implementation of the SWAG algorithm for Generalized Linear Models which allows to perform a meta-learning procedure that combines screening and wrapper methods to find a set of extremely low-dimensional attribute combinations. The package then performs test on the network of selected models to identify the variables that are highly predictive by using entropy-based network measures. Package: r-cran-swatches Architecture: arm64 Version: 0.5.0-1.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-httr, r-cran-pack, r-cran-stringr, r-cran-xml2, r-cran-colorspace Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-swatches_0.5.0-1.ca2604.1_arm64.deb Size: 61364 MD5sum: 5857bc7ce561b98f3731789dea1876ed SHA1: 5c4e4b16bd8dbe008ccf3eb8bf5038fbfb039738 SHA256: f976eefc36d7c23d93147438d2c615f6a6114acfecb9bff8dbb682dc58d1a4c7 SHA512: b689fc390b91ff73e554e59fa070dd02ae13854b8f83502c870da14dbc7074503607441f18f468e7ac606efd7fdc990e3edf9761d91706c9265b8907b14dcbff Homepage: https://cran.r-project.org/package=swatches Description: CRAN Package 'swatches' (Read, Inspect, and Manipulate Color Swatch Files) There are numerous places to create and download color palettes. These are usually shared in 'Adobe' swatch file formats of some kind. There is also often the need to use standard palettes developed within an organization to ensure that aesthetics are carried over into all projects and output. Now there is a way to read these swatch files in R and avoid transcribing or converting color values by hand or or with other programs. This package provides functions to read and inspect 'Adobe Color' ('ACO'), 'Adobe Swatch Exchange' ('ASE'), 'GIMP Palette' ('GPL'), 'OpenOffice' palette ('SOC') files and 'KDE Palette' ('colors') files. Detailed descriptions of 'Adobe Color' and 'Swatch Exchange' file formats as well as other swatch file formats can be found at . Package: r-cran-swdpwr Architecture: arm64 Version: 1.12-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 222 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.6.0), r-api-4.0, r-cran-spatstat.random Filename: pool/dists/resolute/main/r-cran-swdpwr_1.12-1.ca2604.1_arm64.deb Size: 102546 MD5sum: e00ff6ae4cd188396cd6c4623a4aca96 SHA1: e15102074b9e97e945daeaa74ae2ac85bb453b60 SHA256: 1229033a59bce28be3501fcf56e1f4ba0815f4a278d9982dfc44b9aa43a7a3aa SHA512: e2b4147ba5a630e9892a582049943777f9b936cc088848fe5d2e5b80f119aee956a5c175d24273bf05252492bc5306f0dbf13ef6fa6633c6e0a4bdb519edd85c Homepage: https://cran.r-project.org/package=swdpwr Description: CRAN Package 'swdpwr' (Power Calculation for Stepped Wedge Cluster Randomized Trials) To meet the needs of statistical power calculation for stepped wedge cluster randomized trials, we developed this software. Different parameters can be specified by users for different scenarios, including: cross-sectional and cohort designs, binary and continuous outcomes, marginal (GEE) and conditional models (mixed effects model), three link functions (identity, log, logit links), with and without time effects (the default specification assumes no-time-effect) under exchangeable, nested exchangeable and block exchangeable correlation structures. Unequal numbers of clusters per sequence are also allowed. The methods included in this package: Zhou et al. (2020) , Li et al. (2018) . Supplementary documents can be found at: . The Shiny app for swdpwr can be accessed at: . The package also includes functions that perform calculations for the intra-cluster correlation coefficients based on the random effects variances as input variables for continuous and binary outcomes, respectively. Package: r-cran-sweater Architecture: arm64 Version: 0.1.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 998 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 630656 MD5sum: a816a2757f626953461ff8b119451ea0 SHA1: d7c5c78d0d5583297b7a71740a3c179cbe4531bd SHA256: 2e5ff5a22c27dbe593c2720344b0c6e11353ed2723f9402c5cc4ace94b395118 SHA512: cfe5cfc9e5909681f0166597d4d7f38cdc5651efb625efa2774ef6f84bd2b04f99dc5c768fedbac96cbf60d939177363f788793d4abb3ae1e4cdd201e68a25e9 Homepage: https://cran.r-project.org/package=sweater Description: CRAN Package 'sweater' (Speedy Word Embedding Association Test and Extras Using R) Conduct various tests for evaluating implicit biases in word embeddings: Word Embedding Association Test (Caliskan et al., 2017), , Relative Norm Distance (Garg et al., 2018), , Mean Average Cosine Similarity (Mazini et al., 2019) , SemAxis (An et al., 2018) , Relative Negative Sentiment Bias (Sweeney & Najafian, 2019) , and Embedding Coherence Test (Dev & Phillips, 2019) . Package: r-cran-swephr Architecture: arm64 Version: 0.3.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1150 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_arm64.deb Size: 475256 MD5sum: dbf2943d12afcaed5f7c8801b25f1db8 SHA1: 7f2e4697b5a781cdb5539a04b1eb1fb8127a919d SHA256: 0486fd0465b35556b6d533c965b86f4e1c82e781f6d0ab2654e16506f157857b SHA512: a40382df9425a9526409d55370b64b2ef95b61b603e2fe37228bc749c105b12e67f3e602dd09e450c4873c08d9c80359cd24e10ce75d0af5729d8ef5d95c6032 Homepage: https://cran.r-project.org/package=swephR Description: CRAN Package 'swephR' (High Precision Swiss Ephemeris) The Swiss Ephemeris (version 2.10.03) is a high precision ephemeris based upon the DE431 ephemerides from NASA's JPL. It covers the time range 13201 BCE to 17191 CE. This package uses the semi-analytic theory by Steve Moshier. For faster and more accurate calculations, the compressed Swiss Ephemeris data is available in the 'swephRdata' package. To access this data package, run 'install.packages("swephRdata", repos = "https://rstub.r-universe.dev", type = "source")'. The size of the 'swephRdata' package is approximately 115 MB. The user can also use the original JPL DE431 data. Package: r-cran-switchselection Architecture: arm64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1083 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_arm64.deb Size: 798756 MD5sum: f9b523b63f5a1846494d4b9a203205ec SHA1: 01b8d36f1de4e1c622b704d00df4c8b846937147 SHA256: e202bcf73df7e96c8ebcda80d210523c61b290c117af396e725ac8db2e2d7318 SHA512: ecfaae49d36769fb356ba3be39740403f5d6501877b683c949ec079767e9423771949f3569ef6fdd380694b306d418ecf7b519756d2f91cdcbae0bb39d43da20 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: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1634 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 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_arm64.deb Size: 1102940 MD5sum: ed8d91656dcd317b758e6dd716f92170 SHA1: 183fbb002a6911ff2871dd97af4fbc4904e49810 SHA256: 6c30d9c3846a9b5b8bdc0f62907a262b82f3e336bcff04b6b6388d48a540bf49 SHA512: a05c1d43e972e4efb88ad339689c60ea8b5a4b36d958bddbcbfb1570c002e580032b555f047d0df6359bda58b93bc54d677261d1c64c21d85cfd87422a9bcf7a 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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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. 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Package: r-cran-synchronicity Architecture: arm64 Version: 1.3.10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 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_arm64.deb Size: 90794 MD5sum: c8f491c3a71cd9daf7082bf97c2e6081 SHA1: 0e04a05360c7135ab057df62f6d6fb0697838bb8 SHA256: a897f3c29e262d0db716793111cf5ffcc9ba21087221e313fbfe09dd700ba4e0 SHA512: bf365abcadc586ecf9309cb557d31a6bb036fa3d93b099e81e572647a3d79bcf6fe89916eebe5f4bc4ed016af35e52b560394f451950abf0255aafe73e90093e Homepage: https://cran.r-project.org/package=synchronicity Description: CRAN Package 'synchronicity' (Boost Mutex Functionality in R) Boost mutex functionality in R. 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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. 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As font handling varies between systems it is difficult to correctly locate installed fonts across different operating systems. The 'systemfonts' package provides bindings to the native libraries on Windows, macOS and Linux for finding font files that can then be used further by e.g. graphic devices. The main use is intended to be from compiled code but 'systemfonts' also provides access from R. Package: r-cran-systemicrisk Architecture: arm64 Version: 0.4.3-1.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-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_arm64.deb Size: 320720 MD5sum: 5d0da5c3918382d6a948c75f4b0d643f SHA1: 5bd7b80208dd678745692f93dba060f53ba81009 SHA256: 5fac855d3cd3e6333afbb7f9c9b61125fbfc45e502fe09edc2b83bc7d25ecdff SHA512: b57527085ea08ca3f00ae4859bd07b0ed81b0e2ebb54ede2878b5c46559f01e61e2daafe150e73e1a94ac9fa29925d3df647357636d22bc0ba308ef452129262 Homepage: https://cran.r-project.org/package=systemicrisk Description: CRAN Package 'systemicrisk' (Systemic Risk and Network Reconstruction) Analysis of risk through liability matrices. Contains a Gibbs sampler for network reconstruction, where only row and column sums of the liabilities matrix as well as some other fixed entries are observed, following the methodology of Gandy&Veraart (2016) . It also incorporates models that use a power law distribution on the degree distribution. 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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. 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These functions can be used to model deterministic computer experiments, obtain predictions at new inputs, and quantify the uncertainty of the predictions. This research is supported by a U.S. National Science Foundation grant DMS-1712642 and a U.S. Army Research Office grant W911NF-17-1-0007. Package: r-cran-tagcloud Architecture: arm64 Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 485 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 336082 MD5sum: ddbddc40724eb7937778cefb29f70567 SHA1: e66c301d2eee7a97e69ff91c8fa07f972454a938 SHA256: 91eab304833ad142a99c5554574119476fd53b7a694d0c33b338536b3fc2bbd9 SHA512: 1a8f9ed901fe76e0c97ab496459283450220d576018375b9f483395b39674da2d2ee0fcff7c3d8991c84ee7199f706533367fc142edddb2691a2b8918d81ab8c Homepage: https://cran.r-project.org/package=tagcloud Description: CRAN Package 'tagcloud' (Tag Clouds) Generating Tag and Word Clouds. Package: r-cran-tagtools Architecture: arm64 Version: 0.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1517 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), 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_arm64.deb Size: 1317380 MD5sum: 8e482a5e0a46103641a076ac0bc95be3 SHA1: b9fc9538b934d3fb904d4c12ad58b88f2216de46 SHA256: 20f884373a6c77149df1b859ed46ab07414a2a8a4befef6ecaedd98c4297e8fa SHA512: 4c769cde054229ddcfed36719db5a213516c107cbc324743cb38d60545142b5ebed0f367801cf335e91ef05a1c4e5ed7ba1bfa978b51daad10f7762194f0ddab 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: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 557 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_arm64.deb Size: 459930 MD5sum: a0ac7d2934512d1bc54632778453968e SHA1: ea18e1395e6b23e78dae0f4baded5d26753d4f95 SHA256: 36cde524d4c40c8a0b8d6eaba4fc0881ab921b3cf181744596e63d871ffefd70 SHA512: 6db84e0e262f3bf88a8f59376f370d9fd961434c8aade4631a61365f12b50fb9057f402385cab4f499127ed11876b3d3e69ff41a73c20d9e6748f91d9876a0c7 Homepage: https://cran.r-project.org/package=tailDepFun Description: CRAN Package 'tailDepFun' (Minimum Distance Estimation of Tail Dependence Models) Provides functions implementing minimal distance estimation methods for parametric tail dependence models, as proposed in Einmahl, J.H.J., Kiriliouk, A., Krajina, A., and Segers, J. (2016) and Einmahl, J.H.J., Kiriliouk, A., and Segers, J. (2018) . Package: r-cran-tailplots Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 286 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 106890 MD5sum: 2b379aa69699c4c23817ac870daf0934 SHA1: dead9b7b765f69b360043f5a7988c70df024a1e9 SHA256: f5f2866d04a840cda4c2b010082312386be4fa74b04e3f9ff0713fe0a2594951 SHA512: 29f0738898071c40fbcddf94c9c72f5bd2f8da2815815c9ce408ce6452e36f82da5b335f90949a1a3ebd30f4f8645aac1c2cfc02ca410fcbeb14cda37b183daf Homepage: https://cran.r-project.org/package=tailplots Description: CRAN Package 'tailplots' (Estimators and Plots for Gamma and Pareto Tail Detection) Estimators for two functionals used to detect Gamma, Pareto or Lognormal distributions, as well as distributions exhibiting similar tail behavior, as introduced by Iwashita and Klar (2023) and Klar (2024) . One of these functionals, g, originally proposed by Asmussen and Lehtomaa (2017) , distinguishes between log-convex and log-concave tail behavior. Furthermore the characterization of the lognormal distribution is based on the work of Mosimann (1970) . The package also includes methods for visualizing these estimators and their associated confidence intervals across various threshold values. Package: r-cran-talib Architecture: arm64 Version: 0.9-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 18463 Depends: libc6 (>= 2.17), r-base-core (>= 4.6.0), r-api-4.0 Suggests: r-cran-ggplot2, r-cran-knitr, r-cran-plotly, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-talib_0.9-2-1.ca2604.1_arm64.deb Size: 3627684 MD5sum: 7c64f75220286003b1d8f4003a349d18 SHA1: bd2f06032e5b91cb1067d12efdc76fa85fc9ab9c SHA256: 368e9b4a6e5a70e36b2ff8534b0c5d883f0783beffe86e0c9863fa4d1fcfad6f SHA512: 53778a7f15904ffada717ca2191e05bdbcbcca1d426b96f5aaf1245dec135a0f101433b93a0e6d2dfd18418d13a2ab1136822f7afd926b5ff9a7e8856843fc67 Homepage: https://cran.r-project.org/package=talib Description: CRAN Package 'talib' (Interface to 'TA-Lib' for Technical Analysis and CandlestickPatterns) Interface to the 'TA-Lib' (Technical Analysis Library) 'C' library, providing access to 150+ indicators (e.g. Average Directional Movement Index (ADX), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), Stochastic Oscillator, Bollinger Bands), candlestick pattern recognition, and rolling-window utilities. Core computations are implemented in 'C' for fast Open-High-Low-Close-Volume (OHLCV) time-series feature engineering and rule-based signal generation, with optional interactive visualization via 'plotly'. Package: r-cran-tall Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4476 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2162206 MD5sum: 55a280ad93f94626e512d6dcc0fcdf2c SHA1: af74aa34a8505b968e7fffcd325dd0f3edc699e3 SHA256: 989bd43da9858f722591a818364458a244e1274c82d079523e4fd58a7a1fa2ff SHA512: 645f9b64523a1acd105304d45d9c622e1e8d04ad596ba494eb8e8ba4f32b2aba0a689be2496ebaf7b944da9e2fa9baf8281f8e010a334232d76115556fe4224b 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 . 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Tools for summarizing, illustrating and manipulating the cluster objects are also available. Package: r-cran-tapes Architecture: arm64 Version: 0.14.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1301 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-taper, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-rbenchmark, r-cran-rbdat, r-cran-rodbc Filename: pool/dists/resolute/main/r-cran-tapes_0.14.1-1.ca2604.1_arm64.deb Size: 959786 MD5sum: 304ade4a110488e610aee7fa605ba252 SHA1: 9005609f63c9e1add1df0992309b421517a5fd2f SHA256: 3c1333d817aa123f4f2037b7419aa4f1151828eae15293f2669cd00b9b824b71 SHA512: c1e980bf02e02f87486f107324830c69ec5ad1c52418b6f8a9ed5e322dd0a0d6e412d0b6d4c373b7ff2f78e29e91a08b4df66997ddcf8aeca7ffc1805d1f3e22 Homepage: https://cran.r-project.org/package=TapeS Description: CRAN Package 'TapeS' (Tree Taper Curves and Sorting Based on 'TapeR') Providing new german-wide 'TapeR' Models and functions for their evaluation. Included are the most common tree species in Germany (Norway spruce, Scots pine, European larch, Douglas fir, Silver fir as well as European beech, Common/Sessile oak and Red oak). Many other species are mapped to them so that 36 tree species / groups can be processed. Single trees are defined by species code, one or multiple diameters in arbitrary measuring height and tree height. The functions then provide information on diameters along the stem, bark thickness, height of diameters, volume of the total or parts of the trunk and total and component above-ground biomass. It is also possible to calculate assortments from the taper curves. Uncertainty information is provided for diameter, volume and component biomass estimation. Package: r-cran-taqmngr Architecture: arm64 Version: 2018.5-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 319 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_arm64.deb Size: 102886 MD5sum: 5b4a7ded7e538d640f1dc49ff67fde15 SHA1: 0d5a345b90f6bf127f75febd9dd30352dc15c0ec SHA256: b2f16a6b1e0733a751d9262e0ed74388363d8f0d3f3c2d17b313d86a1f167299 SHA512: 2410b5121e8c08008f68f04267639787efc0db2d4d96c6dba442fd3931a4da599fd747eef988f0816a82e98954e6f7ad08c6f9fa21a031f6f9082fcc9d8e50d0 Homepage: https://cran.r-project.org/package=TAQMNGR Description: CRAN Package 'TAQMNGR' (Manage Tick-by-Tick Transaction Data) Manager of tick-by-tick transaction data that performs 'cleaning', 'aggregation' and 'import' in an efficient and fast way. 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Package: r-cran-tardis Architecture: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-dplyr, r-cran-magrittr, r-cran-purrr, r-cran-rlang, r-cran-stringi, r-cran-stringr, r-cran-tidyr, r-cran-cpp11 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tardis_0.1.5-1.ca2604.1_arm64.deb Size: 122794 MD5sum: ba6dcf08a52f466853e586532bae4794 SHA1: 9664f8af5cfb8627d9d6f2b403a0fe6cd6856592 SHA256: dd5763634da45ec07bd7486ab6e82415b57cebab30e92a075b53d6df78941689 SHA512: 282364f65f07ecbbf5e7278eb7fc590fd14f9e1b0fb2f9d13e2a4cc11fd98c6bcea86b2b63031c266c05fcb97f958d52cd01205b3591b2e39e0d5a8c531e97a5 Homepage: https://cran.r-project.org/package=tardis Description: CRAN Package 'tardis' (Text Analysis with Rules and Dictionaries for InferringSentiment) Measure text's sentiment with dictionaries and simple rules covering negations and modifiers. User-supplied dictionaries are supported, including Unicode emojis and multi-word tokens, so this package can also be used to study constructs beyond sentiment. Package: r-cran-targeted Architecture: arm64 Version: 0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3106 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-r6, r-cran-rcpp, r-cran-abind, r-cran-cli, r-cran-future.apply, r-cran-lava, r-cran-mets, r-cran-quadprog, r-cran-progressr, r-cran-rlang, r-cran-survival, r-cran-rcpparmadillo Suggests: r-cran-superlearner, r-cran-mass, r-cran-cmprsk, r-cran-data.table, r-cran-e1071, r-cran-earth, r-cran-glmnet, r-cran-grf, r-cran-hal9001, r-cran-mgcv, r-cran-nnls, r-cran-optimx, r-cran-polle, r-cran-pracma, r-cran-quarto, r-cran-randomforestsrc, r-cran-ranger, r-cran-riskregression, r-cran-scatterplot3d, r-cran-tinytest, r-cran-viridislite, r-cran-xgboost Filename: pool/dists/resolute/main/r-cran-targeted_0.7.1-1.ca2604.1_arm64.deb Size: 2036486 MD5sum: b03797c525ccaaa28141b178894d4782 SHA1: 81aca8beac4bb1e41a846720fbca64cdd9cf4b35 SHA256: 6e0da879b469e89985894d4253cfe3187d5b8b8f26552086d58a4df86d1ad021 SHA512: 59a448d9349f6110b720deb642f80430cb45825100cc35888150f172638e60e14132c7a3405b648622613d436d7d7c6e0c309c06562c9db8136205540e601cda Homepage: https://cran.r-project.org/package=targeted Description: CRAN Package 'targeted' (Targeted Inference) Various methods for targeted and semiparametric inference including augmented inverse probability weighted (AIPW) estimators for missing data and causal inference (Bang and Robins (2005) ), variable importance and conditional average treatment effects (CATE) (van der Laan (2006) ), estimators for risk differences and relative risks (Richardson et al. (2017) ), assumption lean inference for generalized linear model parameters (Vansteelandt et al. (2022) ). Package: r-cran-tau Architecture: arm64 Version: 0.0-28-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 247 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-tm Filename: pool/dists/resolute/main/r-cran-tau_0.0-28-1.ca2604.1_arm64.deb Size: 145774 MD5sum: ff82d4a026e6063dc15441d77aa160b6 SHA1: 6c1bd6bfca6584e93435c875e4c560cb177a03d9 SHA256: fa0fe5cf0b03b75a8d4e0f6e743fc31b83eea67e8f8d869996d78d09b5bc1281 SHA512: 61fe8f94862861d71e3e5d2a814aeac91613c2d8fe37ea418599ffcdd02e245a6f96364b46d0f3bff3ff735357d97f051710df7acc29e38984347dd5ba463523 Homepage: https://cran.r-project.org/package=tau Description: CRAN Package 'tau' (Text Analysis Utilities) Utilities for text analysis. Package: r-cran-taustar Architecture: arm64 Version: 1.1.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 374 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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-taustar_1.1.9-1.ca2604.1_arm64.deb Size: 141308 MD5sum: cfff3b6818d0ff36cdce6b8e90b4dab4 SHA1: 2c9307f74208f2003ec5a3c6f0914a768f1d1c7b SHA256: 5a5f3134f8bfe2531902301a8d96a85bd4b21dd9dd9103ec2e465d43908f5f4a SHA512: 8ecd1ff37a1801579f807b780b1c4fda25c661ebfaff4982d9a26b1c7c4cf0094a7c787b0e75330bc0c2970519f40a0eff5775d7b094da9ba08117ff4d396d55 Homepage: https://cran.r-project.org/package=TauStar Description: CRAN Package 'TauStar' (Efficient Computation and Testing of the Bergsma-Dassios SignCovariance) Computes the t* statistic corresponding to the tau* population coefficient introduced by Bergsma and Dassios (2014) and does so in O(n^2) time following the algorithm of Heller and Heller (2016) building off of the work of Weihs, Drton, and Leung (2016) . Also allows for independence testing using the asymptotic distribution of t* as described by Nandy, Weihs, and Drton (2016) . Package: r-cran-taxonomizr Architecture: arm64 Version: 0.11.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rsqlite, r-cran-r.utils, r-cran-data.table, r-cran-curl Suggests: r-cran-testthat, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-taxonomizr_0.11.1-1.ca2604.1_arm64.deb Size: 157154 MD5sum: 99531503c5bf69cd0bbbb6c65377ad00 SHA1: f8543ea87a1cce70e6de91fad33fd73130e6b654 SHA256: fe92aec11da7d865a731a0dd30b49075693596bb2fc494fec879c14f6201a27d SHA512: bf3795de859582fd840463e1a67740d4a12b2526da8ff8c94cc4865d281f9285e2d977d4b5ccc1cad1f4c9634cec0de984dfcfbdcbfc5adb2f9680175888a37d Homepage: https://cran.r-project.org/package=taxonomizr Description: CRAN Package 'taxonomizr' (Functions to Work with NCBI Accessions and Taxonomy) Functions for assigning taxonomy to NCBI accession numbers and taxon IDs based on NCBI's accession2taxid and taxdump files. This package allows the user to download NCBI data dumps and create a local database for fast and local taxonomic assignment. Package: r-cran-tbl2xts Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 390 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-xts, r-cran-zoo, r-cran-dplyr, r-cran-tibble, r-cran-rlang Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-ggplot2, r-cran-performanceanalytics Filename: pool/dists/resolute/main/r-cran-tbl2xts_1.0.4-1.ca2604.1_arm64.deb Size: 236744 MD5sum: 225e1a5eba9762666d126614fa151689 SHA1: d43c219e0d52e7f7c3e1b1331bb46e94fcbe041f SHA256: f6720e9a0261d4e3380dbc26c5f0e0a39dc9717c60d45bb79ee3d1198751c3f7 SHA512: b6a5d5707dba993d23f5fae95c4173a09a0b477ed24f2d5ce51547f291161b5d6757e17368dd4fd8b051c3e4f18bfa8be5fc9cd0e5730ff9d303b487a17bb47d Homepage: https://cran.r-project.org/package=tbl2xts Description: CRAN Package 'tbl2xts' (Convert Tibbles or Data Frames to Xts Easily) Facilitate the movement between data frames to 'xts'. Particularly useful when moving from 'tidyverse' to the widely used 'xts' package, which is the input format of choice to various other packages. It also allows the user to use a 'spread_by' argument for a character column 'xts' conversion. Package: r-cran-tbrdist Architecture: arm64 Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1011 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-rdpack, r-cran-treedist, r-cran-treetools, r-cran-bh, r-cran-rcpp Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tbrdist_2.0.0-1.ca2604.1_arm64.deb Size: 434212 MD5sum: 0b8debdc24d239e394bcc53b28198e97 SHA1: 57841fca67949ebf1e842e9b6b3de766a115716e SHA256: 8707100478aaad000936231065e3e0798d6b2befe3aecaff243b222010c05672 SHA512: 177059f4baee3050f33db938e5967bd79a25031bbf7789722af0319cf1a98b94f5989c51de12bb8f3d1510449c1bbd4e484520c4e6080031daccf75730a06aaa Homepage: https://cran.r-project.org/package=TBRDist Description: CRAN Package 'TBRDist' (Rearrangement Distances Between Phylogenetic Trees) Fast calculation of tree rearrangement distances. For unrooted trees: Subtree Prune and Regraft (SPR), Tree Bisection and Reconnection (TBR), and Replug distances, using the algorithms of Whidden and Matsen (2017) . For rooted trees: rooted SPR (rSPR) distance, using the fixed-parameter algorithms of Whidden, Beiko, and Zeh (2013) . Package: r-cran-tchazards Architecture: arm64 Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3666 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_arm64.deb Size: 521740 MD5sum: d569f8f32dcca66d802eca8ef73b6e07 SHA1: 274945b23d546270ffb6a0f15e077f8b1b62b10e SHA256: fecdc510a8ee96fc67e96a69e01c67dd9d86247b2d4761118b6d318bf8e47728 SHA512: 58e77cd5f3735602fd2019cda7b9e27be73c5fda2ceb304edde84d8bf543d6067684b462b043df636a0c068c85659ee37380d7f5b166f7fd8499a2085aa54ff3 Homepage: https://cran.r-project.org/package=TCHazaRds Description: CRAN Package 'TCHazaRds' (Tropical Cyclone (Hurricane, Typhoon) Spatial Hazard Modelling) Methods for generating modelled parametric Tropical Cyclone (TC) spatial hazard fields and time series output at point locations from TC tracks. R's compatibility to simply use fast 'cpp' code via the 'Rcpp' package and the wide range spatial analysis tools via the 'terra' package makes it an attractive open source environment to study 'TCs'. This package estimates TC vortex wind and pressure fields using parametric equations originally coded up in 'python' by 'TCRM' and then coded up in 'Cuda' 'cpp' by 'TCwindgen' . Package: r-cran-tci Architecture: arm64 Version: 0.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1515 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-ggplot2, r-cran-mvtnorm, r-cran-reshape, r-cran-rcpp, r-cran-knitr, r-cran-rcpparmadillo Suggests: r-cran-testthat, r-cran-rmarkdown, r-cran-mrgsolve, r-cran-gridextra, r-cran-reshape2, r-cran-truncnorm, r-cran-xtable Filename: pool/dists/resolute/main/r-cran-tci_0.2.1-1.ca2604.1_arm64.deb Size: 937452 MD5sum: a9adf26dd3b8e209a09ac0c73cc8c20e SHA1: d76c7c8ff2bb9955bb220b913c19d3c044adf065 SHA256: 9b896f4340ac5d1f4cc93d472cbe9ba4fa40dd5885994e327273a3a5076fb438 SHA512: 5f39844a4c07f248801ad222512f80a277fe0f7dc20f4a8e79fad246f219d796776b404b59b24857759c20f36c651803faad8a93fdf01b693ea52506b4d3891d Homepage: https://cran.r-project.org/package=tci Description: CRAN Package 'tci' (Target Controlled Infusion (TCI)) Implementation of target-controlled infusion algorithms for compartmental pharmacokinetic and pharmacokinetic-pharmacodynamic models. Jacobs (1990) ; Marsh et al. (1991) ; Shafer and Gregg (1993) ; Schnider et al. (1998) ; Abuhelwa, Foster, and Upton (2015) ; Eleveld et al. (2018) . Package: r-cran-tciu Architecture: arm64 Version: 1.2.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 13503 Depends: libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-dplyr, r-cran-tidyr, r-cran-rcolorbrewer, r-cran-fancycut, r-cran-scales, r-cran-plotly, r-cran-gridextra, r-cran-ggpubr, r-cran-icsnp, r-cran-rrcov, r-cran-geometry, r-cran-dt, r-cran-forecast, r-cran-fmri, r-cran-pracma, r-cran-zoo, r-cran-extradistr, r-cran-foreach, r-cran-spatstat.explore, r-cran-spatstat.geom, r-cran-cubature, r-cran-doparallel, r-cran-reshape2, r-cran-multiwayregression, r-cran-interp Suggests: r-cran-oro.nifti, r-cran-magrittr, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tciu_1.2.8-1.ca2604.1_arm64.deb Size: 2775280 MD5sum: 677556470cf2774d4503d30b970ebe35 SHA1: a378c347b872815d17fabd426bd07923dde13840 SHA256: e5e7c26c2d15144bebc4db67a3cd1d987110e9f6014955265aee85d1bf9aad9e SHA512: 7620fbae63c93d9178257614e3521970b6d8c4e4c8996953845794a685e67ea3b28088c7a8c37151f04ef6f5c2f8d33e8af440df449acfe117479e4cb8c9fc50 Homepage: https://cran.r-project.org/package=TCIU Description: CRAN Package 'TCIU' (Spacekime Analytics, Time Complexity and Inferential Uncertainty) Provide the core functionality to transform longitudinal data to complex-time (kime) data using analytic and numerical techniques, visualize the original time-series and reconstructed kime-surfaces, perform model based (e.g., tensor-linear regression) and model-free classification and clustering methods in the book Dinov, ID and Velev, MV. (2021) "Data Science: Time Complexity, Inferential Uncertainty, and Spacekime Analytics", De Gruyter STEM Series, ISBN 978-3-11-069780-3. . The package includes 18 core functions which can be separated into three groups. 1) draw longitudinal data, such as Functional magnetic resonance imaging(fMRI) time-series, and forecast or transform the time-series data. 2) simulate real-valued time-series data, e.g., fMRI time-courses, detect the activated areas, report the corresponding p-values, and visualize the p-values in the 3D brain space. 3) Laplace transform and kimesurface reconstructions of the fMRI data. Package: r-cran-tclust Architecture: arm64 Version: 2.2-0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1844 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 1417774 MD5sum: 55b419ca8f0c2b83077c6af1c11b731a SHA1: d5c4c06727912d0db007159e623949573d5d88ee SHA256: 96a63a39a0376760485e249de8514aff399ecc5387424a9b378b8850a0f13880 SHA512: c926917449df40f36490d678793909498763b7c59a98fa6bfc7c57c197e01aafbff49109d3cb49ebaf4cf60e0bae98a8c89f72e4050f5a430ff743469ecd2337 Homepage: https://cran.r-project.org/package=tclust Description: CRAN Package 'tclust' (Robust Trimmed Clustering) Provides functions for robust trimmed clustering. The methods are described in Garcia-Escudero (2008) , Fritz et al. (2012) , Garcia-Escudero et al. (2011) and others. Package: r-cran-tcv Architecture: arm64 Version: 0.1.0-1.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), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gfm, r-cran-countsplit, r-cran-irlba, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tcv_0.1.0-1.ca2604.1_arm64.deb Size: 66506 MD5sum: be089bf741f7cb6ef6034de9da03c1ab SHA1: 586c5b6f28608ab0ea5dd594b79af5676f961dd8 SHA256: 9e2c1bc19c38bbd5a4b378c3fb042c230dee87112be66e06bad74f206c9b3a56 SHA512: 5d1107e88c5d029fde01576332f0922859094ebeb46bc8a79c33d25d85646572323ef2c928600996973cba5bb23226555949c69d5c96988c12a962b83a3b4909 Homepage: https://cran.r-project.org/package=tcv Description: CRAN Package 'tcv' (Determining the Number of Factors in Poisson Factor Models viaThinning Cross-Validation) Implements methods for selecting the number of factors in Poisson factor models, with a primary focus on Thinning Cross-Validation (TCV). 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Package: r-cran-tda Architecture: arm64 Version: 1.9.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2831 Depends: libc6 (>= 2.32), 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-igraph, r-cran-scales, r-cran-bh, r-cran-rcppeigen Suggests: r-cran-testthat, r-cran-lintr Filename: pool/dists/resolute/main/r-cran-tda_1.9.4-1.ca2604.1_arm64.deb Size: 1976672 MD5sum: d0611d3e584ddd3de2d09b4318c78845 SHA1: 029326ace0bf4250279422e69a8178605642c737 SHA256: 89dd48f6b5ac3194c650f68156a858c6e3de6e930fe7f57c110b70a9606236f8 SHA512: 9a57ddf128ce804223c6858c05b062cc08d53fb89c63ea06de59f4113b0e6e79ee0e7b00db94130db26644bb938673cf1714d1a879720aec51843a7708da9f86 Homepage: https://cran.r-project.org/package=TDA Description: CRAN Package 'TDA' (Statistical Tools for Topological Data Analysis) Tools for Topological Data Analysis. The package focuses on statistical analysis of persistent homology and density clustering. For that, this package provides an R interface for the efficient algorithms of the C++ libraries 'GUDHI' , 'Dionysus' , and 'PHAT' . This package also implements methods from Fasy et al. (2014) and Chazal et al. (2015) for analyzing the statistical significance of persistent homology features. Package: r-cran-tdakit Architecture: arm64 Version: 0.1.3-1.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), 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-rdpack, r-cran-tdastats, r-cran-t4cluster, r-cran-energy, r-cran-ggplot2, r-cran-maotai, r-cran-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-tdakit_0.1.3-1.ca2604.1_arm64.deb Size: 187964 MD5sum: c9502257739e92d8f28e23539d171123 SHA1: 5733eca4cd8d984891ead62af4c5e2e5dcabe9ad SHA256: 2cdf4675404bdc1f2460044505909853ab4effb7ffa5a0aff321df12b2941c6a SHA512: 1f47d5c821f67a2c984cab16c74e5ad6fea83f4b6c84914b2f18b65bd249fa5bde98904a0e46149a807358d9be8f72111cac474531e7f50e2da0456ee806a4a2 Homepage: https://cran.r-project.org/package=TDAkit Description: CRAN Package 'TDAkit' (Toolkit for Topological Data Analysis) Topological data analysis studies structure and shape of the data using topological features. We provide a variety of algorithms to learn with persistent homology of the data based on functional summaries for clustering, hypothesis testing, visualization, and others. We refer to Wasserman (2018) for a statistical perspective on the topic. Package: r-cran-tdapplied Architecture: arm64 Version: 3.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9011 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_arm64.deb Size: 3926380 MD5sum: 2720f62995cb1edabebc356ba5157466 SHA1: 0c0c845860ae14479645256bab3eb3dd3598ab4e SHA256: 4e33f7c903de9e0e67aa35264c1cf8606e8b4491532c5de8f7bd7fcf8f1f5bb6 SHA512: 5e2f3b053d20f5a7f28166da3e28e8465728a1f5d0569583d65bab9be61eb0bd766995b5fb5f5dace20031f483d041e64fd5b6df6b06208c1baca3abc0d843ee Homepage: https://cran.r-project.org/package=TDApplied Description: CRAN Package 'TDApplied' (Machine Learning and Inference for Topological Data Analysis) Topological data analysis is a powerful tool for finding non-linear global structure in whole datasets. The main tool of topological data analysis is persistent homology, which computes a topological shape descriptor of a dataset called a persistence diagram. 'TDApplied' provides useful and efficient methods for analyzing groups of persistence diagrams with machine learning and statistical inference, and these functions can also interface with other data science packages to form flexible and integrated topological data analysis pipelines. Package: r-cran-tdastats Architecture: arm64 Version: 0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 730 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 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_arm64.deb Size: 364498 MD5sum: aef15f831c0f136359f77c96e148ea46 SHA1: 79c7566351be1da1354d5e39b345f959ed21817b SHA256: b75991a1c999ebe81241a5814fa4e9ef9b227ec15886c590512f7e53beb5be08 SHA512: 066382062364259a420ae64593928d387edc4a9702c7b17f4738b88362b11ce85376fa5e6e97ae72746ea365e7d5ea1ce7ae07a0d729155181f1d568cc2d4cca Homepage: https://cran.r-project.org/package=TDAstats Description: CRAN Package 'TDAstats' (Pipeline for Topological Data Analysis) A comprehensive toolset for any useR conducting topological data analysis, specifically via the calculation of persistent homology in a Vietoris-Rips complex. The tools this package currently provides can be conveniently split into three main sections: (1) calculating persistent homology; (2) conducting statistical inference on persistent homology calculations; (3) visualizing persistent homology and statistical inference. The published form of TDAstats can be found in Wadhwa et al. (2018) . For a general background on computing persistent homology for topological data analysis, see Otter et al. (2017) . To learn more about how the permutation test is used for nonparametric statistical inference in topological data analysis, read Robinson & Turner (2017) . To learn more about how TDAstats calculates persistent homology, you can visit the GitHub repository for Ripser, the software that works behind the scenes at . This package has been published as Wadhwa et al. (2018) . 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It also includes functionality for converting data between different frequencies. Package: r-cran-tdavec Architecture: arm64 Version: 0.1.41-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 583 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-tdastats Filename: pool/dists/resolute/main/r-cran-tdavec_0.1.41-1.ca2604.1_arm64.deb Size: 238764 MD5sum: e5ca8c36a327effa3a8e638bc5919e12 SHA1: 44ac18e54c8fe6d51906b97623b6361a5276cf0c SHA256: edf393f32ba08f5ba17b865bba2d63d4fa498324cf06407eef4dae35de282abd SHA512: 90d8318bb6846c42acd902978270a1c5b96e0edcef7ddf809d55e7dafa6cf4e73bb002b1061833f7ee90c29b0b11a10b873a207fa1f87d7c5d1accfbd014a49c Homepage: https://cran.r-project.org/package=TDAvec Description: CRAN Package 'TDAvec' (Vector Summaries of Persistence Diagrams) Provides tools for computing various vector summaries of persistence diagrams studied in Topological Data Analysis. For improved computational efficiency, all code for the vector summaries is written in 'C++' using the 'Rcpp' and 'RcppArmadillo' packages. 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Package: r-cran-tdroc Architecture: arm64 Version: 2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 260 Depends: libc6 (>= 2.17), 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 Filename: pool/dists/resolute/main/r-cran-tdroc_2.0-1.ca2604.1_arm64.deb Size: 117928 MD5sum: f928c9d2b6a894bc8168b25d5ed57cfe SHA1: 9a2cea2e55cb308730a5b58975cc0092067133bb SHA256: 69ae4908f9cb955de0b24c8ac6b13c0b5c04dba1d2093d3589567aa9ae842eed SHA512: 479b7f13bc9c7d6f61ef88402b973a5f846a543f907c58c980c40f80216e1718fdaef0962c2f48311655f7ac773be053cc0d972d572fd5a2f1c2e2ee22db1102 Homepage: https://cran.r-project.org/package=tdROC Description: CRAN Package 'tdROC' (Nonparametric Estimation of Time-Dependent ROC, Brier Score, andSurvival Difference from Right Censored Time-to-Event Data withor without Competing Risks) The tdROC package facilitates the estimation of time-dependent ROC (Receiver Operating Characteristic) curves and the Area Under the time-dependent ROC Curve (AUC) in the context of survival data, accommodating scenarios with right censored data and the option to account for competing risks. 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Package: r-cran-terrainmeshr Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2469 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2394278 MD5sum: 21bf034ea5a3a947387741badefb92a3 SHA1: 00fe0563274900d842ace30b82c5bcca8f4781d7 SHA256: 7bdc24bfa7ce6fc037ac1d24c095bab88ef96a48a032a8d1f68c4869bdb0cfc3 SHA512: f1e07c42eb726d8d1ab3de21eb9e62aa5c32f8a8eb829d19c7fb338b4df77f632f2682560475c3be5bfdf915bdd7778a89324d7fbda80571839931af4e32e12a 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: arm64 Version: 0.0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 309 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 173040 MD5sum: 792453139d2666551d7782a945dca93b SHA1: 7d760f8d958090b775ae8894c45cf95db0d5f3b9 SHA256: b855a67789781d54165536be235f74230ef62479b3f2e06ff1504410a826e01f SHA512: bb8eb84ceee8a667f5f14e7c3f85b381b5fa217884edd8a1d7abe2c3faf7c86f1c4663f00de69141be6c936db67b363a78e5482ee88579b394235fc6202d7cb5 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-testfordep Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 517 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-minerva, r-cran-hmisc, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-testfordep_0.2.0-1.ca2604.1_arm64.deb Size: 340286 MD5sum: 4e1cfeb6019c2f983792b40b2398f82e SHA1: c670789eaa1d9990486a9e84242a6a2a0c4540d4 SHA256: 7e28b569f1b485b1d678c90993580cff3046f9ad07e525c715992458c0296818 SHA512: 40693534ccfc1028f88ea4395938f9c0f51ca913548e809ef834d128c5b93a66c7d1819f6d59fbcb6be3a3c000d35fae69ccab31c2824e80a44008e9c167c9de Homepage: https://cran.r-project.org/package=testforDEP Description: CRAN Package 'testforDEP' (Dependence Tests for Two Variables) Provides test statistics, p-value, and confidence intervals based on 9 hypothesis tests for dependence. 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Package: r-cran-tetrascatt Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 207 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_arm64.deb Size: 71404 MD5sum: c0225583ef8abac8ba27cc8eec73977a SHA1: 78ba8c55579d2d2b4e20cc36d5b2d62875ea2057 SHA256: 3ee83936b4c4fc70ceb0fda7adc0018ed42d947a7dceb969e4db19257de84e3f SHA512: 233111f3883eabac4db54257c09b9cfb410cb24c611d475c7d9dd43b7cc67f76af1d30a295ebb7f73be3c187205c229dfb6f590bf43476130ae4c0d13226a66d Homepage: https://cran.r-project.org/package=tetrascatt Description: CRAN Package 'tetrascatt' (Acoustic Scattering for Complex Shapes by Using the DWBA) Uses the Distorted Wave Born Approximation (DWBA) to compute the acoustic backward scattering, the geometry of the object is formed by a volumetric mesh, composed of tetrahedrons. 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Package: r-cran-texexamrandomizer Architecture: arm64 Version: 1.2.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 880 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-assertthat, r-cran-stringr, r-cran-jsonlite Suggests: r-cran-optparse, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-texexamrandomizer_1.2.7-1.ca2604.1_arm64.deb Size: 330598 MD5sum: 2f748eda23ba05d7c095dff53eedb72d SHA1: 2a0a81f5aa9d2cb4188b95a26fe6e8b334f7dc32 SHA256: 79ee1b5831e8061a4714785d4867046e7750f4c579d2993d37e18b842c789757 SHA512: dcad4eebf467e6eb8d1eb114bdb760f075b0f76b77e80c4255091df6eb4237f6eeca6d8be235325e54d1101b57a5c5292bdd5425c66f993f021969c102c2cb9c Homepage: https://cran.r-project.org/package=TexExamRandomizer Description: CRAN Package 'TexExamRandomizer' (Personalizes and Randomizes Exams Written in 'LaTeX') Randomizing exams with 'LaTeX'. 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Package: r-cran-text.alignment Architecture: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1125 Depends: libc6 (>= 2.17), 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-markdown, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-text.alignment_0.1.5-1.ca2604.1_arm64.deb Size: 349862 MD5sum: 06562605b027a4d3f7c1832897671dfc SHA1: f46fb5f0468ae2102b030e2dae501d1491cc857e SHA256: 5d1016f23be876361d806064b4af82bc3ebda79751f9adfaec9cd8dc2425874c SHA512: b55b3c10936a95c73c475f1f335cc5bbd3b4f1513c4e2954b0a0305747bc4701399f7ad3537d997f1a81a521bf5ea95f25c6ca863c857a53a78d10254a84e65e Homepage: https://cran.r-project.org/package=text.alignment Description: CRAN Package 'text.alignment' (Text Alignment with Smith-Waterman) Find similarities between texts using the Smith-Waterman algorithm. The algorithm performs local sequence alignment and determines similar regions between two strings. The Smith-Waterman algorithm is explained in the paper: "Identification of common molecular subsequences" by T.F.Smith and M.S.Waterman (1981), available at . This package implements the same logic for sequences of words and letters instead of molecular sequences. 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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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For more information see (1) "kmeans++ the advantages of the k-means++ algorithm" by David Arthur and Sergei Vassilvitskii (2007), Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, pp. 1027-1035, and (2) "The Effectiveness of Lloyd-Type Methods for the k-Means Problem" by Rafail Ostrovsky, Yuval Rabani, Leonard J. Schulman and Chaitanya Swamy . Package: r-cran-tgp Architecture: arm64 Version: 2.4-23-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3523 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-maptree Suggests: r-cran-mass Filename: pool/dists/resolute/main/r-cran-tgp_2.4-23-1.ca2604.1_arm64.deb Size: 2955770 MD5sum: bef5a10ebb6bf47d51b769b6226ea10b SHA1: 22cb541150fd79b089eb6dc804f3c8986c8e3be2 SHA256: dbe018ea19fdaefcf6e21d219c6cf8de9bd2ae6316a233211140cc43307738de SHA512: 5aa445651374812398b28dd50a7f395ae21f78933c1bbfc82617a0300626d8b9b0261a10b4ea81f1251174d041d5b9598f6cba93886ebd6a45242daf6fb864d5 Homepage: https://cran.r-project.org/package=tgp Description: CRAN Package 'tgp' (Bayesian Treed Gaussian Process Models) Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes (GPs) with jumps to the limiting linear model (LLM). 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Package: r-cran-tgstat Architecture: arm64 Version: 2.3.32-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 351 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 11), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tgstat_2.3.32-1.ca2604.1_arm64.deb Size: 138164 MD5sum: 3e3991716d356dc504ae71ab677f903f SHA1: b10fa75d85e540d7bcbab9d90efb9f281dd48a56 SHA256: 781d462a5b221d96704f461cfaff360316ab8488ab0f258456b211eb8debb69e SHA512: 8af583993c59dc6258a519dff129a81711dcf0c31e1759c99c772a9df8d1c0516ed959667f07ea638bb563b76aafec7f032fb0a984ec213a2cd74941857e7f6a Homepage: https://cran.r-project.org/package=tgstat Description: CRAN Package 'tgstat' (Amos Tanay's Group High Performance Statistical Utilities) A collection of high performance utilities to compute distance, correlation, auto correlation, clustering and other tasks. 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Package: r-cran-tidylda Architecture: arm64 Version: 0.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1146 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_arm64.deb Size: 748552 MD5sum: 14a937da8256fe7121de739b765ce9aa SHA1: 66fcb1d271a7219609b1201bbcb264dc4198fe71 SHA256: febd28efb656af6ea2203510980a6ffec88818b2ca19ea3f4a40aaace704ae15 SHA512: 4c0ff8625448b1c3e80256930c35c0a7561ec5751c42144e6f9b9da0c9afaf6431900e33ee19b6b7813644b5fc3f143eaec40d6f335f2ff407a5479085f9e5f1 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. 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Package: r-cran-tidypopgen Architecture: arm64 Version: 0.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4218 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_arm64.deb Size: 2853018 MD5sum: 26d092d658fea18aaccc5106197c9878 SHA1: 3d4cca4634764e0fb62e322f60574efb5848f6a8 SHA256: 8e529a96641c7b76b93ee5bfd328f0759480a2ce89dec9fac5d05b22313757e2 SHA512: c185db5347c2da7ab2e6bc0fca2d332d84a2fc1e3f6bca094429a003f4ada9610b6d4dc7e87b9f02e3d47aa1faac2e8998ecace237cf36a3d3961c1ba04954bb Homepage: https://cran.r-project.org/package=tidypopgen Description: CRAN Package 'tidypopgen' (Tidy Population Genetics) We provide a tidy grammar of population genetics, facilitating the manipulation and analysis of data on biallelic single nucleotide polymorphisms (SNPs). 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Package: r-cran-timereg Architecture: arm64 Version: 2.0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1275 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-lava, r-cran-numderiv Suggests: r-cran-mets Filename: pool/dists/resolute/main/r-cran-timereg_2.0.7-1.ca2604.1_arm64.deb Size: 969024 MD5sum: 6af5596786740eba89b8df6e92d85b6a SHA1: 48ad99e32cb8168924988415a21cfb3c3177900e SHA256: c292f01190aa2467ede3cf4cd2e7cd5e21909a9b8a803083616671fac3188f0b SHA512: 6419037daebf25ae7c0d38c806b3c3220f10630d361f612100f44e6ed707d3f80052e4250ef1fae4288f34c87eeb994eaa7f70adcd780e803e78c252c1342fa6 Homepage: https://cran.r-project.org/package=timereg Description: CRAN Package 'timereg' (Flexible Regression Models for Survival Data) Programs for Martinussen and Scheike (2006), `Dynamic Regression Models for Survival Data', Springer Verlag. Plus more recent developments. Additive survival model, semiparametric proportional odds model, fast cumulative residuals, excess risk models and more. Flexible competing risks regression including GOF-tests. Two-stage frailty modelling. PLS for the additive risk model. Lasso in the 'ahaz' package. Package: r-cran-timetools Architecture: arm64 Version: 1.15.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 754 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_arm64.deb Size: 501954 MD5sum: 265433593fe1d81b7b9ce1c64288d927 SHA1: a332160d00850854d1dc138e97e81993360f60f1 SHA256: b5c7cb7f1705e92ebe8aaacd6643fa80a85caf28b29d46182ab6b18336d5def8 SHA512: cee66a5770385747b4950a8225678f57e047da9812e719e6990258b94752c636934cc11ccc8b57082d5062f3f951e5ef6685608cda99c93cbdcadcdb15d0b895 Homepage: https://cran.r-project.org/package=timetools Description: CRAN Package 'timetools' (Seasonal/Sequential (Instants/Durations, Even or not) TimeSeries) Objects to manipulate sequential and seasonal time series. Sequential time series based on time instants and time duration are handled. Both can be regularly or unevenly spaced (overlapping duration are allowed). Only POSIX* format are used for dates and times. The following classes are provided : 'POSIXcti', 'POSIXctp', 'TimeIntervalDataFrame', 'TimeInstantDataFrame', 'SubtimeDataFrame' ; methods to switch from a class to another and to modify the time support of series (hourly time series to daily time series for instance) are also defined. Tools provided can be used for instance to handle environmental monitoring data (not always produced on a regular time base). Package: r-cran-timp Architecture: arm64 Version: 1.13.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1087 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fields, r-cran-colorspace, r-cran-desolve, r-cran-gclus, r-cran-gplots, r-cran-minpack.lm, r-cran-nnls Filename: pool/dists/resolute/main/r-cran-timp_1.13.6-1.ca2604.1_arm64.deb Size: 896944 MD5sum: e67532c28f37e111aefaa9726237fd28 SHA1: c48caf09f6005b4fd1180b928a2ec3079819c5eb SHA256: 98ad7052a16ed58cbe5203b57cb3e7aa87f9dd44ed391595a1689820ef921fcd SHA512: 5393f9100e851efa7c1f43fd19267876e0013054224ece1b0f44b733d6bfe6197793b6e811584182422b323a0a38a1252cbc8b53df6c9d0aefad4938e619aae5 Homepage: https://cran.r-project.org/package=TIMP Description: CRAN Package 'TIMP' (Fitting Separable Nonlinear Models in Spectroscopy andMicroscopy) A problem solving environment (PSE) for fitting separable nonlinear models to measurements arising in physics and chemistry experiments, as described by Mullen & van Stokkum (2007) for its use in fitting time resolved spectroscopy data, and as described by Laptenok et al. 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Package: r-cran-timsac Architecture: arm64 Version: 1.3.8-6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 852 Depends: libc6 (>= 2.38), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-timsac_1.3.8-6-1.ca2604.1_arm64.deb Size: 619950 MD5sum: 0e7e19c36f353b1e10f47692f2efa60e SHA1: 953bd2b1e7e78e5f7bc4432af71bd0cbf0ca2485 SHA256: 062823c258aa8241978674e2931e7338ff200a1c0872c46dd8f07fbceaf99458 SHA512: 6ab95c4539d5f446290b05f35b57c3af32ccf0245842163593db74a7060dc17a89f9efe7f0e13d9c3d0a3c70540a71306aba8ae533fe97f9598d325111d30400 Homepage: https://cran.r-project.org/package=timsac Description: CRAN Package 'timsac' (Time Series Analysis and Control Package) Functions for statistical analysis, prediction and control of time series based mainly on Akaike and Nakagawa (1988) . Package: r-cran-tind Architecture: arm64 Version: 0.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1378 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-crayon, r-cran-testthat, r-cran-knitr, r-cran-rmarkdown, r-cran-fansi, r-cran-htmltools Filename: pool/dists/resolute/main/r-cran-tind_0.2.4-1.ca2604.1_arm64.deb Size: 834576 MD5sum: 256611297d5305cf444b4ea73b13814b SHA1: b72b5f2e30ea0dfeabef57a8ffb83d0fdd7b2133 SHA256: 00d72e986d7fa916918ccd4be27d500cf8af6a3731ceb9f0e4ee80116d3a8c15 SHA512: 89870625925161f0e51c23ee68be2518bb5309ed4b5e11f22e1ead10236fc0752f8a652d7ff0454eb14196d33d9792e4593ce5aa7bce0047e0aa3f4b712c6a64 Homepage: https://cran.r-project.org/package=tind Description: CRAN Package 'tind' (A Common Representation of Time Indices of Different Types) Provides an easy-to-use tind class representing time indices of different types (years, quarters, months, ISO 8601 weeks, dates, time of day, date-time, and arbitrary integer/numeric indices). 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Package: r-cran-tinflex Architecture: arm64 Version: 2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 200 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-runuran Filename: pool/dists/resolute/main/r-cran-tinflex_2.4-1.ca2604.1_arm64.deb Size: 110622 MD5sum: f4ede8cab09b920d8d62869ce8f697e3 SHA1: e451a32ab5a896c097d27d328d6d7d6dd537fd5d SHA256: 618251c7c5ef7cea5555972662738f7b737d9c2ed867a7ca1723aef99f5b90b4 SHA512: 33c91a0f6cdce88aafe8c7c85fa54685b34ade0d5001de69becc78931ad6293ff4c938a6da4c2da3238f0a925b51682e11a4f02b8795bbc1dd3a7c7963eb4cbc Homepage: https://cran.r-project.org/package=Tinflex Description: CRAN Package 'Tinflex' (A Universal Non-Uniform Random Number Generator) A universal non-uniform random number generator for quite arbitrary distributions with piecewise twice differentiable densities. Package: r-cran-tinycodet Architecture: arm64 Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1235 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-stringi Suggests: r-cran-tinytest, r-cran-ggplot2, r-cran-mgcv, r-cran-nlme, r-cran-collapse, r-cran-kit, r-cran-knitr, r-cran-rmarkdown, r-cran-roxygen2 Filename: pool/dists/resolute/main/r-cran-tinycodet_0.6.2-1.ca2604.1_arm64.deb Size: 433694 MD5sum: e23f3512db429ec5e0b1296f3e442a27 SHA1: 59319b829e244758c418960ac59e7ea5d572a667 SHA256: 4685175a783affa09c93146e4f868ad40e27f42023e8b15b74947e9626a1071a SHA512: 504e61b677eb3004d1f7855b266c57286d3ca81487d66a9291f99af6ee83236dfc0f652bc0703ec0e9bbb5f3c9fc86ed6ec7707b3272b93c51a6da715797b087 Homepage: https://cran.r-project.org/package=tinycodet Description: CRAN Package 'tinycodet' (Functions to Help in your Coding Etiquette) Adds some functions to help in your coding etiquette. 'tinycodet' primarily focuses on 4 aspects. 1) Safer decimal (in)equality testing, standard-evaluated alternatives to with() and aes(), and other functions for safer coding. 2) A new package import system, that attempts to combine the benefits of using a package without attaching it, with the benefits of attaching a package. 3) Extending the string manipulation capabilities of the 'stringi' R package. 4) Reducing repetitive code. Besides linking to 'Rcpp', 'tinycodet' has only one other dependency, namely 'stringi'. Package: r-cran-tinyimg Architecture: arm64 Version: 0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1539 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_arm64.deb Size: 641798 MD5sum: 26f39d54012a47b8ecbfb0d3cfe37f03 SHA1: 58a15c4ea58790ce67236690b1d3b7bc90555c0e SHA256: a51989ca0465d43d49447196412416564a86232a453eb17aa91ad7882b5a47b3 SHA512: ab20efd07f2a9b397234b8542a53567787cc2dac48c9141c6bb40edbb12a642aa028500646e19bb954585750a7394dc8b750012b2fe4747d610d2ee5597ae779 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-tipitaka.critical Architecture: arm64 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_arm64.deb Size: 5123274 MD5sum: 9f5006f46ebac90aeff072117e91f1fe SHA1: 24d48e76a81785e81a392a75779834714189bdc3 SHA256: 2120186ece967b723d8492e96678ad2376eaa0fbe06b9c9119e4b9fb444796e1 SHA512: 3bde0fbe49293b8a7367aa3a8470c3cdf88db4d9a4250dd6c6c7acf996ff55e7ffe207544e90c02d80e888ca2a1f7b4fd73d84e7370f55fafd7c877d6c7a7f7a Homepage: https://cran.r-project.org/package=tipitaka.critical Description: CRAN Package 'tipitaka.critical' (Lemmatized Critical Edition of the Pali Canon) A lemmatized critical edition of the complete Pali Canon (Tipitaka), the canonical scripture of Theravadin Buddhism. Based on a five-witness collation of the Pali Text Society (PTS) edition (via 'GRETIL'), 'SuttaCentral', the Vipassana Research Institute (VRI) Chattha Sangayana edition, the Buddha Jayanti Tipitaka (BJT), and the Thai Royal Edition. All text is lemmatized using the 'Digital Pali Dictionary', grouping inflected forms by dictionary headword. Covers all three pitakas (Sutta, Vinaya, Abhidhamma) with 5,777 individual text units. The companion package 'tipitaka' provides the original VRI edition data and Pali text tools. For background on the collation method, see Zigmond (2026) . Package: r-cran-tipitaka Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3114 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-stringr, r-cran-cpp11 Suggests: r-cran-dplyr, r-cran-magrittr, r-cran-stringi Filename: pool/dists/resolute/main/r-cran-tipitaka_1.0.0-1.ca2604.1_arm64.deb Size: 3057990 MD5sum: decc037b082195d1089bb4afe472976f SHA1: 2078c5d3deb05ad4c75bf1e160069e05249a2525 SHA256: b5157caecf21c4644c6025e608ee043a968f1a8d258e82f3d948b303c2c23bf4 SHA512: f078dfc7d72ed8eb96f12559f582e0a66562d3577193e43024a29071c3c4e75db99347962560586cffd4f8da0a06abb6bbfe98e0286378969e81aa9067ea9cbf Homepage: https://cran.r-project.org/package=tipitaka Description: CRAN Package 'tipitaka' (Data and Tools for Analyzing the Pali Canon) Provides access to the complete Pali Canon, or Tipitaka, the canonical scripture for Theravadin Buddhists worldwide. Based on the Chattha Sangayana Tipitaka version 4 (Vipassana Research Institute, 1990). Includes word frequency data and tools for Pali string sorting. For a lemmatized critical edition with sutta-level granularity, see the companion package 'tipitaka.critical'. Package: r-cran-tips Architecture: arm64 Version: 1.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2065 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1017588 MD5sum: 28a37ba5096bd04ecedeb9786cb67cda SHA1: 05c9d411254d4421c7d151e71c4f033e85b4884c SHA256: 0b726ee395123911b00f70278be562fc5cb61a70d6a18913cd05628424d986c3 SHA512: 49164b24c27370d896c0b5ce8a51d6a107de1f7a47a8f449fa9a1e5048a188524536dace1d183bb06df954a683e144ff65cd3a0a7dc279b408c046528e3cf3a4 Homepage: https://cran.r-project.org/package=TiPS Description: CRAN Package 'TiPS' (Trajectories and Phylogenies Simulator) Generates stochastic time series and genealogies associated with a population dynamics model. Times series are simulated using the Gillespie exact and approximate algorithms and a new algorithm we introduce that uses both approaches to optimize the time execution of the simulations. Genealogies are simulated from a trajectory using a backwards-in-time based approach. Methods are described in Danesh G et al (2022) . Package: r-cran-tipsae Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6159 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-shiny, r-cran-rcpp, r-cran-rstan, r-cran-ggplot2, r-cran-nlme, r-cran-sp, r-cran-ggpubr, r-cran-rdpack, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-rstantools, r-cran-callr, r-cran-sf, r-cran-dplyr, r-cran-leaflet, r-cran-tmap, r-cran-spam, r-cran-spdep, r-cran-gridextra, r-cran-r.rsp, r-cran-shinythemes, r-cran-shinyfeedback, r-cran-shinybusy, r-cran-shinywidgets, r-cran-shinyjs, r-cran-bayesplot, r-cran-dt, r-cran-loo Filename: pool/dists/resolute/main/r-cran-tipsae_1.0.3-1.ca2604.1_arm64.deb Size: 4103306 MD5sum: 7ffc29ae1357d93c224ae0bfd03943f2 SHA1: fcb7df885d0f33b2467800bfac177ae89e6a616a SHA256: bd64a87f73ac151f4f535a7a401946abe8e80c214228b54c7fec9fe859022091 SHA512: 12ce277679674717c8d53e64530b18075cba86565651ad58349bed40287f8c9ccb0bde68bad95d8986e5eef6a0da19ed3a81f9eed5cc9132283f8b6c282ad05f Homepage: https://cran.r-project.org/package=tipsae Description: CRAN Package 'tipsae' (Tools for Handling Indices and Proportions in Small AreaEstimation) It allows for mapping proportions and indicators defined on the unit interval. It implements Beta-based small area methods comprising the classical Beta regression models, the Flexible Beta model and Zero and/or One Inflated extensions (Janicki 2020 ). Such methods, developed within a Bayesian framework through Stan , come equipped with a set of diagnostics and complementary tools, visualizing and exporting functions. A Shiny application with a user-friendly interface can be launched to further simplify the process. For further details, refer to De Nicolò and Gardini (2024 ). 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Package: r-cran-tlrmvnmvt Architecture: arm64 Version: 1.1.2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 398 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), liblapack3 | liblapack.so.3, libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-rcppeigen, r-cran-bh Suggests: r-cran-mvtnorm Filename: pool/dists/resolute/main/r-cran-tlrmvnmvt_1.1.2.1-1.ca2604.1_arm64.deb Size: 167734 MD5sum: 0edb22211eb28fee173bb558edaee2a5 SHA1: 50b7715b6b353cdecefd372981dd8a93b3a41ce8 SHA256: 005e47cdbe1eb38d3fba51947e67f137366cbf90ca3bfcb6e6d4463716ff2cd1 SHA512: 5ed4b8e3392869ccf4c0f5b16d17e4b16732c2a272d5532f3bbba1f840868a57ae129f9f38b201077c5cc1881acd365315aec8a1f256e72a6b539422db390887 Homepage: https://cran.r-project.org/package=tlrmvnmvt Description: CRAN Package 'tlrmvnmvt' (Low-Rank Methods for MVN and MVT Probabilities) Implementation of the classic Genz algorithm and a novel tile-low-rank algorithm for computing relatively high-dimensional multivariate normal (MVN) and Student-t (MVT) probabilities. References used for this package: Foley, James, Andries van Dam, Steven Feiner, and John Hughes. "Computer Graphics: Principle and Practice". Addison-Wesley Publishing Company. Reading, Massachusetts (1987, ISBN:0-201-84840-6 1); Genz, A., "Numerical computation of multivariate normal probabilities," Journal of Computational and Graphical Statistics, 1, 141-149 (1992) ; Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. "Exploiting Low Rank Covariance Structures for Computing High-Dimensional Normal and Student- t Probabilities," Statistics and Computing, 31.1, 1-16 (2021) ; Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. "tlrmvnmvt: Computing High-Dimensional Multivariate Normal and Student-t Probabilities with Low-Rank Methods in R," Journal of Statistical Software, 101.4, 1-25 (2022) . 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Package: r-cran-tmbstan Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1356 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_arm64.deb Size: 500256 MD5sum: 9677f94a3393993fa86510960d11e63e SHA1: c008331c318c19a1a504405a59145d364b1a7115 SHA256: 3b285429379697695f9ebb0446b89a41344fbd55c477f860db4bb17712e1efa7 SHA512: 6e1c16aa432493f784139165e8ec81c1064093b46087b80420b8a8c9c18942af7eda0142fb309d01b8027e23c6ccd47d59ef80ac48b66919c41d084bb1c7d269 Homepage: https://cran.r-project.org/package=tmbstan Description: CRAN Package 'tmbstan' (MCMC Sampling from 'TMB' Model Object using 'Stan') Enables all 'rstan' functionality for a 'TMB' model object, in particular MCMC sampling and chain visualization. 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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. 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The methods implemented here are described in Mogensen and Markussen (2021) . Package: r-cran-tmvnsim Architecture: arm64 Version: 1.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 115 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_arm64.deb Size: 20292 MD5sum: eb7b0c375ed6ddb64914b1b4f99abd7b SHA1: eb3f82e2aa190e6ce470e0920fb6fa9dbc6e6716 SHA256: a7f9dbaef3b5ee2294792665670c6fc8ae7168438b669a64e68d5a63ca48ba24 SHA512: 65fb106fcd276ad519d85943c9bf83f96c0d0fd452a294f64d08de5cf81077032f38e4403748cec1ca3b829622fbc102b4d20556471f45a720ffc9de0ae590c4 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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'Tracer' () is a GUI tool to parse and analyze the files generated by 'BEAST2'. This package provides a way to parse and analyze 'BEAST2' input files without active user input, but using R function calls instead. Package: r-cran-trackdem Architecture: arm64 Version: 0.7.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1033 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-png, r-cran-neuralnet, r-cran-raster, r-cran-rcpp, r-cran-mass, r-cran-shiny, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-trackdem_0.7.2-1.ca2604.1_arm64.deb Size: 593860 MD5sum: a1037acbaba2579423da231ca75d5b3d SHA1: 8ee6862daf76597962b0a120fe803b9fee380acf SHA256: cdb46ed671242c211edace12bc4295bab6700a495a396d5176220292f0a819c3 SHA512: 06e5367830f3cace5075d339a827a3ad535488d641ef4bcc7ec3ad3d01a79f78f5cb254635ece807cbe675589eb7d8f4de6f5009c4d1d99dcc87c0cc511a9b80 Homepage: https://cran.r-project.org/package=trackdem Description: CRAN Package 'trackdem' (Particle Tracking and Demography) Obtain population density and body size structure, using video material or image sequences as input. Functions assist in the creation of image sequences from videos, background detection and subtraction, particle identification and tracking. An artificial neural network can be trained for noise filtering. The goal is to supply accurate estimates of population size, structure and/or individual behavior, for use in evolutionary and ecological studies. Package: r-cran-trajer Architecture: arm64 Version: 0.11.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2754 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-minpack.lm, r-cran-numderiv, r-cran-ucminf, r-cran-mass, r-cran-capushe, r-cran-rcpparmadillo Suggests: r-cran-spelling Filename: pool/dists/resolute/main/r-cran-trajer_0.11.1-1.ca2604.1_arm64.deb Size: 1239136 MD5sum: 6978b21abaaa3b2d93ed32aea009c723 SHA1: 57d52005885ee2c848149f080d7722f4d59a927a SHA256: b9cb7842d0a3aa5302d29450995f9f13df05ca9753d774d4db08644f247785b6 SHA512: 4d6f44eec5f6ae2baa0e2616fe10ade74e0e8254ca4a0a3413deb862da5a55b28db3e71181fa1e050e9cb16c3caed9a48547d15820ec53149ee4efefbcd74d88 Homepage: https://cran.r-project.org/package=trajeR Description: CRAN Package 'trajeR' (Group Based Modeling Trajectory) Estimation of group-based trajectory models, including finite mixture models for longitudinal data, supporting censored normal, zero-inflated Poisson, logit, and beta distributions, using expectation-maximization and quasi-Newton methods, with tools for model selection, diagnostics, and visualization of latent trajectory groups, , Nagin, D. (2005). Group-Based Modeling of Development. Cambridge, MA: Harvard University Press. and Noel (2022), , thesis. Package: r-cran-traminer Architecture: arm64 Version: 2.2-13-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2720 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cluster, r-cran-colorspace, r-cran-rcolorbrewer, r-cran-boot, r-cran-vegan Suggests: r-cran-xtable, r-cran-traminerextras, r-cran-weightedcluster Filename: pool/dists/resolute/main/r-cran-traminer_2.2-13-1.ca2604.1_arm64.deb Size: 2229616 MD5sum: 26320364310fcb85a7673d3e3e5980e9 SHA1: 72427bf826e0f5d4492113c9384fb843dac40d11 SHA256: 7ad6af06f989bec234467c91520e45c47766ced01801b0b5516a42e8d810a0b5 SHA512: 29c0bf373c3eb22928d68394e8267be32fb566403e944812917e033f7bed6b6bc4a09e52da213c22e8f90980a29d489ca6e5823b06908d2bc298473a4e412496 Homepage: https://cran.r-project.org/package=TraMineR Description: CRAN Package 'TraMineR' (Trajectory Miner: a Sequence Analysis Toolkit) Set of sequence analysis tools for manipulating, describing and rendering categorical sequences, and more generally mining sequence data in the field of social sciences. Although this sequence analysis package is primarily intended for state or event sequences that describe time use or life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and functions for extracting the most frequent event subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page. Package: r-cran-traminerextras Architecture: arm64 Version: 0.6.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-cluster, r-cran-rcolorbrewer, r-cran-colorspace, r-cran-doparallel, r-cran-foreach Filename: pool/dists/resolute/main/r-cran-traminerextras_0.6.8-1.ca2604.1_arm64.deb Size: 412466 MD5sum: 35cdb4bacc92639d0081a07adc2f16bc SHA1: 3a0d8ffed44a1aa21efc288eb5f5712245fffd11 SHA256: f1c1c3c29111ec4124ba154c5f0ead608d1a31ce4d1636f7c4392cb95201883e SHA512: b77324bd5035ba29fd3290f29ac8b50c024399b5ab5865f804ce735d0f12af6d2a6ae7b304b1f64bdc062d083e34affee63a0b20e8636544e16138b0a2f12ae8 Homepage: https://cran.r-project.org/package=TraMineRextras Description: CRAN Package 'TraMineRextras' (TraMineR Extension) Collection of ancillary functions and utilities to be used in conjunction with the 'TraMineR' package for sequence data exploration. Includes, among others, specific functions such as state survival plots, position-wise group-typical states, dynamic sequence indicators, and dissimilarities between event sequences. Also includes contributions by non-members of the TraMineR team such as methods for polyadic data and for the comparison of groups of sequences. Package: r-cran-tramme Architecture: arm64 Version: 1.0.8-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4099 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_arm64.deb Size: 3124382 MD5sum: b1d0ded81cc8033e69a5847b39f2dd20 SHA1: 56058faf61ae257274dc9c7a555dc3b2e100341a SHA256: 3298754389c001e2af0b002a1c253ef9412e2a12681db4f125a65d32b6c1ee83 SHA512: 39ab9658479d62ea67e2aa2cf128029ed9ac6ad81fa78ea390a035bd1a5e4dd2b9509c7f90d842772bfaa74caa752cd18cc7c5ec6cd63d982c3fd16110147f3e 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) . The technical details of transformation models are given in Hothorn et al. (2018) . Likelihood contributions of exact, randomly censored (left, right, interval) and truncated observations are supported. The random effects are assumed to be normally distributed on the scale of the transformation function, the marginal likelihood is evaluated using the Laplace approximation, and the gradients are calculated with automatic differentiation (Tamasi & Hothorn, 2021) . Penalized smooth shift terms can be defined using the 'mgcv' notation. Additive mixed-effects transformation models are described in Tamasi (2025) . Package: r-cran-transformr Architecture: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 283 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-tweenr, r-cran-rlang, r-cran-sf, r-cran-lpsolve, r-cran-vctrs, r-cran-cpp11 Suggests: r-cran-covr, r-cran-magrittr Filename: pool/dists/resolute/main/r-cran-transformr_0.1.5-1.ca2604.1_arm64.deb Size: 161596 MD5sum: 384c4f5138c60c4cd8bb13e7ee4ff820 SHA1: 86b2828f93cc7bae7bb312c1717418a2fa8a61c5 SHA256: c780dac56cfc292069f7ddd2a41bb874445bcaafe1b6ea578670fbe6a0ccd846 SHA512: 8de3d01abb80a4591ddcc74a04b8095ca3fba0a7908d39c3797fcd93e43b970e902f815755e6d9174f9431474bf0fcaf3cf950438d52478779e66ebcade3160b Homepage: https://cran.r-project.org/package=transformr Description: CRAN Package 'transformr' (Polygon and Path Transformations) In order to smoothly animate the transformation of polygons and paths, many aspects needs to be taken into account, such as differing number of control points, changing center of rotation, etc. The 'transformr' package provides an extensive framework for manipulating the shapes of polygons and paths and can be seen as the spatial brother to the 'tweenr' package. Package: r-cran-transfr Architecture: arm64 Version: 1.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2744 Depends: libc6 (>= 2.39), libgfortran5 (>= 10), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-stars, r-cran-sf, r-cran-doparallel, r-cran-foreach, r-cran-units, r-cran-glmnet, r-cran-rdpack Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-elevatr, r-cran-progress, r-cran-whitebox Filename: pool/dists/resolute/main/r-cran-transfr_1.1.4-1.ca2604.1_arm64.deb Size: 2481664 MD5sum: d8d2947b7be9dee3d239b2792d7076b2 SHA1: 1c5bae34c40a0152c69878ef007348588f68659b SHA256: 32ec33282dd3877a5d2759e2b14273aec88f263b483b6ef330bc7bd55834e1ec SHA512: f72513222b9cd97fdca5c0566161dd54b0b4e00bf7a40e42715320099a4bebd7693b6e6e4ceecd6f0a640b93b813b9738f8eed18b2ea0f43099dce6fed725113 Homepage: https://cran.r-project.org/package=transfR Description: CRAN Package 'transfR' (Transfer of Hydrograph from Gauged to Ungauged Catchments) A geomorphology-based hydrological modelling for transferring streamflow measurements from gauged to ungauged catchments. 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: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 401 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 176232 MD5sum: 68876d377c0db54d28310570d5e20cd6 SHA1: d9d140afb87aa3b046186cddbc48650cf8517e5a SHA256: 37b278516a402ca3b8470180fe13d371ad765e832a37320c8989450fc9ccb368 SHA512: 7d9bc9a4bd22828ae71c136be4d1898febaa6353125baf1b8f86a4672c5a615e1a6e8ff92b19f6547da40a6b53ec3f59945cb849de0df608d173f6d474880849 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: arm64 Version: 0.1.0-1.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-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_arm64.deb Size: 96300 MD5sum: 563f36968cfa644d55bf56c11a919660 SHA1: c1c1ccaabc0e4d6f7c8e5b2ec02516b7d804c3b3 SHA256: 3286a2862f1b6961ca5a3c122a727d28df24e35b8bc5a774a80d04cb4edb7eee SHA512: cca98f5512c98d42745333700dbe4a810914297fee260f9fe3e198041cd731347065292442e86a39b7d170a65e76b0444755e160e3b29ec891495bc1c9a15a6e 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: arm64 Version: 0.15-4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1004 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_arm64.deb Size: 654276 MD5sum: a5e822c9cc59aa44f7c827e746f7c54e SHA1: e766a700d476ed756af25aa39bed245b91919141 SHA256: 29a265a88cd441ec450121b5cb0d985d29d9c4b5ee2ee8482a907b2b960fe42f SHA512: 661a0b48f1535bac75a2df666e95110feb438fddd19ad44a08f627772cd76cca40628060457de08b9704572cda654bae91b1c4af3c939ba759e9bd549b1c59cc 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: arm64 Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 341 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rootsolve, r-cran-truncsp, r-cran-survival, r-cran-squarem Suggests: r-cran-mass, r-cran-boot Filename: pool/dists/resolute/main/r-cran-transurv_1.2.4-1.ca2604.1_arm64.deb Size: 177950 MD5sum: e10d2e19a5dcaa15e00b7187c5bd224f SHA1: c79464ddb834dea8ac9b1064fda77d75c003ec0c SHA256: df54637e1ff31c2b5e1830e8ed1bceebbd47a0e8f8c5d8abfd63bc7c12cb5df2 SHA512: fb0718a341498e1fceac3bcd26a641340c4df563c25b2e4b473aea310d363ae516f556134d67f4fc86573d492fc717d285f4e1afabbe4d8a4172f00253e294e8 Homepage: https://cran.r-project.org/package=tranSurv Description: CRAN Package 'tranSurv' (Transformation-Based Regression under Dependent Truncation) A latent, quasi-independent truncation time is assumed to be linked with the observed dependent truncation time, the event time, and an unknown transformation parameter via a structural transformation model. The transformation parameter is chosen to minimize the conditional Kendall's tau (Martin and Betensky, 2005) or the regression coefficient estimates (Jones and Crowley, 1992) . The marginal distribution for the truncation time and the event time are completely left unspecified. The methodology is applied to survival curve estimation and regression analysis. Package: r-cran-trapezoid Architecture: arm64 Version: 2.0-2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 276 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_arm64.deb Size: 164968 MD5sum: b7333c207793fb32e7b82539e6c81143 SHA1: e193c4ebfee60fa074fd098a740a320d391f97c5 SHA256: 438dce6d57c1e8c004297609fb20cbf1b2d628a206e80e0e9d3dedb1f32e1168 SHA512: 22f75e7ab22cae7edef31828d8652b653a3b6fc60772022c61676786328226dd5ec89b8fc3f439fa4a3532966ffa9c4886fb0e4584e1f499f90f601fa813f850 Homepage: https://cran.r-project.org/package=trapezoid Description: CRAN Package 'trapezoid' (The Trapezoidal Distribution) The trapezoid package provides 'dtrapezoid', 'ptrapezoid', 'qtrapezoid', and 'rtrapezoid' functions for the trapezoidal distribution. Package: r-cran-trc Architecture: arm64 Version: 0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 134 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-trc_0.2-1.ca2604.1_arm64.deb Size: 42188 MD5sum: 998a95ea3315b0c662e76bc6e9bf96c5 SHA1: 9e4c6dfbc083c59d10ec8cfbb9bef18ea94c9d4a SHA256: ed71646521854ddbc605044b84e3f6535b581522b4a9f0d38fa7fb407ac24280 SHA512: 046918823a9de665e53eaf478950a3ba98dfef2eda4d427708f11df4fb4124d4d1af26dfdaff37dcee0530979b6c165cb49b9a3d65a795744bef087a7302a28a Homepage: https://cran.r-project.org/package=trc Description: CRAN Package 'trc' (Truncated Rank Correlation) A new measure of similarity between a pair of mass spectrometry (MS) experiments, called truncated rank correlation (TRC). To provide a robust metric of similarity in noisy high-dimensional data, TRC uses truncated top ranks (or top m-ranks) for calculating correlation. Truncated rank correlation as a robust measure of test-retest reliability in mass spectrometry data. For more details see Lim et al. (2019) . Package: r-cran-treats Architecture: arm64 Version: 1.1.6-1.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_arm64.deb Size: 305750 MD5sum: 30ca10eafcc556682a3b5c2430baf400 SHA1: 6ac75370474547852a606569abbe6891aa134c8a SHA256: d7dd0e019a4d2ffd750e850052a3b85be9d8038d54475c356df487771740c8c9 SHA512: b4c31f18d354b8a97ca16a3e6e9fb1f0848faf9e6832c6924ac70a44fa38cc26378f819ac0bd12491c9fef5c5bc1f176ac1b3c383a71c0af28f3b95e6be34d62 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. Trees can be simulated using modular birth-death parameters (e.g. changing starting parameters or algorithm rules). Traits can be simulated in any way designed by the user. The growth of the tree and the traits can influence each other through modifiers objects providing rules for affecting each other. Finally, events can be created to modify both the tree and the traits under specific conditions ( Guillerme, 2024 ). Package: r-cran-tree.interpreter Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 308 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 111344 MD5sum: 37c7bf5c945d6a57cb7aa9eab0d1f4f7 SHA1: 270412344e3138efe8f8ae49012b2ba40d7f4e93 SHA256: 7bfd6c99b8fec26b160d0b0c18504ee6fa215d23229ce171785a13c7035c6e41 SHA512: 8bc48d5099b0ad4ad91ee72abb5217aec0e211b4d55ee115a20d7488c2e8159a0dee14b1046b02bac5155e797796c64aa6cd51935bbce135bc4f8cc042ed2812 Homepage: https://cran.r-project.org/package=tree.interpreter Description: CRAN Package 'tree.interpreter' (Random Forest Prediction Decomposition and Feature ImportanceMeasure) An R re-implementation of the 'treeinterpreter' package on PyPI . Each prediction can be decomposed as 'prediction = bias + feature_1_contribution + ... + feature_n_contribution'. This decomposition is then used to calculate the Mean Decrease Impurity (MDI) and Mean Decrease Impurity using out-of-bag samples (MDI-oob) feature importance measures based on the work of Li et al. (2019) . Package: r-cran-tree Architecture: arm64 Version: 1.0-45-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 246 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-mass, r-cran-islr2 Filename: pool/dists/resolute/main/r-cran-tree_1.0-45-1.ca2604.1_arm64.deb Size: 152712 MD5sum: def2ec3a3a034756ac8f73a05c08db45 SHA1: 8f15cb59b4fea0b5d54aef1a7e394938497583a0 SHA256: ca0c03da3a25f2fd32a1d8e7dd711b5b0f7fe4d083ce48afa75ccd74467cb421 SHA512: 54fcb342045625ed53ace47c45f964b317beafa74087eea618aa89e5c7fbd52d95270912623009ccdd7b4cc63d8f769f99637e570f484da6b7248a9a6f0940df Homepage: https://cran.r-project.org/package=tree Description: CRAN Package 'tree' (Classification and Regression Trees) Classification and regression trees. Package: r-cran-treebugs Architecture: arm64 Version: 1.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1643 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_arm64.deb Size: 1275786 MD5sum: 6d8de002c90eea709aef86bf58b65111 SHA1: bc55abddb52a9cb0a03f1faaa2a0ae00c0031851 SHA256: 5fd715369438661c6df5a5ae7e161e87dde694bf2b73ba0587e5042cd50c3d72 SHA512: 74bae138b8ae3a4adeb3abdc4e9cc1905787966d8dc3b4a638b5296a8ff149f423913a04a67deae4efeccd9dd4d77240cf514eabc30ad79e3f6cb758a410555a Homepage: https://cran.r-project.org/package=TreeBUGS Description: CRAN Package 'TreeBUGS' (Hierarchical Multinomial Processing Tree Modeling) User-friendly analysis of hierarchical multinomial processing tree (MPT) models that are often used in cognitive psychology. Implements the latent-trait MPT approach (Klauer, 2010) and the beta-MPT approach (Smith & Batchelder, 2010) to model heterogeneity of participants. MPT models are conveniently specified by an .eqn-file as used by other MPT software and data are provided by a .csv-file or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides tests of heterogeneity and MPT-tailored summaries and plotting functions. A detailed documentation is available in Heck, Arnold, & Arnold (2018) and a tutorial on MPT modeling can be found in Schmidt, Erdfelder, & Heck (2023) . Package: r-cran-treeclim Architecture: arm64 Version: 2.0.7.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 547 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 334422 MD5sum: bd19c44f6ba5388620e9e2dd44d38d23 SHA1: 41bcddb402ebd41786fb1ef95a9d36492f4391cf SHA256: 813b0b715df12863920200a29b60ac35eca6cd336b1ab4a18784faa04e68d8cf SHA512: 73efcf8e688fc4e6e3df0c4966a13ef04cbf0a59ecf8f804ea938c19d2f9d337a9f60e0f87b93f1d6b04d8e725afead48267f22e3216fab1b6c893fa7af16d0d Homepage: https://cran.r-project.org/package=treeclim Description: CRAN Package 'treeclim' (Numerical Calibration of Proxy-Climate Relationships) Bootstrapped response and correlation functions, seasonal correlations and evaluation of reconstruction skills for use in dendroclimatology and dendroecology, see Zang and Biondi (2015) . Package: r-cran-treedimensiontest Architecture: arm64 Version: 0.0.2-1.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 (>= 4.5.0), 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_arm64.deb Size: 1572784 MD5sum: 814a31cd3b902c134c44b466defd69c8 SHA1: c302db9850eebd545a7c0b6d149878eab711145c SHA256: bbc0e7db75b66790af8b185889f137c66b01d642d4a5619253fbecb985e6d85e SHA512: 81fa8cae2eb27a389686f7f9f6f90119175bdf238d121d3da36109410a741373e13dd6a43413faa9f15379a647badcd61f27930ff34174db7a29cd3992a00e1d Homepage: https://cran.r-project.org/package=TreeDimensionTest Description: CRAN Package 'TreeDimensionTest' (Trajectory Presence and Heterogeneity in Multivariate Data) Testing for trajectory presence and heterogeneity on multivariate data. Two statistical methods (Tenha & Song 2022) are implemented. The tree dimension test quantifies the statistical evidence for trajectory presence. The subset specificity measure summarizes pattern heterogeneity using the minimum subtree cover. There is no user tunable parameters for either method. Examples are included to illustrate how to use the methods on single-cell data for studying gene and pathway expression dynamics and pathway expression specificity. Package: r-cran-treedist Architecture: arm64 Version: 2.14.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2637 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_arm64.deb Size: 1419212 MD5sum: ac6b0787a63061fb4e6e6da362b3594a SHA1: d37985b8ac0fc46bab4bbbbc8e83b9816c6e8995 SHA256: 53fd587aca5cd5bca55da99a0812a5e7bcd9ea06a5460ba69861232ef06a3a4e SHA512: 75307deca46b7261f9047d80afdaf0db1ba5852a1d7de84abed6f2cb8128c3a499a639d71a5df254005b3c6c75e457168a9b22d0f7f28245efa923255fb1b00c 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: arm64 Version: 1.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4866 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2486238 MD5sum: cc320e03b135ad4ceed1be14d3f2d5f4 SHA1: 9de0ea0f890c5062f7a2c7ebb730c004869692e3 SHA256: 89fd6112c938add038f209296f7a564dc2ec1702014f2527d0e0b55657c6a032 SHA512: 4aba43338dd976c093a5eb14dccfe427b7cb7489f755acd1b3238a5acc2dbb6fec61dac78ed92c71f725307a5733277bcb939a92fb9b680438b83269749ffa86 Homepage: https://cran.r-project.org/package=TreeSearch Description: CRAN Package 'TreeSearch' (Phylogenetic Analysis with Discrete Character Data) Reconstruct phylogenetic trees from discrete data. Inapplicable character states are handled using the algorithm of Brazeau, Guillerme and Smith (2019) with the "Morphy" library, under equal or implied step weights. Contains a "shiny" user interface for interactive tree search and exploration of results, including character visualization, rogue taxon detection, tree space mapping, and cluster consensus trees (Smith 2022a, b) , . Profile Parsimony (Faith and Trueman, 2001) , Successive Approximations (Farris, 1969) and custom optimality criteria are implemented. Package: r-cran-treeshap Architecture: arm64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1384 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1203832 MD5sum: 60992ce0a96668f515aa98c5b4443f4a SHA1: fa38f44c1f03774ea3bf6dd3f32d5b35d05b37ec SHA256: f72584e8685a13a640b8b84910066743ad930c85a5336aa64ae1de6b58039393 SHA512: 3883c24f199d7cbfcfdd712e34407e77c5ce0e32029414f899735a1f93eed4772aa243d439c162b492ba15387819291e4977cc9a13fb1981f3fed24432766263 Homepage: https://cran.r-project.org/package=treeshap Description: CRAN Package 'treeshap' (Compute SHAP Values for Your Tree-Based Models Using the'TreeSHAP' Algorithm) An efficient implementation of the 'TreeSHAP' algorithm introduced by Lundberg et al., (2020) . It is capable of calculating SHAP (SHapley Additive exPlanations) values for tree-based models in polynomial time. Currently supported models include 'gbm', 'randomForest', 'ranger', 'xgboost', 'lightgbm'. Package: r-cran-treesitter.c Architecture: arm64 Version: 0.0.4.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 947 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_arm64.deb Size: 175658 MD5sum: d0a0f1bdcf56e3a1aceef93ad6b3df8c SHA1: b9fde436c53319c67e84d4e9cf6826546ad30575 SHA256: 52b8da9546ba11df3c5fe503255a1dd165bc41ceaaf5bec45d3179f96373badf SHA512: 259eb1c10d4a1d14e59b08d07ed2ca09e43a89b278ed2c35f4e41a336a327f3315fe43c430807aeb2d6483eb7ae0c9c0e95ac54725e1a70abceacec872a76c54 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: arm64 Version: 1.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 574 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 52580 MD5sum: 3cb6e898424f35185fe24064803c082b SHA1: 984feb5c5c0d7dfeac540e260faefcb1c2740410 SHA256: 71a6a88248fb047db712fe06116a6c6aeba02550ea2b46784ea2ee10049fe344 SHA512: a11fe8d3d0a21fa8079b3e277edcab2b7b61fa4348a221cd62b3ad638422725b8e971848ed85e03500b4751b30b2ef2eceb30ff45aca9ccbcf0c17ea37380bb8 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: arm64 Version: 0.3.2-1.ca2604.2 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 794 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_arm64.deb Size: 538074 MD5sum: ed4128d55a20a736056ddf0e9fe89ed7 SHA1: d417ed0d0fceffd56bd422f0c0d5ffe2f44a090a SHA256: 08334a82c3eea468549f629748402002c9837ab3464415ecbdd00a5febf8e401 SHA512: 06e87458e4c34b048e8804cc969d9106f57dd790b701dba37bd8805a70bb584c4724c475839ebdf14cd3986c84aca40fc0337846d7d6fbea67863388c83e3d84 Homepage: https://cran.r-project.org/package=treesitter Description: CRAN Package 'treesitter' (Bindings to 'Tree-Sitter') Provides bindings to 'Tree-sitter', an incremental parsing system for programming tools. 'Tree-sitter' builds concrete syntax trees for source files of any language, and can efficiently update those syntax trees as the source file is edited. It also includes a robust error recovery system that provides useful parse results even in the presence of syntax errors. Package: r-cran-treespace Architecture: arm64 Version: 1.1.4.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1416 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1127612 MD5sum: c7eaefc8295459d8b8556ca67c487cb8 SHA1: 9ed3657b254dfd96fbebe3d48ccef0a29706a7b4 SHA256: deda20056dedcfdd62c49aefdb2c79aa019ccb59926adcc5ef33cded7d1d7f00 SHA512: 474c3cf21a2752b0899841eb234d273084c62a48c94801071630664dc09586d3520d398c9a6d3e9cc30f787c6e6c5fc00fe111010b4c86cd11b5406b3cb4bd35 Homepage: https://cran.r-project.org/package=treespace Description: CRAN Package 'treespace' (Statistical Exploration of Landscapes of Phylogenetic Trees) Tools for the exploration of distributions of phylogenetic trees. This package includes a 'shiny' interface which can be started from R using treespaceServer(). For further details see Jombart et al. (2017) . Package: r-cran-treess Architecture: arm64 Version: 0.1.44-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1260 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_arm64.deb Size: 1062316 MD5sum: e8500f863e7e8011feb57c01c62c5957 SHA1: fced7eadf2b3d4a3eacd803af65af04813a07247 SHA256: c94d7019c40726f0d04b210c776cd92f43620322bc3e5147e87b54c6c0d8461a SHA512: d4ee23125b5419af0f9f2f8d325aa05d2773cf77d81bb41ffdb3e2c0be700172b288aea380430722af92b8904a7b15447219bd5d59669ca491be9a92a8df220f Homepage: https://cran.r-project.org/package=treeSS Description: CRAN Package 'treeSS' (Tree-Spatial Scan Statistic for Cluster Detection) Implements the tree-spatial scan statistic for detecting clusters that combine both spatial and hierarchical structures, as proposed by Cancado et al. (2025) . The method extends Kulldorff (1997) circular spatial scan statistic and the tree-based scan statistic of Kulldorff et al. (2003) by searching for anomalies in both geographic regions and branches of hierarchical trees simultaneously. The package also provides standalone implementations of Kulldorff's circular spatial scan statistic and the tree-based scan statistic. Statistical significance is assessed via Monte Carlo simulation under a Poisson or binomial model, with optional 'OpenMP' parallelization. Package: r-cran-treestats Architecture: arm64 Version: 1.70.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 8164 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_arm64.deb Size: 4361744 MD5sum: c2e0b64796b52fabfb7f49bf2dc292df SHA1: a5c6555e76266a657209ba40ccb38a24c297bae5 SHA256: 66bd0802806c38eea6fd2b86c89da3846219b8de0032446026d1112eb9f75fe3 SHA512: 193ccc39a7056659220bca36b07261f31f6f457c50c345f9f172db0d67ee0e88fa700a6e528bce07b72d2d0000cc03c670b4750565ae3865ce2a04f05666bed2 Homepage: https://cran.r-project.org/package=treestats Description: CRAN Package 'treestats' (Phylogenetic Tree Statistics) Collection of phylogenetic tree statistics, collected throughout the literature. All functions have been written to maximize computation speed. The package includes umbrella functions to calculate all statistics, all balance associated statistics, or all branching time related statistics. Furthermore, the 'treestats' package supports summary statistic calculations on Ltables, provides speed-improved coding of branching times, Ltable conversion and includes algorithms to create intermediately balanced trees. Full description can be found in Janzen (2024) . Package: r-cran-treestructure Architecture: arm64 Version: 0.7.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2487 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 1791878 MD5sum: ac54d0e4b0c1266ffc9f02fdb439c755 SHA1: eec1b665aeabe197769e997e0c3e286f32f38b36 SHA256: 94189136d92681e3a23859375b18a873eba680fd801c969e21d55b807acda8d3 SHA512: 94261d77a38c1ab9166a40d024f36d66d77ac29dfadb9a2669ca03aa0c808061d9e6e7407ddee86405d603d2ccf24ad2a7a6662819727442251f47ac22591c02 Homepage: https://cran.r-project.org/package=treestructure Description: CRAN Package 'treestructure' (Detect Population Structure Within Phylogenetic Trees) Algorithms for detecting population structure from the history of coalescent events recorded in phylogenetic trees. 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Package: r-cran-treetools Architecture: arm64 Version: 2.3.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2545 Depends: libc6 (>= 2.17), libgcc-s1 (>= 4.2), 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_arm64.deb Size: 1790964 MD5sum: b20132ddecc9f700c3fdd634d334dbac SHA1: 47f59d018fd943c15b5a37a241982c904df4e7f4 SHA256: 717df386c6a529926b3fb916fe397da87c79b7e3c90da9febc282db3c948087e SHA512: f432c96daef2cf8cd12a1d8607a65f5c4e35e34ce55b96b0d1f05c6cd98eef165c25e6c26f4d97040b6c06f270bb1a4677acbbc7b82f7ce775e4f2c01da094cc Homepage: https://cran.r-project.org/package=TreeTools Description: CRAN Package 'TreeTools' (Create, Modify and Analyse Phylogenetic Trees) Efficient implementations of functions for the creation, modification and analysis of phylogenetic trees. Applications include: generation of trees with specified shapes; tree rearrangement; analysis of tree shape; rooting of trees and extraction of subtrees; calculation and depiction of split support; plotting the position of rogue taxa (Klopfstein & Spasojevic 2019) ; calculation of ancestor-descendant relationships, of 'stemwardness' (Asher & Smith, 2022) , and of tree balance (Mir et al. 2013, Lemant et al. 2022) , ; artificial extinction (Asher & Smith, 2022) ; import and export of trees from Newick, Nexus (Maddison et al. 1997) , and TNT formats; and analysis of splits and cladistic information. Package: r-cran-trend Architecture: arm64 Version: 1.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 543 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 374268 MD5sum: a9f096c7d309ee3472a6d8be50a22955 SHA1: 97f928b6d628ab0c63226f3b7c486031cada358a SHA256: 4a84b60d53a9b298e427a0e2911069d95c5e86b2e13467923a7cd88e67a6d43d SHA512: a54dd33673718a75c0ce881b5645837b9bc1c44c9bf6dc2061bfb2ed37ff53ba87111f7a959e831bd7189c5786c4a383fab60e75b47fe88d85773ab927f2c7bf Homepage: https://cran.r-project.org/package=trend Description: CRAN Package 'trend' (Non-Parametric Trend Tests and Change-Point Detection) The analysis of environmental data often requires the detection of trends and change-points. This package includes tests for trend detection (Cox-Stuart Trend Test, Mann-Kendall Trend Test, (correlated) Hirsch-Slack Test, partial Mann-Kendall Trend Test, multivariate (multisite) Mann-Kendall Trend Test, (Seasonal) Sen's slope, partial Pearson and Spearman correlation trend test), change-point detection (Lanzante's test procedures, Pettitt's test, Buishand Range Test, Buishand U Test, Standard Normal Homogeinity Test), detection of non-randomness (Wallis-Moore Phase Frequency Test, Bartels rank von Neumann's ratio test, Wald-Wolfowitz Test) and the two sample Robust Rank-Order Distributional Test. Package: r-cran-trialemulation Architecture: arm64 Version: 0.0.4.11-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3155 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 2672932 MD5sum: 799869e7f241b7ea614af136ff988985 SHA1: daf1de8b27685dd9591d52d6b90de5636f0df671 SHA256: 3c65e0c0f8c7db6f2e5495ee173e9ee4904048ca6d946b855c5010c6cce64922 SHA512: 178e3f5e83b3f21300e0a71b208a322c67d7843ca2f3cf533ce62bfbd42085a33b3122a127e547a213e4fdad9d20ff9b7be0f2b740999095538b2b7d08c55131 Homepage: https://cran.r-project.org/package=TrialEmulation Description: CRAN Package 'TrialEmulation' (Causal Analysis of Observational Time-to-Event Data) Implements target trial emulation methods to apply randomized clinical trial design and analysis in an observational setting. 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Package: r-cran-trialr Architecture: arm64 Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 11513 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_arm64.deb Size: 3498580 MD5sum: ac3028e61320d8728a726e5f55c135b6 SHA1: 0bdaa039e4f46707631d5609be36f9abf5d0e38e SHA256: 60dbfb0c09dc977b2d752389ea0ed28be58f08277b326e6d8a67f4e1153441bc SHA512: b185065fb72592611a03b7c1554e981088c339a3c5a2e898df04e438ab6a5f411fdfc1b94177e7cd098db9b9e1d58460bf3bbf21df071450cb4a48aef95de1d2 Homepage: https://cran.r-project.org/package=trialr Description: CRAN Package 'trialr' (Clinical Trial Designs in 'rstan') A collection of clinical trial designs and methods, implemented in 'rstan' and R, including: the Continual Reassessment Method by O'Quigley et al. (1990) ; EffTox by Thall & Cook (2004) ; the two-parameter logistic method of Neuenschwander, Branson & Sponer (2008) ; and the Augmented Binary method by Wason & Seaman (2013) ; and more. We provide functions to aid model-fitting and analysis. The 'rstan' implementations may also serve as a cookbook to anyone looking to extend or embellish these models. We hope that this package encourages the use of Bayesian methods in clinical trials. There is a preponderance of early phase trial designs because this is where Bayesian methods are used most. If there is a method you would like implemented, please get in touch. Package: r-cran-trialsimulator Architecture: arm64 Version: 1.18.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4902 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.6.0), r-api-4.0, r-cran-base64enc, r-cran-dplyr, r-cran-emmeans, r-cran-ggplot2, r-cran-gmcplite, r-cran-htmltools, r-cran-mvtnorm, r-cran-r6, r-cran-rcpp, r-cran-rlang, r-cran-rpact, r-cran-rstudioapi, r-cran-survival Suggests: r-cran-data.table, r-cran-dosefinding, r-cran-graphicalmcp, r-cran-kableextra, r-cran-knitr, r-cran-mirai, r-cran-pweall, r-cran-pwexp, r-cran-rmarkdown, r-cran-simdata, r-cran-survminer, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-trialsimulator_1.18.4-1.ca2604.1_arm64.deb Size: 2895704 MD5sum: 05945ca1dfc2ec236aa45ac2b42388f5 SHA1: 9ba5df4ade94a38c62e40a47e788cb0735422ca3 SHA256: b82d9f22271e81d0697eca15ffd667939d913691cbc9ddff441367395719c9d0 SHA512: cb9c82c53d5fcf56d4a40caada76539564e0c58e0e63898cb2b8a35070616782c66b4cbf6bb69842f18a8ef009ff7e1a6c91576db7e099382fae86cd5212f63a Homepage: https://cran.r-project.org/package=TrialSimulator Description: CRAN Package 'TrialSimulator' (Clinical Trial Simulator) Simulate phase II and/or phase III clinical trials. It supports various types of endpoints and adaptive strategies. Tools for carrying out graphical testing procedure and combination test under group sequential design are also provided. Package: r-cran-trialsize Architecture: arm64 Version: 1.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 385 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_arm64.deb Size: 269082 MD5sum: 409e45a9c6c84dffc457dda0e1f4ab3d SHA1: d2395c3a38481917f764183aa336730e5330162c SHA256: 1095dc51fdb7aee5bb922a22e5d265a9615ba6e463b8e2d0bd7d477d0b5f290b SHA512: e39640b0ac554bfffed9f5f89f4767ccced2bbc5b5f2349cbaec1b6e3f9e7aad32e877449ca10440c85f808fa173652ddf8456c79a55018fc93e23f2e15d946a 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: arm64 Version: 0.4.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 616 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 155678 MD5sum: 4beb444650830ed4d9e1292ed4c35776 SHA1: 27d77eb5a01d9c3c555002325ea153db8a28255c SHA256: 7d9d01aecc9c9b829259b7a80d56cd311e72e782647b4756203426b44cea6a9e SHA512: 759eaeaa28e3fb91921fc8f11b43ae6e2cb6424c61c866b0975417d99d1c2e9922642a32fbc08bee9d3bbf3f0f68da93c7ba963eb17a71bc1013a7ebd3677379 Homepage: https://cran.r-project.org/package=triebeard Description: CRAN Package 'triebeard' ('Radix' Trees in 'Rcpp') 'Radix trees', or 'tries', are key-value data structures optimised for efficient lookups, similar in purpose to hash tables. 'triebeard' provides an implementation of 'radix trees' for use in R programming and in developing packages with 'Rcpp'. Package: r-cran-triosgl Architecture: arm64 Version: 1.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 118 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-triosgl_1.1.0-1.ca2604.1_arm64.deb Size: 26794 MD5sum: 344284bdd5ca13b98f999c170aaab4f2 SHA1: 3865984829784f84f39549b36868f4b94bfe0e80 SHA256: 85e7056ad5b81155bb6483ff6b6269b8aa03de25ab94e8183e269519c1c8d9eb SHA512: bd463633907afc4f801f1546a9137b02f0f414d533ccb60bf761c4bbe28136bef66c9549eaa69c5f2b01174af5d50a107b85cb654c31be2c9f5f321a3bd7de06 Homepage: https://cran.r-project.org/package=TrioSGL Description: CRAN Package 'TrioSGL' (Trio Model with a Combination of Lasso and Group LassoRegularization) Fit a trio model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. This package is based on Noah Simon, et al. (2011) . 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Please note that most of the functions are now also covered in package interp, which is a re-implementation from scratch under a free license based on a different triangulation algorithm. Package: r-cran-triplediff Architecture: arm64 Version: 0.2.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 533 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 361846 MD5sum: e820e634420cacac14f9f26135ef773a SHA1: 39d5c8b5f1f0d133ad558c5d0b8e977d6de58f99 SHA256: 6768b8b322d160b99fea194dd8d9299ca021f4e5db5752dd1a8e2a5bc0721861 SHA512: 8eacdbba2cec85135f2f5e4d6324196691f0bee92f42bfd3fc9a91fcca2f4282eecdb0017261602a8bd4f7a2b469e6dd9c900b4d07c767a54e6f64b2b02db504 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. Methods include regression adjustment, inverse-probability weighting, and doubly-robust estimators, all of which rely on a conditional DDD parallel-trends assumption and allow covariate adjustment across multiple pre- and post-treatment periods. The methodology is detailed in Ortiz-Villavicencio and Sant'Anna (2025) . 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Package: r-cran-trtswitch Architecture: arm64 Version: 0.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3282 Depends: libc6 (>= 2.38), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 1345806 MD5sum: a7b506ba4ecbfe2955fffea7904646d9 SHA1: c2b2c2ec9e4e22561da24d296b11d46086c14ba9 SHA256: eb4fb118ca8049f5bd4fd1a714d768782f28f38933bae99bf10cc4add6a42ac9 SHA512: 71b7873a94ae8e3d57073cec7897c0071ae4f1eea23617d9b89fff9acac6d493324ae80592a8c77971f3d0d9afdc93025b8e917e602a8c4601f325224ea9e38b Homepage: https://cran.r-project.org/package=trtswitch Description: CRAN Package 'trtswitch' (Treatment Switching) Implements rank preserving structural failure time model (RPSFTM), iterative parameter estimation (IPE), inverse probability of censoring weights (IPCW), marginal structural model (MSM), simple two-stage estimation (TSEsimp), and improved two-stage estimation with g-estimation (TSEgest) methods for treatment switching in randomized clinical trials. Package: r-cran-truncatednormal Architecture: arm64 Version: 2.3-1.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-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_arm64.deb Size: 347574 MD5sum: 21b0d9ea295eeb918bc41c7bc0e55df3 SHA1: 9cb421851ca7c1143b412872baf16a4bc410f975 SHA256: fa2cab36b1c24049c96157c49c5366fccf47fba4a451ff9119451699ade59bb4 SHA512: ca832526d91299c50861121b13b4f5179c4f64befad684887b9e0623170e1e38c1970cd711e2b46bd79dbb3c358ff555410d9d41f382442b592d08d2c9eb53d2 Homepage: https://cran.r-project.org/package=TruncatedNormal Description: CRAN Package 'TruncatedNormal' (Truncated Multivariate Normal and Student Distributions) A collection of functions to deal with the truncated univariate and multivariate normal and Student distributions, described in Botev (2017) and Botev and L'Ecuyer (2015) . Package: r-cran-truncnorm Architecture: arm64 Version: 1.0-9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 112 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-truncnorm_1.0-9-1.ca2604.1_arm64.deb Size: 21996 MD5sum: cd4425fc79b30e7caf3a980af47f05e7 SHA1: c026537e319c83aac1584fd7eb1916e216c4a5d3 SHA256: 2fadd6eff9deb782ca12b37264eaa2026ee5205a8813b8ef4615226be4c52451 SHA512: af57b6404d6adbd83077f5822f9d1e49c65eac4ba1958f2ab389589ee457e33186febb2e5e2bca67c26395067b95da7557f39d09ee32e44043cb3f3b6b38b2f6 Homepage: https://cran.r-project.org/package=truncnorm Description: CRAN Package 'truncnorm' (Truncated Normal Distribution) Density, probability, quantile and random number generation functions for the truncated normal distribution. Package: r-cran-truncnormbayes Architecture: arm64 Version: 0.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1335 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-rdpack, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-testthat, r-cran-truncnorm, r-cran-withr Filename: pool/dists/resolute/main/r-cran-truncnormbayes_0.0.3-1.ca2604.1_arm64.deb Size: 486388 MD5sum: 228807cf5ed08fa0145ae72d41ee4934 SHA1: f7545dd52275cbaa66530e059feb67479c18592f SHA256: 0cb168e8abc80d48f2325887aebfeee10f4edd67f064aede9f4bd515ade570bc SHA512: e0ef8ae09fb8dc3094794e34906af6894dd9b4daca50c454947576f52bf65b3d8a64ccc9557519af3aaf37ff0cf9757ba78630821b0ced4de88f09d5d9cd52f6 Homepage: https://cran.r-project.org/package=truncnormbayes Description: CRAN Package 'truncnormbayes' (Estimates Moments for a Truncated Normal Distribution using'Stan') Finds the posterior modes for the mean and standard deviation for a truncated normal distribution with one or two known truncation points. 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Package: r-cran-truncproxy Architecture: arm64 Version: 0.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 252 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_arm64.deb Size: 77914 MD5sum: 65257d022db4f5e460d3dc288bc1ed8e SHA1: f02fcda12ed03047d9d1ac824071b1d313ced7fe SHA256: 0ea8260174f1e606744f96441bd8f2eb0608294ab2a9ff9bb42963778f86ffd8 SHA512: 2f62528376ba398ee06546f4c7e0e12546e5fbe34cca7492bc906f1b5a61e906628fc1c00ff43d968609c7bdc6cbf50942aff35c9cf42b58773ed1f2addc1a71 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. 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Package: r-cran-trustoptim Architecture: arm64 Version: 0.8.7.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 503 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 170298 MD5sum: 6d14e175e8e85461bf7bd0050106f088 SHA1: ab0f1d768303a1fa237047351d3b37197797abbd SHA256: 7669fcf1a7a6880cd3c51c48d86f1be1a53c5de7b2fc7f5ea9eda52a3ef97bff SHA512: 6a25bae4874812417ad99ca147f2374de915f46c1389308a435464d39c95e3b0f1aad043c7f435dad4771986149df380393727a3eb3256fae3a8c1af414bccc0 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. 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Different functions based on AMUSE and SOBI are also provided for estimating the dimension of the white noise subspace. The package is fully described in Nordhausen, Matilainen, Miettinen, Virta and Taskinen (2021) . 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Distributions include the Normal, Student and GED as well as their skewed variants ('Fernandez and Steel'), the 'Johnson SU', and the Generalized Hyperbolic. Also included is the semi-parametric piece wise distribution ('spd') with Pareto tails and kernel interior. Package: r-cran-tsdyn Architecture: arm64 Version: 11.0.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 4055 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 3761340 MD5sum: 20ba8fc8cd9869d2aaee3c402b64187a SHA1: 2f35bce5b9b80f883c806cc39faf24bc8a67884a SHA256: a405a03049da71f3eaec527f48958fa4044fc7efdb627183397baa5293d96688 SHA512: 83da31185adb8382bfbe25ede7706ff75b7c914a3c51b0c3da5014696a1c8df99f800866572755e9f8c3c98366bb7bef1fc2a96952cfc8a1d887a124e950a671 Homepage: https://cran.r-project.org/package=tsDyn Description: CRAN Package 'tsDyn' (Nonlinear Time Series Models with Regime Switching) Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006). Package: r-cran-tsentropies Architecture: arm64 Version: 0.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 139 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tsentropies_0.9-1.ca2604.1_arm64.deb Size: 41860 MD5sum: 423f846d235aed02673704f4a3035403 SHA1: f073b2c2687076dac9640af8baabfb1216b3d628 SHA256: 502a11724ccc1330b0b00c618f761a5206644f67463c7486cc9fa3fbfbe0143e SHA512: c101f76de5abbf57eb14b7dc94fcf87b131c9684a79059a1832d69db0b4ecb79415a7f2d3693a319ba33c4dc8e0ac7c61f859b84e94c5f6206e8252b5dd82e66 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. 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It provides functions and methods for: TARMA model fitting and forecasting, including robust estimators, see Goracci et al. JBES (2025) ; tests for threshold effects, see Giannerini et al. JoE (2024) , Goracci et al. Statistica Sinica (2023) , Angelini et al. (2024) OBES ; unit-root tests based on TARMA models, see Chan et al. Statistica Sinica (2024) . 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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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A review of some of these models can be found in Boudt, Galanos, Payseur and Zivot (2019) . 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These models are commonly used in psychology to represent temporal and contemporaneous relationships between multiple variables in intensive longitudinal data. Fitted models can be compared with a test based on matrix norm differences of posterior point estimates to quantify the differences between two estimated networks. See also Siepe, Kloft & Heck (2024) . 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Package: r-cran-tspi Architecture: arm64 Version: 1.0.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 181 Depends: libc6 (>= 2.17), liblapack3 | liblapack.so.3, r-base-core (>= 4.5.0), r-api-4.0, r-cran-kfas Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tspi_1.0.4-1.ca2604.1_arm64.deb Size: 84744 MD5sum: ae1983526e4ef59585b48b08ccfaeb2c SHA1: db9f9f0ac9ccaf3afa8845fb1c4402daf11d0145 SHA256: 101d56f9db4426caba60de2ca423e8e05b037f6a3a115c2bee01a0790aeb1cf1 SHA512: 473b30693aa64b245435bed3dfe09617a0b0448bc80323a92f2fe47b7c2409c01f4062709f1f144aa972b9c234db9be4db13e89acfc1c41e2efc8a712a607404 Homepage: https://cran.r-project.org/package=tsPI Description: CRAN Package 'tsPI' (Improved Prediction Intervals for ARIMA Processes and StructuralTime Series) Prediction intervals for ARIMA and structural time series models using importance sampling approach with uninformative priors for model parameters, leading to more accurate coverage probabilities in frequentist sense. Instead of sampling the future observations and hidden states of the state space representation of the model, only model parameters are sampled, and the method is based solving the equations corresponding to the conditional coverage probability of the prediction intervals. This makes method relatively fast compared to for example MCMC methods, and standard errors of prediction limits can also be computed straightforwardly. Package: r-cran-tspmeta Architecture: arm64 Version: 1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-ggplot2, r-cran-tsp, r-cran-mass, r-cran-bbmisc, r-cran-checkmate, r-cran-fpc, r-cran-vegan, r-cran-stringr, r-cran-splancs Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tspmeta_1.2-1.ca2604.1_arm64.deb Size: 133394 MD5sum: 4cb04c2e44223f7dee6e3fbfe95fcb59 SHA1: ac5ff17e172e3ca83a2f4e677c889b41595b3676 SHA256: 0d95e6f1320f8816b974d73c76b9658882685568721f5aef8761a79b91f92524 SHA512: 9662f9e1225529b9a22e6bce1e834a59df56233416aaa809e4ccdbd490b1c772df076509681a8643789f4a8c7195c4ea17413b1f6ad6bf8d53405b7176dc1864 Homepage: https://cran.r-project.org/package=tspmeta Description: CRAN Package 'tspmeta' (Instance Feature Calculation and Evolutionary InstanceGeneration for the Traveling Salesman Problem) Instance feature calculation and evolutionary instance generation for the traveling salesman problem. 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Package: r-cran-tsss Architecture: arm64 Version: 1.3.4-7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 583 Depends: libc6 (>= 2.41), libgfortran5 (>= 10), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tsss_1.3.4-7-1.ca2604.1_arm64.deb Size: 421954 MD5sum: bcc7b775d42093dd3c8595431af89d7d SHA1: ce2ba36dd80ee2f7a95f1bb98e9548d524323e83 SHA256: 092048c091ac5ad22d0df8eadf106e9cf3d64f6340608393cced62299c40bc43 SHA512: e04648d4229f2bccee55312b616fdc149c7ee733f49b3554a75a2d779a8d55c3e2ae0109a78299f66717f35b118a8ba3e03c9dff8defe786655acdefca693412 Homepage: https://cran.r-project.org/package=TSSS Description: CRAN Package 'TSSS' (Time Series Analysis with State Space Model) Functions for statistical analysis, modeling and simulation of time series with state space model, based on the methodology in Kitagawa (2020, ISBN: 978-0-367-18733-0). 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Package: r-cran-tsxtreme Architecture: arm64 Version: 0.3.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 261 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), r-api-4.0, r-cran-evd, r-cran-mvtnorm, r-cran-mass, r-cran-tictoc Filename: pool/dists/resolute/main/r-cran-tsxtreme_0.3.4-1.ca2604.1_arm64.deb Size: 184980 MD5sum: fc8932592b80a80ec8224e6a95046557 SHA1: 4ced64798b9ef020d3773bab7fb9e0e6430d50f5 SHA256: 6e7c892e0b4be433c7b6c1ff967c948b10bd364eae393e2526ff140599b27735 SHA512: 24b4f6a913ce2eaeedb857f1ff9b234b5f2810c9a98fd43066223c227626221fe705f68798ba4eb840c2b6e154715d96743786f19b5c40bab46e6ac5bfd82f6c Homepage: https://cran.r-project.org/package=tsxtreme Description: CRAN Package 'tsxtreme' (Bayesian Modelling of Extremal Dependence in Time Series) Characterisation of the extremal dependence structure of time series, avoiding pre-processing and filtering as done typically with peaks-over-threshold methods. 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Package: r-cran-tulpamesh Architecture: arm64 Version: 0.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 828 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-rcppparallel Suggests: r-cran-testthat, r-cran-sf, r-cran-fmesher, r-cran-ggplot2, r-cran-svglite, r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tulpamesh_0.1.1-1.ca2604.1_arm64.deb Size: 274122 MD5sum: 8c218d0fa7a220090034ade21a76ade9 SHA1: 7fa4d0d3c4271b94c46e5fd3ad8dc23a3c586c4a SHA256: 6f2158ccdab3ad2c7208c5b94c86e94f180a355395d9c4ca2b05bce38b31a1ec SHA512: 8db5b41650e287d86fef95bce0cddfd6361bfc912f5d7915f3d2729f966e989f6f77d5a8402defc75fa4f9a17938da962d05bc8a547148540530859380264fb0 Homepage: https://cran.r-project.org/package=tulpaMesh Description: CRAN Package 'tulpaMesh' (Constrained Delaunay Triangulation Meshes for Spatial 'SPDE'Models) Generate constrained Delaunay triangulation meshes for use with stochastic partial differential equation (SPDE) spatial models (Lindgren, Rue and Lindstroem 2011 ). 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Package: r-cran-tunepareto Architecture: arm64 Version: 2.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 317 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-snowfall, r-cran-igraph, r-cran-gsl, r-cran-class, r-cran-tree, r-cran-e1071, r-cran-randomforest, r-cran-klar Filename: pool/dists/resolute/main/r-cran-tunepareto_2.5.3-1.ca2604.1_arm64.deb Size: 213726 MD5sum: 60f69291bf7a2b021888bf29328f6d14 SHA1: c81b222cc06c9022c0e129db9b7458c5ee63abf6 SHA256: 07f3f95abe4429c162b8a86b8b1ee2096bff27cf7cb93dfa7043da15a053fa93 SHA512: 6a8ac321d66f35caec5c560f39001838744125d13940c64638e7ee5be004f099e7fed5226fb9d96de6f1096c02c7ada8351075d4aabd4dee4568e40bdfbf4ee3 Homepage: https://cran.r-project.org/package=TunePareto Description: CRAN Package 'TunePareto' (Multi-Objective Parameter Tuning for Classifiers) Generic methods for parameter tuning of classification algorithms using multiple scoring functions (Muessel et al. (2012), ). Package: r-cran-tuner Architecture: arm64 Version: 1.4.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 759 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-signal Suggests: r-cran-pastecs Filename: pool/dists/resolute/main/r-cran-tuner_1.4.7-1.ca2604.1_arm64.deb Size: 554860 MD5sum: bcd0963f82f4473477b0889ed499c696 SHA1: e4d04d6fb534af366abc5477ba8b5798fce41755 SHA256: 3943db8107daecb610718b27a3ebb010b14f32653ca0e9929858c0fe1b4b3492 SHA512: f53c615fc58dd17fe654fd2c559e3b9d4994242a9dd79bd5d090e6829ed031019ccebea0a47f2bfeaf2e1de9fd28c1da613852c81575aa1e5494886e25a7ecfc Homepage: https://cran.r-project.org/package=tuneR Description: CRAN Package 'tuneR' (Analysis of Music and Speech) Analyze music and speech, extract features like MFCCs, handle wave files and their representation in various ways, read mp3, read midi, perform steps of a transcription, ... Also contains functions ported from the 'rastamat' 'Matlab' package. Package: r-cran-tuwmodel Architecture: arm64 Version: 1.1-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1038 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-tuwmodel_1.1-1-1.ca2604.1_arm64.deb Size: 902416 MD5sum: 8d9fd9d99353b8a02a990f8c0262ad9d SHA1: 4a55010899fda578b001f713c126546e933ef7b6 SHA256: 4fada6ef902b302b24303af6e30d63fccbaa135c2ff2410a430798ab5323013e SHA512: 5069a1dd3e92aa0aaf9ab0a943ed4e7249dede5d02accdda32990231403f8caca051deabcb76e6c91a25c9fe11927244c3c172242227d88531d2a25b2f3bf26d Homepage: https://cran.r-project.org/package=TUWmodel Description: CRAN Package 'TUWmodel' (Lumped/Semi-Distributed Hydrological Model for EducationPurposes) The model, developed at the Vienna University of Technology, is a lumped conceptual rainfall-runoff model, following the structure of the HBV model. The model can also be run in a semi-distributed fashion and with dual representation of soil layer. The model runs on a daily or shorter time step and consists of a snow routine, a soil moisture routine and a flow routing routine. See Parajka, J., R. Merz, G. Bloeschl (2007) Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments, Hydrological Processes, 21, 435-446. Package: r-cran-tvdenoising Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 316 Depends: libc6 (>= 2.17), 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-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tvdenoising_1.0.0-1.ca2604.1_arm64.deb Size: 210538 MD5sum: d4bea4a6b9ad2cee79af6484ec20dc2c SHA1: 5298e17077dc54af4881509fb3dd08a97ab758a9 SHA256: 3e5e0b1016a19b03eadbb27c88ea006640f2bef11a9c5bb3e0092281f096e986 SHA512: ab6c2fe67281652e7f4bad6cadca6eff85aa49ea0fb103e3de53cac975b7a4fc49fe70fb6e796a9595784dbb57c5b97e041abd13aa2a907adaecdd90ba2f9b8d Homepage: https://cran.r-project.org/package=tvdenoising Description: CRAN Package 'tvdenoising' (Univariate Total Variation Denoising) Total variation denoising can be used to approximate a given sequence of noisy observations by a piecewise constant sequence, with adaptively-chosen break points. An efficient linear-time algorithm for total variation denoising is provided here, based on Johnson (2013) . Package: r-cran-tvgarchkf Architecture: arm64 Version: 0.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 161 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-fgarch, r-cran-rcpp Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tvgarchkf_0.0.1-1.ca2604.1_arm64.deb Size: 61874 MD5sum: 8f9b569128d1b51a48d467056392beb7 SHA1: 1c4548e4ff71f0fa1a04a5a594a6a0a936c2fdf8 SHA256: 9462747188bed49a9b1e697fe1c33e660df689d7432998d4558c5ff41111c360 SHA512: 7a4207b8c2bb5b8a43aff7ec141d748bb81fcc9821910aa2757a62ada952300f4c1aa17c932291adcd825f7bdc49d23a155d3f7494f5552fc2122e6c00485e1e Homepage: https://cran.r-project.org/package=tvGarchKF Description: CRAN Package 'tvGarchKF' (Time-Varying Garch Models Through a State-Space Representation) Estimates the time-varying (tv) parameters of the GARCH(1,1) model, enabling the modeling of non-stationary volatilities by allowing the model parameters to change gradually over time. The estimation and prediction processes are facilitated through the application of the Kalman filter and state-space equations. This package supports the estimation of tv parameters for various deterministic functions, which can be identified through exploratory analysis of different time periods or segments of return data. The methodology is grounded in the framework presented by Ferreira et al. (2017) . Package: r-cran-tvr Architecture: arm64 Version: 0.3.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 873 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-rdpack, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-tvr_0.3.3-1.ca2604.1_arm64.deb Size: 693358 MD5sum: 57b5dba08d1578966014c0f9603b3593 SHA1: 9f762d02cacbe46e5940257dfb2db0f88267c725 SHA256: e765f648b9ef4995409c0a5f9e8154a8db610d8fc3cb5bccadc9a28aa595c202 SHA512: 22edecf0ab6b36e90082ae37b65f2d823c403d85a9247cd387502e851a1cfb0bf01d2be791f22bf153a137f67bc538a39ad2006c8d55ec26e057ebd6787b3852 Homepage: https://cran.r-project.org/package=tvR Description: CRAN Package 'tvR' (Total Variation Regularization) Provides tools for denoising noisy signal and images via Total Variation Regularization. Reducing the total variation of the given signal is known to remove spurious detail while preserving essential structural details. For the seminal work on the topic, see Rudin et al (1992) . Package: r-cran-twang Architecture: arm64 Version: 2.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1512 Depends: libc6 (>= 2.29), r-base-core (>= 4.5.0), r-api-4.0, r-cran-gbm, r-cran-survey, r-cran-xtable, r-cran-lattice, r-cran-latticeextra, r-cran-matrixmodels, r-cran-data.table, r-cran-ggplot2, r-cran-xgboost Suggests: r-cran-knitr Filename: pool/dists/resolute/main/r-cran-twang_2.6.2-1.ca2604.1_arm64.deb Size: 1239280 MD5sum: dc0edae2c98b2b54043b1c3410044367 SHA1: 0bfeff76b374c2f0c636414a4fc6707a9a1f58c7 SHA256: 26cf28c2858175aa89331a14acbf62bed822d8b738ca3384fcd195d652a9347f SHA512: a08216b6b97a61319ad05de98a696195664a6609c76aac4dd13a629239c9ab1aaf369c7488fc4b683f05c18a0d3d4210ab46830390f645edecb8b6ed7ca4221c Homepage: https://cran.r-project.org/package=twang Description: CRAN Package 'twang' (Toolkit for Weighting and Analysis of Nonequivalent Groups) Provides functions for propensity score estimating and weighting, nonresponse weighting, and diagnosis of the weights. Package: r-cran-twdtw Architecture: arm64 Version: 1.0-1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 216 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgfortran5 (>= 8), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-proxy Suggests: r-cran-rbenchmark, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-twdtw_1.0-1-1.ca2604.1_arm64.deb Size: 79598 MD5sum: 1d4b3fa91e3a56bc4a6b93349e01d3fd SHA1: 267546ff975fd442aefe793ef411c48a42a99186 SHA256: 00882d93570e87e78de44e09ab2b0336b0d1b235495fe9633fc9568b5b52b62f SHA512: 642b65610f47fce3a4d52f14c1e276db8832664190b0ed6f9f92c9d8373b5d5dd8a828ead1b1e91ee3c885531cfd89effc14f8676a8eed76448e0658d9bba74c Homepage: https://cran.r-project.org/package=twdtw Description: CRAN Package 'twdtw' (Time-Weighted Dynamic Time Warping) Implements Time-Weighted Dynamic Time Warping (TWDTW), a measure for quantifying time series similarity. The TWDTW algorithm, described in Maus et al. (2016) and Maus et al. (2019) , is applicable to multi-dimensional time series of various resolutions. It is particularly suitable for comparing time series with seasonality for environmental and ecological data analysis, covering domains such as remote sensing imagery, climate data, hydrology, and animal movement. The 'twdtw' package offers a user-friendly 'R' interface, efficient 'Fortran' routines for TWDTW calculations, flexible time weighting definitions, as well as utilities for time series preprocessing and visualization. Package: r-cran-tweedie Architecture: arm64 Version: 3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 531 Depends: libc6 (>= 2.29), libgfortran5 (>= 10), r-base-core (>= 4.6.0), r-api-4.0, r-cran-lifecycle, r-cran-statmod Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tweedie_3.1.0-1.ca2604.1_arm64.deb Size: 379558 MD5sum: b60fbecbc3112fd803863f47b725fe74 SHA1: 3b6ecf1ae39858c6d5d1c450c24f612de98097e4 SHA256: cdb32270ebc823b64073311a841ff3e84fc4b7754d4f5471a322d3b9a913de5b SHA512: 03eff861c3d6b9d5c7b327b58c8f6d626913c78d6ddb21c669a133e18219270f80de65b4d799fae49769e59863e453808894005c36a232da054a8b1af527537f Homepage: https://cran.r-project.org/package=tweedie Description: CRAN Package 'tweedie' (Evaluation of Tweedie Exponential Family Models) Maximum likelihood computations for Tweedie families, including the series expansion (Dunn and Smyth, 2005; ) and the Fourier inversion (Dunn and Smyth, 2008; ), and related methods. Package: r-cran-tweenr Architecture: arm64 Version: 2.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 798 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-farver, r-cran-magrittr, r-cran-rlang, r-cran-vctrs, r-cran-cpp11 Suggests: r-cran-testthat, r-cran-covr Filename: pool/dists/resolute/main/r-cran-tweenr_2.0.3-1.ca2604.1_arm64.deb Size: 418034 MD5sum: ad0ac9056fde4617fbbe292646399e29 SHA1: 789eeeabc6a2b138521773ef0d0a78f1923bf7f5 SHA256: 6244c921ea558791f8e06f48bb7ebedc1255914c562cf42900e7a6031b4eacb8 SHA512: 437cbaa8c01df43c9a329ab422048c6358faa4f570a17192c95430e922e99a47f8ca7c6d1a6e16a65e2837a21aacc3bc38113478b188a28d8c687f4736d6a698 Homepage: https://cran.r-project.org/package=tweenr Description: CRAN Package 'tweenr' (Interpolate Data for Smooth Animations) In order to create smooth animation between states of data, tweening is necessary. This package provides a range of functions for creating tweened data that can be used as basis for animation. Furthermore it adds a number of vectorized interpolaters for common R data types such as numeric, date and colour. Package: r-cran-twingp Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 439 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-rcpp, r-cran-nloptr, r-cran-rcppeigen Filename: pool/dists/resolute/main/r-cran-twingp_1.0.0-1.ca2604.1_arm64.deb Size: 146202 MD5sum: e63896fe4d0f0b20b7a0f5b97c575fdc SHA1: 7701c6f59d56e6f76fc6e20dd0d26309cab0fc1e SHA256: f4a90c3664d99c43b90e555438031834e4e05f4c3ca7a5e3a591abbda67d6dd1 SHA512: 1fde5c4e73b3ffdd710975ee5ec0bda0a8e2fe013a89415c0b343a403c0d7d82d52d13fb70e4ea3d0291af24ff9f512a8f50ef4a3ee3d05123935f353f2849ff Homepage: https://cran.r-project.org/package=twingp Description: CRAN Package 'twingp' (A Fast Global-Local Gaussian Process Approximation) A global-local approximation framework for large-scale Gaussian process modeling. Please see Vakayil and Joseph (2024) for details. This work is supported by U.S. NSF grants CMMI-1921646 and DMREF-1921873. Package: r-cran-twinning Architecture: arm64 Version: 1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 267 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_arm64.deb Size: 84604 MD5sum: a66756ce7cc8b40d9af63a4f685d0394 SHA1: 86525ef82c102dba69e205a2318500699a5b52f9 SHA256: 9abdb727cbfdd1f18cdd06b738eb2de91e799874b4465b0a58a14226f8c8c761 SHA512: 6566b820419d617b3561aea4e3415fbf9f1bb8e1c6acaa1262e8ada5bf791555c495ba7450401dac8f1ef4e2634036ae3f0916592d11c3214cf1a80c2edf10d6 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: arm64 Version: 2.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 312 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_arm64.deb Size: 191766 MD5sum: bdb14e376fc466e142b73d493830ef8a SHA1: 11e46e2088f4670f10f4f3a70b03bf3bd17f72ae SHA256: 38b56668e5b6d271f78217bef9d090c91f6fa234255daedbe60172e42f663c2b SHA512: c2e2ad0d7be41b785482248c949162fb0a27640863a4e52a615d46d3f55366e0ab353287cc8821b95afb6b2a0dfb67954fa55a7bae72b85dfca5692d4da8801e Homepage: https://cran.r-project.org/package=twosamples Description: CRAN Package 'twosamples' (Fast Permutation Based Two Sample Tests) Fast randomization based two sample tests. Testing the hypothesis that two samples come from the same distribution using randomization to create p-values. Included tests are: Kolmogorov-Smirnov, Kuiper, Cramer-von Mises, Anderson-Darling, Wasserstein, and DTS. The default test (two_sample) is based on the DTS test statistic, as it is the most powerful, and thus most useful to most users. The DTS test statistic builds on the Wasserstein distance by using a weighting scheme like that of Anderson-Darling. See the companion paper at or for details of that test statistic, and non-standard uses of the package (parallel for big N, weighted observations, one sample tests, etc). We also include the permutation scheme to make test building simple for others. Package: r-cran-twostepsdfm Architecture: arm64 Version: 0.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2372 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-zoo, r-cran-xts, r-cran-lubridate, r-cran-ggplot2, r-cran-patchwork, r-cran-dosnow, r-cran-doparallel, r-cran-foreach, r-cran-rdpack, r-cran-withr, r-cran-rcppeigen Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-twostepsdfm_0.2.2-1.ca2604.1_arm64.deb Size: 1380590 MD5sum: 906c267739c48ff3bb390d1663edb1fc SHA1: 6d35c1ceba2fa84b7fd452b4724d0e1e093fea11 SHA256: e21766fa357054962b71202f860ad3e95f58fcbcc4e1c8c482e15c2d45b71c37 SHA512: c577ffd5f41ae57d705a5185114f68e3b3144eb94b6e58961d936d243699c444dd52beb899786c8a796a253c8a739d95e9e09a8a51e9246b16e5025a2774d737 Homepage: https://cran.r-project.org/package=TwoStepSDFM Description: CRAN Package 'TwoStepSDFM' (Estimate a Sparse Mixed Frequency Gaussian Factor Model Using aTwo-Step Procedure) Estimate a sparse Gaussian state-space model with mixed frequency data via sparse principal components analysis and the Kalman filter and smoother. For more details see Franjic and Schweikert (2024) . Package: r-cran-tynding Architecture: arm64 Version: 0.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 36467 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-jsonlite Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tynding_0.1.2-1.ca2604.1_arm64.deb Size: 13981504 MD5sum: 5b85f39a146666ce728cb51f4130bd53 SHA1: 658a1ca9efaa0018b41b8992ceb3211e105bbdea SHA256: c7ad433db4350e19e17b4d977b44a4a8422528ec1cda8898586f2929d081a931 SHA512: e54a338c43429193eb61454b2d4ff6c5ea1a057628e98159b4913ff30679300d8c8f9bd990a33fe5b990bcafda56766e2a85852bf02457a44b1c840c96350dbf Homepage: https://cran.r-project.org/package=tynding Description: CRAN Package 'tynding' ('Typst' Bindings) Provides bindings to the 'Typst' typesetting system, enabling users to compile 'Typst' documents directly from R. The package interfaces with the 'Typst' 'Rust' library to render documents, making it possible to integrate 'Typst'-based workflows into R scripts, reports, and reproducible research pipelines. It supports programmatic document generation, compilation, and output handling, facilitating seamless use of 'Typst' alongside tools such as knitr and Quarto. Package: r-cran-typetracer Architecture: arm64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 791 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-brio, r-cran-checkmate, r-cran-rlang, r-cran-tibble, r-cran-withr Suggests: r-cran-knitr, r-cran-rematch, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-typetracer_0.2.3-1.ca2604.1_arm64.deb Size: 275044 MD5sum: 92fac298bfff2b8f11c5481155730697 SHA1: ff9f98622d0e02600ccb944a301ff26ea1872044 SHA256: 50f696ef910933da9cb9a62733e16953645f8076221245fb8f00aba0c27c2a34 SHA512: 2e5b5482b56ccd864a5eb541b09a21480d793c52cef47db58af262c8c48c01585660d1ca95e22e736e4915453101c131c6eedbc97d9d3d1c162a3656e06f980c Homepage: https://cran.r-project.org/package=typetracer Description: CRAN Package 'typetracer' (Trace Function Parameter Types) The 'R' language includes a set of defined types, but the language itself is "absurdly dynamic" (Turcotte & Vitek (2019) ), and lacks any way to specify which types are expected by any expression. 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. Package: r-cran-tzdb Architecture: arm64 Version: 0.5.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2289 Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libstdc++6 (>= 13.1), r-base-core (>= 4.5.0), r-api-4.0, r-cran-cpp11 Suggests: r-cran-covr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-tzdb_0.5.0-1.ca2604.1_arm64.deb Size: 527204 MD5sum: 245aca1c71864d9a3e9700ff296ac24a SHA1: 0f00707a7f2cc3931cea74f21584757441a85bb9 SHA256: d97040ccaecbe4ed2e183e55f0daa8a80d680fe55877350c0dd9c99fbc72e3a2 SHA512: afaf789d44dd117fe20cc49b3c735d4f5ee1f171555b2057ad536e885618025cd9313929e2356f61fab2835bc9e1beecd45f602cc24654ed6c7bab021573f317 Homepage: https://cran.r-project.org/package=tzdb Description: CRAN Package 'tzdb' (Time Zone Database Information) Provides an up-to-date copy of the Internet Assigned Numbers Authority (IANA) Time Zone Database. It is updated periodically to reflect changes made by political bodies to time zone boundaries, UTC offsets, and daylight saving time rules. Additionally, this package provides a C++ interface for working with the 'date' library. 'date' provides comprehensive support for working with dates and date-times, which this package exposes to make it easier for other R packages to utilize. Headers are provided for calendar specific calculations, along with a limited interface for time zone manipulations. Package: r-cran-uahdatasciencesf Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 870 Depends: r-base-core (>= 4.5.0), r-api-4.0, r-cran-magick, r-cran-crayon Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-uahdatasciencesf_1.0.0-1.ca2604.1_arm64.deb Size: 531030 MD5sum: 33dcf46096f010370f362c5a1d769851 SHA1: f7377d38275a36df03828920a7d695f054be89c0 SHA256: 6cdd681a62213b383e1477d83febe077891f06ef40a894740893155dcbe724e8 SHA512: 6c5e515629b161ab889a30aa607d6da0d3759c976160d0dca13772490c44e6baadb3f4705abce76bfa0907216093fc92eac1911a4fcc9857a71401e946c22d03 Homepage: https://cran.r-project.org/package=UAHDataScienceSF Description: CRAN Package 'UAHDataScienceSF' (Interactive Statistical Learning Functions) An educational toolkit for learning statistical concepts through interactive exploration. 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Package: r-cran-ubms Architecture: arm64 Version: 1.2.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 6049 Depends: libblas3 | libblas.so.3, 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-unmarked, r-cran-ggplot2, r-cran-gridextra, r-cran-loo, r-cran-matrix, r-cran-pbapply, r-cran-rcpp, r-cran-reformulas, r-cran-rlang, r-cran-rspectra, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcpparmadillo, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-raster, r-cran-rmarkdown, r-cran-terra, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ubms_1.2.9-1.ca2604.1_arm64.deb Size: 2484490 MD5sum: 178451a8db3547bb5effecb474909210 SHA1: c092f209932548551327feefd7168f741e405f2c SHA256: 4b295fcf0ccb8d8392fd5ebaf34c6cde4e2808004bb8b3abedabdc0caff3af79 SHA512: 49dfb17680d007b4da20d187a58d70a2fb2ac8b85d087d3df013c5e37f01091653887dcbda2a12d526185f91f309df5e5a6ceabcf8ad3f653e6899352081c82c Homepage: https://cran.r-project.org/package=ubms Description: CRAN Package 'ubms' (Bayesian Models for Data from Unmarked Animals using 'Stan') Fit Bayesian hierarchical models of animal abundance and occurrence via the 'rstan' package, the R interface to the 'Stan' C++ library. Supported models include single-season occupancy, dynamic occupancy, and N-mixture abundance models. Covariates on model parameters are specified using a formula-based interface similar to package 'unmarked', while also allowing for estimation of random slope and intercept terms. References: Carpenter et al. (2017) ; Fiske and Chandler (2011) . Package: r-cran-ucminf Architecture: arm64 Version: 1.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 127 Depends: libblas3 | libblas.so.3, libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-numderiv Filename: pool/dists/resolute/main/r-cran-ucminf_1.2.3-1.ca2604.1_arm64.deb Size: 35558 MD5sum: 4fcb27bed44cf0777f3bfd6afae17d62 SHA1: 6c66c3497514a50fdd1686d5aaa759a1825ca30f SHA256: 2043d2d365455b88a93a2b30d8fcbacc1e2460b913ac9cff1c944c64c46e5811 SHA512: 85eb9f050ee63791b3eebe6d48891747af52c11156a88c28ca3fa37e0a8ff5aa9d584e77cf144052ed86bb52e022c913f75eb4fc1c1ed35895568584a15051db Homepage: https://cran.r-project.org/package=ucminf Description: CRAN Package 'ucminf' (General-Purpose Unconstrained Non-Linear Optimization) An algorithm for general-purpose unconstrained non-linear optimization. The algorithm is of quasi-Newton type with BFGS updating of the inverse Hessian and soft line search with a trust region type monitoring of the input to the line search algorithm. The interface of 'ucminf' is designed for easy interchange with 'optim'. Package: r-cran-ucminfcpp Architecture: arm64 Version: 1.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 237 Depends: libc6 (>= 2.17), 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-ucminf, r-cran-microbenchmark Filename: pool/dists/resolute/main/r-cran-ucminfcpp_1.0.0-1.ca2604.1_arm64.deb Size: 92132 MD5sum: 38be72c43297727a6d7d39b328ada084 SHA1: 214c47d4f09faefd916e3cbb6990b6898292e950 SHA256: 3467ee2d78e1310ea703a0922f03a89c01cd0598dbd1fccb1a91fe14c11d4b9f SHA512: 1e90300c774bca62651073cd9eb66c291979ef94774bec2d129bcc7576187b2f812bcb3346a02423ccb1263499eb83335d2ce4e420d2d22dcd845221e4573a70 Homepage: https://cran.r-project.org/package=ucminfcpp Description: CRAN Package 'ucminfcpp' ('C++' Reimplementation of the 'ucminf' Unconstrained NonlinearOptimizer) A modern 'C++17/ reimplementation of the 'UCMINF/ algorithm for unconstrained nonlinear optimization (Nielsen and Mortensen, 2011, ), offering full API compatibility with the original 'ucminf' R package but developed independently. The optimizer core has been rewritten in 'C' with a modern header-only 'C++17' interface, zero-allocation line search, and an 'Rcpp' interface. The goal is numerical equivalence with improved performance, reproducibility, and extensibility. Includes extensive test coverage, performance regression tests, and compatibility checks against 'ucminf'. This package is not affiliated with the original maintainers but acknowledges their authorship of the algorithm and the original R interface. Package: r-cran-ucomp Architecture: arm64 Version: 5.3.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1870 Depends: libblas3 | libblas.so.3, libc6 (>= 2.43), libgcc-s1 (>= 4.0), 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-gridextra, r-cran-tsibble, r-cran-tsoutliers, r-cran-ggforce, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown Filename: pool/dists/resolute/main/r-cran-ucomp_5.3.1-1.ca2604.1_arm64.deb Size: 916884 MD5sum: f1207884efef48f6537ce4d09bd7aa64 SHA1: f51eaaec8350d4d3b71f5f7751be6ce9fa1cec9c SHA256: 551ea18905b1405d898bccafa293379e51082b3c95e4ef542a4f37ae182a4f55 SHA512: 8bc1996cb200974375986893868e053e475dbae5fff249874de0e53de57a0d78a3047a4c0f6d6e93db3d19cdf1fd3a8088f5c2fe80f5690a3fd95aefdab1396d Homepage: https://cran.r-project.org/package=UComp Description: CRAN Package 'UComp' (Automatic Univariate Time Series Modelling of many Kinds) Comprehensive analysis and forecasting of univariate time series using automatic time series models of many kinds. Harvey AC (1989) . Pedregal DJ and Young PC (2002) . Durbin J and Koopman SJ (2012) . Hyndman RJ, Koehler AB, Ord JK, and Snyder RD (2008) . Gómez V, Maravall A (2000) . Pedregal DJ, Trapero JR and Holgado E (2024) . 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Package: r-cran-ulid Architecture: arm64 Version: 0.4.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 186 Depends: libc6 (>= 2.17), 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-ulid_0.4.0-1.ca2604.1_arm64.deb Size: 49288 MD5sum: 9c183e403514f3259753155f9a93f574 SHA1: 955ac9daf49be634b6a774ab1c03c622b8982a3f SHA256: 18613119133a24abf7b05ec8fbeedf7ddca705b9d839b88159e764ec8e5c0b48 SHA512: af0c4514ebc840542ee7fecbdd04f69b3e42987a7f8db434202922f205510dffc5387bc08dc3d23ad28ba410eb9cce18cafa152c130517d6a7cccc5d4986df15 Homepage: https://cran.r-project.org/package=ulid Description: CRAN Package 'ulid' (Generate Universally Unique 'Lexicographically' 'Sortable'Identifiers) Universally unique identifiers ('UUIDs') can be sub-optimal for many uses-cases because they are not the most character efficient way of encoding 128 bits of randomness; v1/v2 versions are impractical in many environments, as they require access to a unique, stable MAC address; v3/v5 versions require a unique seed and produce randomly distributed IDs, which can cause fragmentation in many data structures; v4 provides no other information than randomness which can cause fragmentation in many data structures. 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The algorithm was described by McInnes and Healy (2018) in . This package provides an interface for two implementations. One is written from scratch, including components for nearest-neighbor search and for embedding. The second implementation is a wrapper for 'python' package 'umap-learn' (requires separate installation, see vignette for more details). Package: r-cran-umatrix Architecture: arm64 Version: 4.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 386 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-shiny, r-cran-shinyjs, r-cran-reshape2, r-cran-fields, r-cran-plyr, r-cran-png, r-cran-abind, r-cran-deldir, r-cran-geometry, r-cran-pdist, r-cran-adaptgauss, r-cran-datavisualizations, r-cran-ggrepel Suggests: r-cran-rgl Filename: pool/dists/resolute/main/r-cran-umatrix_4.0.2-1.ca2604.1_arm64.deb Size: 256462 MD5sum: bf6820090b5410799c84e02ac23b627f SHA1: 1af5cac181d1ffdb9ba9e31774658a8ac40248ec SHA256: b9fe0cec4c6baf9af2dbb468d182501bb7d5669293f8e638e83c173115585534 SHA512: 22169a16f5a1d0c593292ac590a45681b38954240b3f054d2d0d89ed9f9932b56f757ada60513850733a3df4961b74e164be3e99803402f9d2d044c453f69682 Homepage: https://cran.r-project.org/package=Umatrix Description: CRAN Package 'Umatrix' (Visualization of Structures in High-Dimensional Data) By gaining the property of emergence through self-organization, the enhancement of SOMs(self organizing maps) is called Emergent SOM (ESOM). The result of the projection by ESOM is a grid of neurons which can be visualised as a three dimensional landscape in form of the Umatrix. Further details can be found in the referenced publications (see url). This package offers tools for calculating and visualising the ESOM as well as Umatrix, Pmatrix and UStarMatrix. All the functionality is also available through graphical user interfaces implemented in 'shiny'. Based on the recognized data structures, the method can be used to generate new data. 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The method is based on the composite link model and estimation is achieved by maximizing a penalized likelihood. Smooth detailed sequences of counts and rates are so estimated from the binned counts. Ungrouping binned data can be desirable for many reasons: Bins can be too coarse to allow for accurate analysis; comparisons can be hindered when different grouping approaches are used in different histograms; and the last interval is often wide and open-ended and, thus, covers a lot of information in the tail area. Age-at-death distributions grouped in age classes and abridged life tables are examples of binned data. Because of modest assumptions, the approach is suitable for many demographic and epidemiological applications. For a detailed description of the method and applications see Rizzi et al. (2015) . 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Package: r-cran-varbvs Architecture: arm64 Version: 2.6-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2793 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_arm64.deb Size: 2471008 MD5sum: b1f3c8ce72ff2bc881a2a2d45be08a9e SHA1: 3559853217a87ae7247c270530cb3c8535790f77 SHA256: 5682e5fc95db142fe2f3c9bf03db3e3608b1be37e8cdc0186ba42fe896fc099c SHA512: 1f761e11d5ef56137fb0a0680cb3b9a5502918b67d00708ddb76a8e076259497e7a0e8bdb6d2bb7d4232b71e9e300e0175b13af7f09bce9ff42ad3d636414411 Homepage: https://cran.r-project.org/package=varbvs Description: CRAN Package 'varbvs' (Large-Scale Bayesian Variable Selection Using VariationalMethods) Fast algorithms for fitting Bayesian variable selection models and computing Bayes factors, in which the outcome (or response variable) is modeled using a linear regression or a logistic regression. The algorithms are based on the variational approximations described in "Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies" (P. Carbonetto & M. Stephens, 2012, ). This software has been applied to large data sets with over a million variables and thousands of samples. Package: r-cran-varpro Architecture: arm64 Version: 3.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 9227 Depends: libc6 (>= 2.17), libgomp1 (>= 4.9), r-base-core (>= 4.5.0), r-api-4.0, r-cran-randomforestsrc, r-cran-glmnet, r-cran-foreach, r-cran-gbm, r-cran-bart, r-cran-survival Suggests: r-cran-mlbench, r-cran-domc, r-cran-caret, r-cran-mass, r-cran-igraph Filename: pool/dists/resolute/main/r-cran-varpro_3.1.0-1.ca2604.1_arm64.deb Size: 9261688 MD5sum: e003d39e61dcd62cda96e9d137e99de7 SHA1: c81f0a4781fc5366519097f60f405dfdb548253c SHA256: 65db16669540b2bac275449de380659feb0f5a0e937e80bd35df4436967ca3ae SHA512: f99964fadcf5a68d01fd69e73aa12d7109c30d0e21e3f24651bf065a2fafb3c3d06376d6b2a7a437e3fe181dea3893ec65801b204f98e6e38b14976a5a90d2df Homepage: https://cran.r-project.org/package=varPro Description: CRAN Package 'varPro' (Model-Independent Variable Selection via the Rule-Based VariablePriority) A new framework of variable selection, which instead of generating artificial covariates such as permutation importance and knockoffs, creates release rules to examine the affect on the response for each covariate where the conditional distribution of the response variable can be arbitrary and unknown. Package: r-cran-varselectexposure Architecture: arm64 Version: 1.0.3-1.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-rcpparmadillo Filename: pool/dists/resolute/main/r-cran-varselectexposure_1.0.3-1.ca2604.1_arm64.deb Size: 77652 MD5sum: da03b5b380eef38458500a042ef8313f SHA1: ed6cb6f5b4add42ad5bd324c015d146a254c7169 SHA256: cbdde45529a1da0ba18e68e55bd08a32beeb835be6f77194ae705473b8e31a49 SHA512: c5d3e4f3e822406a531b702748f5b436a10a309217c3c9031a66c7d63ee6eb8cb0ef0ca78e58ad9222842cec602db07c6dc61f53f3b42dc60d958f48fa6f0f86 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: arm64 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_arm64.deb Size: 294018 MD5sum: 3a9efea451ec5a2086770a5b8656fdfb SHA1: a4a48d2ca63895a9bffb6349743b8746d0556d14 SHA256: 82d5442e3c8ac0fb626171a6d9a5ae25af265c2f0588780c7ac1fb215c1d4c4d SHA512: 0eaddc36299303cf6ab42d8875351befdf17bee25fee03cce3697f66e0e283deeb7dd764ff995e5d32e8ef003607aa77671d80abd5a2423689444fd6d2504acf 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: arm64 Version: 1.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 223 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_arm64.deb Size: 90398 MD5sum: 7d29085830d82693b2c51d507a061c79 SHA1: a28b4acc5716cdd8865a08f83e44412cb142e095 SHA256: 6ef4dd1c48aa1a84d2f9ab277472f77972f33041dd94a468c7ae52dbda14f894 SHA512: cc354f25bfcc2cdff78d9cea67ebe4b908712f36899860602d7b658ae8a1079fdb851376f844c6036cc9e8518c6fcbf708f0daacbee23dd847909e0eb7b2f05f Homepage: https://cran.r-project.org/package=vasicekreg Description: CRAN Package 'vasicekreg' (Regression Modeling Using Vasicek Distribution) Provides probability density, cumulative distribution, quantile, and random number generation functions for the Vasicek distribution. In addition, two functions are available for fitting Generalized Additive Models for Location, Scale and Shape introduced by Rigby and Stasinopoulos (2005, ). Some functions are written in 'C++' using 'Rcpp', developed by Eddelbuettel and Francois (2011, ). Package: r-cran-vaster Architecture: arm64 Version: 0.6.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 756 Depends: r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-spelling, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-vaster_0.6.0-1.ca2604.1_arm64.deb Size: 435428 MD5sum: b5ca29d804d72069125f36e9288a7d3b SHA1: 1a58bd23971bf43403978681d796d328fe96d512 SHA256: d4c569f479dacfde823a72308451dfd58c32c78f2db2c7947c286fdd3e857d8c SHA512: fef89a12095155baa7a2afb042361f2b3de1a8dfa16d71b0cfca9fdd238c20befe8386164793ea1c45cf07a0096206c64637f058c921ee1f531f165f71298abc Homepage: https://cran.r-project.org/package=vaster Description: CRAN Package 'vaster' (Tools for Raster Grid Logic) Provides raster grid logic, operations that describe a discretized rectangular domain and do not require access to materialized data. 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: arm64 Version: 1.1.7-1.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-rcppeigen Suggests: r-cran-mvtnorm, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-vbel_1.1.7-1.ca2604.1_arm64.deb Size: 105966 MD5sum: 7c56f8bb426615a95bafcac2dd51bdd7 SHA1: 73f31b8c719b7fb03ad049f2d039028b9240dbec SHA256: 1f8d36c7f90ccb5d0a47837ba47db577811e6862bc32a5e14bee4b67dce97d5a SHA512: 0b696bd9efdb36cb828f37ae89040d5676f24bdc76ba223fce42cde1c84f0709d2eff284349064f96a3bf70231e483c0994bdb8868924d41bde0ebb87e8eec1f 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) . Package: r-cran-vblpcm Architecture: arm64 Version: 2.4.9-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 239 Depends: libc6 (>= 2.29), libgsl28 (>= 2.8+dfsg), r-base-core (>= 4.5.0), r-api-4.0, r-cran-ergm, r-cran-network, r-cran-mclust, r-cran-sna Filename: pool/dists/resolute/main/r-cran-vblpcm_2.4.9-1.ca2604.1_arm64.deb Size: 153890 MD5sum: 6c1a09b99ddc6ddbcefe6cfe7997f859 SHA1: f5414508f927e7c6adf11c6889280d6d28b136ba SHA256: 33b8e9137e529e46b63cb49db43f5649493ace8bf0bdab092e5f30e8bb5c1372 SHA512: 0e3326f381a7e8ebb156ca14214353a3301a2df525b5e315666da05bf6f377338dd11931150fdcc6843a10c51185469ee3038c555957d31c9cd2585244416550 Homepage: https://cran.r-project.org/package=VBLPCM Description: CRAN Package 'VBLPCM' (Variational Bayes Latent Position Cluster Model for Networks) Fit and simulate latent position and cluster models for network data, using a fast Variational Bayes approximation developed in Salter-Townshend and Murphy (2013) . Package: r-cran-vc2copula Architecture: arm64 Version: 0.1.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 681 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_arm64.deb Size: 402332 MD5sum: 5417f91f0db368b32569d85d14b0e353 SHA1: 3a64d272a23b785aff97a038f0f46ded52f6b037 SHA256: ed9b099409de1deed04f7cd57746d38959613ba9d16ec52ad3c0bd0ec099303e SHA512: 245fc483b303cd3ae09451cd45210c26b20441f6b99189322c6621818fbe7db85d478fab89b3a7607d0c25901aefeaee1c20b5f095fdc4b7405322399145cb12 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: arm64 Version: 1.5.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1342 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 973696 MD5sum: b12277252e38d165cbeb9dc6b6a49bc6 SHA1: 027a2ccebf7de286fdefb5f5dd02d7e251fd5760 SHA256: 92ed6b680f3d30dbf5aa26663a2523c14af7322273dcbf743154db2f4d56fb66 SHA512: 3a16120ea47f3c840d5eae1e20331d070d8ba01d901bef605dc9b718a472e22c21c7903d9520b262991f245bf2100ee36f1a7545dd150c660166e4dd0218a45a 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: arm64 Version: 1.2.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 537 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgomp1 (>= 4.2.1), 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_arm64.deb Size: 209058 MD5sum: 914cd02aef9b32c8c13bab54b4042126 SHA1: c774d93d392effa54342bb3ae4e75a06a252cf2c SHA256: b83d6fb4bcc9950f907ad947298f22e62c767a9a2bea9f71e44361d412d1b6d1 SHA512: b72e5af6d7631d4c0106c41ec99599923ff6dd90859d1a6fbd9caddbb32aa04b2bf8c29c4208dd226c0510beacfae7c9e3f0d432aefc0da68fda801d0dd9518d 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. 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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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At the heart of this package are the vector generalized linear and additive model (VGLM/VGAM) classes. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are data-driven VGLMs that use smoothing. The book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) gives details of the statistical framework and the package. Currently only fixed-effects models are implemented. Many (100+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE. The other classes are RR-VGLMs (reduced-rank VGLMs), quadratic RR-VGLMs, doubly constrained RR-VGLMs, quadratic RR-VGLMs, reduced-rank VGAMs, RCIMs (row-column interaction models)---these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO). Hauck-Donner effect detection is implemented. Note that these functions are subject to change; see the NEWS and ChangeLog files for latest changes. Package: r-cran-vgamextra Architecture: arm64 Version: 0.0-9-1.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_arm64.deb Size: 1037692 MD5sum: 73a2cd4f8ef35f5dc83b11be4dd7fa18 SHA1: 2a82d1565ca6c80b25bb0ec7f824e71f0c4bbcfd SHA256: cbe7b641a09405eb3cbd818ab7cfcf77f238e60587b4e63e6ebbbd2a19184c2f SHA512: 937d11805d4b727469ab965083422a32d434b2b1649ec7f0be586b5b1b7cd69dc643393491e12cd4f373142d2ca31742abb3637c8c8c880adcc0aec486238591 Homepage: https://cran.r-project.org/package=VGAMextra Description: CRAN Package 'VGAMextra' (Additions and Extensions of the 'VGAM' Package) Extending the functionalities of the 'VGAM' package with additional functions and datasets. At present, 'VGAMextra' comprises new family functions (ffs) to estimate several time series models by maximum likelihood using Fisher scoring, unlike popular packages in CRAN relying on optim(), including ARMA-GARCH-like models, the Order-(p, d, q) ARIMAX model (non- seasonal), the Order-(p) VAR model, error correction models for cointegrated time series, and ARMA-structures with Student-t errors. For independent data, new ffs to estimate the inverse- Weibull, the inverse-gamma, the generalized beta of the second kind and the general multivariate normal distributions are available. In addition, 'VGAMextra' incorporates new VGLM-links for the mean-function, and the quantile-function (as an alternative to ordinary quantile modelling) of several 1-parameter distributions, that are compatible with the class of VGLM/VGAM family functions. Currently, only fixed-effects models are implemented. All functions are subject to change; see the NEWS for further details on the latest changes. Package: r-cran-vglmer Architecture: arm64 Version: 1.0.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 678 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 432132 MD5sum: 224df193f27611ad3a3ee8e326841002 SHA1: 4a758d0e1cf5f36ad6d8fe712c326063bb4040c2 SHA256: 5a5463624d90c06c23c627b15246dd60ac78dfb1287936a1b1e8c88a2cadd954 SHA512: 84a74a0d34efb1b0f7a09c740f7dfe655a197f03777d152a3730b12ab190219533053d8eb2d6e0785885a8e49922a304cecdad6e4bb049e29444aec0932483b3 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. At present, it can estimate logistic, linear, and negative binomial models. It can accommodate models with an arbitrary number of random effects and requires no integration to estimate. It also provides the ability to improve the quality of the approximation using marginal augmentation. Goplerud (2022) and Goplerud (2024) provide details on the variational algorithms. Package: r-cran-vic5 Architecture: arm64 Version: 0.2.6-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1228 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_arm64.deb Size: 788476 MD5sum: 8768a5b85ba8f97d2665fc96f91d4589 SHA1: 1f4ee8611c4d928ad1cffd29519a72712b10d7ff SHA256: fa5b94c332e5d0adf1975f84b37ec1639ee7c7025f80b10824f7731aa5bff1c2 SHA512: d805dd25f52601917a676384e5d1aa8096eb6ac1ceb7d948d445e742ce018214c07c3a230bf438094eb4f106ec47e75185bd3b9a5c380201c562932fa9fa3865 Homepage: https://cran.r-project.org/package=VIC5 Description: CRAN Package 'VIC5' (The Variable Infiltration Capacity (VIC) Hydrological Model) The Variable Infiltration Capacity (VIC) model is a macroscale hydrologic model that solves full water and energy balances, originally developed by Xu Liang at the University of Washington (UW). The version of VIC source code used is of 5.0.1 on , see Hamman et al. (2018). Development and maintenance of the current official version of the VIC model at present is led by the UW Hydro (Computational Hydrology group) in the Department of Civil and Environmental Engineering at UW. VIC is a research model and in its various forms it has been applied to most of the major river basins around the world, as well as globally . References: "Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges (1994), A simple hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res., 99(D7), 14415-14428, "; "Hamman, J. J., Nijssen, B., Bohn, T. J., Gergel, D. R., and Mao, Y. (2018), The Variable Infiltration Capacity model version 5 (VIC-5): infrastructure improvements for new applications and reproducibility, Geosci. Model Dev., 11, 3481-3496, ". Package: r-cran-vicatmix Architecture: arm64 Version: 1.0-1.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-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_arm64.deb Size: 154654 MD5sum: e51fc964ff5414189c8f1f19d65a9361 SHA1: b1f8ede2154f038c4ba3d5a37109c5522c1ddd88 SHA256: 4104b040251b6641389b339757a99c07f1d15a454876beefe6b500265d76e3da SHA512: 75033ea248199d938172de8caaef68e4178b3270d196823b29b960bf41ada5a8b3989e203ff5daaafec684e91eeff152d0b565430a66d7398c90efb0735bdaef 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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(2020) . The algorithm of computing viewshed is based on the work of Franklin & Ray. (1994) . Package: r-cran-vigor Architecture: arm64 Version: 1.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 345 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Filename: pool/dists/resolute/main/r-cran-vigor_1.1.5-1.ca2604.1_arm64.deb Size: 266426 MD5sum: 6b9ee48d59b83703808b461ff2a7dcb9 SHA1: d355ac316d51b396627c559955b19f822af94c07 SHA256: 05aaa9536e5b3a861a629a873ccb0b85e192c408158f93efafdee88962de68b7 SHA512: a63188cf7f6f41629554d7e1074e338af29534d78536e196078e8959b3f413cecdb85eae15846f46d201a318ec4adc862fdde7b087ec1ec10d2cd587a6f54cef 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: arm64 Version: 7.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7179 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 3599820 MD5sum: 4943e384578517c4680a86446942db00 SHA1: dbc1aba566981664313dfdc0fe82e3c2dafecc13 SHA256: cfa0cbe6f3d0b7fa5a76d00442265f1e6dfb5050d95254a721ca16794526174e SHA512: df84d47769bbcbb9a74ae6f5cdaf520912fa7e18d0909505390431334704652f2ea73af41a1ee6e29d6e482a90889115c227b73bb663ff030b20059a59e6ef46 Homepage: https://cran.r-project.org/package=VIM Description: CRAN Package 'VIM' (Visualization and Imputation of Missing Values) Provides methods for imputation and visualization of missing values. It includes graphical tools to explore the amount, structure and patterns of missing and/or imputed values, supporting exploratory data analysis and helping to investigate potential missingness mechanisms (details in Alfons, Templ and Filzmoser, . The quality of imputations can be assessed visually using a wide range of univariate, bivariate and multivariate plots. The package further provides several imputation methods, including efficient implementations of k-nearest neighbour and hot-deck imputation (Kowarik and Templ 2013, , iterative robust model-based multiple imputation (Templ 2011, ; Templ 2023, ), and machine learning–based approaches such as robust GAM-based multiple imputation (Templ 2024, ) as well as gradient boosting (XGBoost) and transformer-based methods (Niederhametner et al., ). General background and practical guidance on imputation are provided in the Springer book by Templ (2023) . 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It enables efficient clustering of high-dimensional data with significantly improved computational speed than traditional MCMC methods. The package incorporates 8 parameterisations and corresponding prior choices for the unknown covariance matrix, from which the user can choose and apply accordingly. Package: r-cran-vinecopula Architecture: arm64 Version: 2.6.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1461 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mass, r-cran-mvtnorm, r-cran-adgoftest, r-cran-lattice Suggests: r-cran-tsp, r-cran-shiny, r-cran-testthat, r-cran-numderiv, r-cran-kdecopula, r-cran-network Filename: pool/dists/resolute/main/r-cran-vinecopula_2.6.1-1.ca2604.1_arm64.deb Size: 1180896 MD5sum: 4cd2595f1fdf3a7d3de43f0df3a60ca0 SHA1: 27e6a10552d4750950899b3dfa2c5a076b99c7af SHA256: becb03f467c08874c9c65a4a203234238332914552cdbe8b7f01c362b1c78373 SHA512: 3f55b6b9e477118a4abd7a1d563dcf5b79fb1cd7b258f85161fcfdc8566323aee91de4895df4f8eb3207818ae12bb10cb9e0cb848221fd54053865e465f956fc Homepage: https://cran.r-project.org/package=VineCopula Description: CRAN Package 'VineCopula' (Statistical Inference of Vine Copulas) Provides tools for the statistical analysis of regular vine copula models, see Aas et al. 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See Kraus and Czado (2017) and Schallhorn et al. (2017) . 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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: arm64 Version: 1.1.2-10-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2723 Depends: libblas3 | libblas.so.3, libc6 (>= 2.38), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 986506 MD5sum: ea50637e49a4c20c10cafe7e7ddbb0fc SHA1: 6dafb478eccc2af3f185607a4caa2eda06309ac0 SHA256: c9e2ede929cde7ea52ce203910a8e8b8b522f4f7a740e845c5c2461f22e8ccda SHA512: 06adb4c551ee4ba766ca384bfa37d46688d4586e4f62a7aa3655c9a079e2672fd2fedba56885c63435ef2135913e8a7bb6183a66f7c93556c39d5674e9b3d878 Homepage: https://cran.r-project.org/package=volesti Description: CRAN Package 'volesti' (Volume Approximation and Sampling of Convex Polytopes) Provides an R interface for 'volesti' C++ package. 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'warbleR' makes use of the basic sound analysis tools from the packages 'tuneR' and 'seewave', and offers new tools for exploring and quantifying acoustic signal structure. The package allows to organize and manipulate multiple sound files, create spectrograms of complete recordings or individual signals in different formats, run several measures of acoustic structure, and characterize different structural levels in acoustic signals (Araya-Salas et al 2016 ). 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The package focuses on cost-effectiveness modelling and aims to be submission-ready to relevant HTA bodies in alignment with 'NICE TSD 15' . More details an examples can be found in the package website . 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Package: r-cran-warp Architecture: arm64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 179 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0 Suggests: r-cran-covr, r-cran-knitr, r-cran-rmarkdown, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-warp_0.2.3-1.ca2604.1_arm64.deb Size: 62424 MD5sum: baaf79be1c962f0e97496623f4dc7598 SHA1: 3da54d7a364abe0dda14d3bdb1128f2ddb71be28 SHA256: 86cfde34a4ee69e1c499b91361f1f07fda54bfd7f7f50f51cc45c5a8cc3c29c9 SHA512: 28558cc48e318a92a4a363d2214836738dc30d05d7244ee9d75790a64fe1ff1c3151b321d18dc4758f684a2e832c942db317de10c72d76a4ba7b8405bdb6087e Homepage: https://cran.r-project.org/package=warp Description: CRAN Package 'warp' (Group Dates) Tooling to group dates by a variety of periods including: yearly, monthly, by second, by week of the month, and more. The groups are defined in such a way that they also represent the distance between dates in terms of the period. 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The Wasserstein barycenter is a geometric approach for combining subset posteriors. It allows for parallel and distributed computation of the posterior in case of complex models and/or big datasets, thereby increasing computational speed tremendously. 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The 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) . 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Package: r-cran-waveslim Architecture: arm64 Version: 1.8.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 879 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 764626 MD5sum: 2dca869c98c9de644b7155657cf63ced SHA1: e4e7b8684559bdd23377cd96a32a0995614e8264 SHA256: d26d9970b87b3232d6635ebbb9301f2d647debd7b47786221597e87f203e64f1 SHA512: adc008fccfb987fba7543648b141950b9e3bd7ac980316b22ca7669982659a77e06d4778f365861a846f5debca0133ecd3e8334cb80adb643e7b1f72321df06a Homepage: https://cran.r-project.org/package=waveslim Description: CRAN Package 'waveslim' (Basic Wavelet Routines for One-, Two-, and Three-DimensionalSignal Processing) Basic wavelet routines for time series (1D), image (2D) and array (3D) analysis. The code provided here is based on wavelet methodology developed in Percival and Walden (2000); Gencay, Selcuk and Whitcher (2001); the dual-tree complex wavelet transform (DTCWT) from Kingsbury (1999, 2001) as implemented by Selesnick; and Hilbert wavelet pairs (Selesnick 2001, 2002). All figures in chapters 4-7 of GSW (2001) are reproducible using this package and R code available at the book website(s) below. 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Package: r-cran-wbsd Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 226 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 107376 MD5sum: 137a7945ce6e0e7272f76f9841411d50 SHA1: abf62d87c9adf96a7afd11aad7dd2f916f04f54e SHA256: 79640acc5303545a777039e1bdd5a3587a8b56f8f984f48a264596e2f7235711 SHA512: 9a94aa6cc9ec1439c2aba96231ec6109b608c12b9b4786a80e7cd056408ec84db90863d10d335f951f3fde5f60b5a39d3d91bb5c884db90102e982ef8986a6a9 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. 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Package: r-cran-wbsts Architecture: arm64 Version: 2.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 238 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-mvtnorm, r-cran-wavelets, r-cran-rcpp Filename: pool/dists/resolute/main/r-cran-wbsts_2.1-1.ca2604.1_arm64.deb Size: 113044 MD5sum: 17201cdf16d749ba9ff4aeccfb414814 SHA1: 8f0a4982f19127f081c3814ed5b6cafd0a4a6392 SHA256: 8c8dae4262985d3c312cc815f47b0f8b468c178d422f20227e81af13f4cf06f8 SHA512: df0a3d3c7e4acb6a1c978fb38bbd8d81bd495428112141d1d3a471e87c47781af3249cfd8ed02ec36bc9c58990e28c69aa0003bb74c14b1d457148070ff7ed89 Homepage: https://cran.r-project.org/package=wbsts Description: CRAN Package 'wbsts' (Multiple Change-Point Detection for Nonstationary Time Series) Implements detection for the number and locations of the change-points in a time series using the Wild Binary Segmentation and the Locally Stationary Wavelet model of Korkas and Fryzlewicz (2017) . 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'WebSocket' is a protocol for low-overhead real-time communication: . Package: r-cran-webutils Architecture: arm64 Version: 1.2.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 133 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 35670 MD5sum: 3dd8a29ceed8d9b803f7c71d70c60d0e SHA1: dc47fed124c3084d53940f65699edd31176196c0 SHA256: dd4132a9f1058faf12b632642c981e237186c301661795734ca9849838b02483 SHA512: 67d242494e94563ea799228fdf1e69c80a9a4b59e3bc98547475d2d1d9ff44118a70f7f427f706ca6de44a86a0c3129440d863f59ea7347d911aa6e86f54358a Homepage: https://cran.r-project.org/package=webutils Description: CRAN Package 'webutils' (Utility Functions for Developing Web Applications) Parses http request data in application/json, multipart/form-data, or application/x-www-form-urlencoded format. Includes example of hosting and parsing html form data in R using either 'httpuv' or 'Rhttpd'. Package: r-cran-weibullr Architecture: arm64 Version: 1.2.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 807 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 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_arm64.deb Size: 555162 MD5sum: 27bd4f6f0afa2fde3f51ce1d322140a3 SHA1: 1910de7d889128584dc3ff04ac3d0def69e02dd1 SHA256: 91acb973866f19ac5f7ae6f62d2f20b0af93c8d95ccd463a15fd608c49c868b7 SHA512: 1417bc9d4eca6333098765189b719056cd433ff2c74f84c48787ab8f0c7ca6d6077d0898a4051e41a6618ed6200c03afbddfb78866019134f126cfea4bb5f9cc Homepage: https://cran.r-project.org/package=WeibullR Description: CRAN Package 'WeibullR' (Weibull Analysis for Reliability Engineering) Life data analysis in the graphical tradition of Waloddi Weibull. Methods derived from Robert B. Abernethy (2008, ISBN 0-965306-3-2), Wayne Nelson (1982, ISBN: 9780471094586), William Q. Meeker and Lois A. Escobar (1998, ISBN: 1-471-14328-6), John I. McCool, (2012, ISBN: 9781118217986). Package: r-cran-weibulltools Architecture: arm64 Version: 2.1.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1358 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-dplyr, r-cran-ggplot2, r-cran-lifecycle, r-cran-magrittr, r-cran-plotly, r-cran-purrr, r-cran-rcpp, r-cran-sandwich, r-cran-segmented, r-cran-tibble, r-cran-rcpparmadillo Suggests: r-cran-knitr, r-cran-rmarkdown, r-cran-testthat, r-cran-pillar Filename: pool/dists/resolute/main/r-cran-weibulltools_2.1.0-1.ca2604.1_arm64.deb Size: 893646 MD5sum: e74d402ad36c5d468b2e2dc00fba7267 SHA1: f71e3476de4e9125721930f59e84a5bfdc8aa541 SHA256: b4e521b934b721eb0f42985866ea1e4bfd6f9b8c37b3d357d14b7bfbeda8fc0e SHA512: 458a8220521abc80e8c1f4e96aa20ad9f801fba6aa8e10e793902982eaf22ecd388f4c4547565a538202510a62663afe39c91f3121dd0a9f5555e7bf43ccc09b Homepage: https://cran.r-project.org/package=weibulltools Description: CRAN Package 'weibulltools' (Statistical Methods for Life Data Analysis) Provides statistical methods and visualizations that are often used in reliability engineering. Comprises a compact and easily accessible set of methods and visualization tools that make the examination and adjustment as well as the analysis and interpretation of field data (and bench tests) as simple as possible. Non-parametric estimators like Median Ranks, Kaplan-Meier (Abernethy, 2006, ), Johnson (Johnson, 1964, ), and Nelson-Aalen for failure probability estimation within samples that contain failures as well as censored data are included. The package supports methods like Maximum Likelihood and Rank Regression, (Genschel and Meeker, 2010, ) for the estimation of multiple parametric lifetime distributions, as well as the computation of confidence intervals of quantiles and probabilities using the delta method related to Fisher's confidence intervals (Meeker and Escobar, 1998, ) and the beta-binomial confidence bounds. If desired, mixture model analysis can be done with segmented regression and the EM algorithm. Besides the well-known Weibull analysis, the package also contains Monte Carlo methods for the correction and completion of imprecisely recorded or unknown lifetime characteristics. (Verband der Automobilindustrie e.V. (VDA), 2016, ). Plots are created statically ('ggplot2') or interactively ('plotly') and can be customized with functions of the respective visualization package. The graphical technique of probability plotting as well as the addition of regression lines and confidence bounds to existing plots are supported. Package: r-cran-weightedcl Architecture: arm64 Version: 0.7-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 218 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_arm64.deb Size: 127030 MD5sum: 292d1be4bc9e66bf18550fa1db4cee01 SHA1: 4db890d4ba612b4da66cc83d7cac5d408aa899a8 SHA256: bef36b43cf7a932d38ce7d0fd495189e3ac2f4a72ea33059c16b1e2257a8fd98 SHA512: b9df9f508b95c986a1daee13ca368210b81d8229370b9ffe58400673cfe3504dec3a08e6b085dc3e9befb7ffd43e3012d2c4921f800f73fb720862be97f57476 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) . It provides autoregressive moving average correlation structures and binary, ordinal, Poisson, and negative binomial regressions. 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It provides an optimized weighted PAM algorithm as well as functions for aggregating replicated cases, computing cluster quality measures for a range of clustering solutions, sequence analysis typology validation using parametric bootstraps and plotting (fuzzy) clusters of state sequences. It further provides a fuzzy and crisp CLARA algorithm to cluster large database with sequence analysis, and a methodological framework for Robustness Assessment of Regressions using Cluster Analysis Typologies (RARCAT). Package: r-cran-weightedscores Architecture: arm64 Version: 0.9.5.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 355 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 277846 MD5sum: cc4918b30492b7b1f84e6b9df31bf0cb SHA1: 91a161d0f7647c93efb9a9fac2e9454c99dabbf6 SHA256: d04f37541e8a4ccaf4f93e25ba5f2237eb450a56cfbd83e2844fa63a1cdedd0f SHA512: 7e0be52a8862816ee0e93642296250a55d85d247912fb75f09e6dd75c0c83617269d250eb21317c9ff04a4cc854c08e2bb1359a436f3afa8d40c1c0a21d21198 Homepage: https://cran.r-project.org/package=weightedScores Description: CRAN Package 'weightedScores' (Weighted Scores Method for Regression Models with Dependent Data) The weighted scores method and composite likelihood information criteria as an intermediate step for variable/correlation selection for longitudinal ordinal and count data in Nikoloulopoulos, Joe and Chaganty (2011) , Nikoloulopoulos (2016) and Nikoloulopoulos (2017) . Package: r-cran-weightedtreemaps Architecture: arm64 Version: 0.1.4-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2789 Depends: libc6 (>= 2.43), libgcc-s1 (>= 4.5), 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_arm64.deb Size: 1950738 MD5sum: 76561fbf508bc6353ef10da3155b3b16 SHA1: 2b755293561ca359022e0ce170b395f2a79c7417 SHA256: 5ac132b43d76e6c7f58e94342bb6168e475469d7dba89ca6e38dc8e0e41447e0 SHA512: de03593babd306fe9d42544f4e8037a87b7ec76ae85748c1891a740413096876cc0d901f8ed99452139173b8fe214b7daf99a1395d2a2fe37d7dd7fe9b3a7ca6 Homepage: https://cran.r-project.org/package=WeightedTreemaps Description: CRAN Package 'WeightedTreemaps' (Generate and Plot Voronoi or Sunburst Treemaps from HierarchicalData) Treemaps are a visually appealing graphical representation of numerical data using a space-filling approach. A plane or 'map' is subdivided into smaller areas called cells. The cells in the map are scaled according to an underlying metric which allows to grasp the hierarchical organization and relative importance of many objects at once. This package contains two different implementations of treemaps, Voronoi treemaps and Sunburst treemaps. The Voronoi treemap function subdivides the plot area in polygonal cells according to the highest hierarchical level, then continues to subdivide those parental cells on the next lower hierarchical level, and so on. The Sunburst treemap is a computationally less demanding treemap that does not require iterative refinement, but simply generates circle sectors that are sized according to predefined weights. The Voronoi tesselation is based on functions from Paul Murrell (2012) . Package: r-cran-weights Architecture: arm64 Version: 1.1.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 368 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_arm64.deb Size: 263550 MD5sum: dff698605ce78fba462ac6bdf6b7ce8c SHA1: 56bc22915af88798f22eeaf720659f42e26dfc1d SHA256: cc70b1bcabebd9385b9ff9d8a16e5887507e777ec4cddf5a3bcc6c843e7272d5 SHA512: d860b340fcd4ffcd45f3081dca3a3878f3f879aa926b3a4bbca5201f9e263af2cc84477a79a26dc101886a9ad996b0ee0c752eab5cd593c4fcbd563ca6a9355b Homepage: https://cran.r-project.org/package=weights Description: CRAN Package 'weights' (Weighting and Weighted Statistics) Provides a variety of functions for producing simple weighted statistics, such as weighted Pearson's correlations, partial correlations, Chi-Squared statistics, histograms, and t-tests as well as simple weighting graphics including weighted histograms, box plots, bar plots, and violin plots. Also includes software for quickly recoding survey data and plotting estimates from interaction terms in regressions (and multiply imputed regressions) both with and without weights and summarizing various types of regressions. Some portions of this package were assisted by AI-generated suggestions using OpenAI's GPT model, with human review and integration. Package: r-cran-weightsvm Architecture: arm64 Version: 1.7-16-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 349 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.0), libstdc++6 (>= 5), r-base-core (>= 4.5.0), 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_arm64.deb Size: 264626 MD5sum: 1981f89625840d4519de40f89dfe0ef9 SHA1: c5c544316ee188901d8ace2343f56005bf990597 SHA256: 60da9016725f7d0e61a3e9d2d82768a8918094540d6d464603f7e041f266dc6d SHA512: d848aa3a0b29e80e79a80b3b4ca999032db0166e0cd6bbb5f28691213133dbeefa055814a26d744dc706c35d204a640e9ade8022fa8ad5ebeecdb5f70688085b Homepage: https://cran.r-project.org/package=WeightSVM Description: CRAN Package 'WeightSVM' (Subject Weighted Support Vector Machines) Functions for subject/instance weighted support vector machines (SVM). 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Package: r-cran-wfe Architecture: arm64 Version: 1.9.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 228 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 157374 MD5sum: 61c1f44264d3f59fb76b47ed9f8c5a3c SHA1: fd9f6b5149a9149c8e3607dade11da5c0234af4b SHA256: 5ee2886da0e445cc764a564c533fd4911d23a10bbc7ccf7b58633a470fa2f4e7 SHA512: 496e1ee15b26bcf6fb04b508ea7f571592e7590b043e4e4e8e359a9adf1550ed3f5398cc67f0792789732dcdeeed2cbb1107f7150c6d632c6e2278495308609c Homepage: https://cran.r-project.org/package=wfe Description: CRAN Package 'wfe' (Weighted Linear Fixed Effects Regression Models for CausalInference) Provides a computationally efficient way of fitting weighted linear fixed effects estimators for causal inference with various weighting schemes. Weighted linear fixed effects estimators can be used to estimate the average treatment effects under different identification strategies. This includes stratified randomized experiments, matching and stratification for observational studies, first differencing, and difference-in-differences. The package implements methods described in Imai and Kim (2017) "When should We Use Linear Fixed Effects Regression Models for Causal Inference with Longitudinal Data?", available at . Package: r-cran-wgcna Architecture: arm64 Version: 1.74-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 3116 Depends: libc6 (>= 2.34), libgcc-s1 (>= 4.2), 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_arm64.deb Size: 2912574 MD5sum: faad21f3a320586fd94f3a91eba0e192 SHA1: 822eefb1be9664bebb76b5b29b72fe4a0d062edb SHA256: e5733fe9568b0faa1484a3272ad0aa114db37daad0bb63fe4c2dc7caa7e6c70c SHA512: 40341bb423b696ad88cf6ee34fea2cc86c5a8041baeb58c03b95ac547ccb398687d1fd650637a711d5e93697b3711b9d27c7ccc3a747d4adf04d9438c1832f5d Homepage: https://cran.r-project.org/package=WGCNA Description: CRAN Package 'WGCNA' (Weighted Correlation Network Analysis) Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) and Langfelder and Horvath (2008) . Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization. Package: r-cran-wh Architecture: arm64 Version: 2.0.0-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 476 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 285120 MD5sum: 61dc7c0074e933c79ca46d30ac826726 SHA1: e9ee5472e6052b529775b6846b611eefa9291223 SHA256: 91eab87f1767488afdc021dd47941d5f7d5436abd43423e1b05007740dee793a SHA512: 2b2a8e55b50b11f1130b9de860a0d8f3cc34a5cd97f171cfba2df373a7b9e5d25b2c1d0801372afdc6e38caeed277959e0e0a9d6bb85851b50fb4f5930f6035b Homepage: https://cran.r-project.org/package=WH Description: CRAN Package 'WH' (Enhanced Implementation of Whittaker-Henderson Smoothing) An enhanced implementation of Whittaker-Henderson smoothing for the graduation of one-dimensional and two-dimensional actuarial tables used to quantify Life Insurance risks. 'WH' is based on the methods described in Biessy (2025) . Among other features, it generalizes the original smoothing algorithm to maximum likelihood estimation, automatically selects the smoothing parameter(s) and extrapolates beyond the range of data. Package: r-cran-whitelabrt Architecture: arm64 Version: 1.0.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 7569 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_arm64.deb Size: 5060606 MD5sum: ad85d76d89e6f4f52cafa9ec0806f282 SHA1: 9f08d279a376d14ac45333715d51950d9c1c1c68 SHA256: fadd0fc6b1e248ee09197e156251485b4f1b1645d6549afc6437fbbaeaf7d625 SHA512: b3a1a51c8a6de0901789bc19affc82a744c82bb3d01dfe5d45fb5e8ffabb0169a68d3d7bd18d237a881d973360071454f64ac612d7bdc681461bd917995649ab Homepage: https://cran.r-project.org/package=WhiteLabRt Description: CRAN Package 'WhiteLabRt' (Novel Methods for Reproduction Number Estimation,Back-Calculation, and Forecasting) A collection of functions related to novel methods for estimating R(t), created by the lab of Professor Laura White. Currently implemented methods include two-step Bayesian back-calculation and now-casting for line-list data with missing reporting delays, adapted in 'STAN' from Li (2021) , and calculation of time-varying reproduction number assuming a flux between various adjacent states, adapted into 'STAN' from Zhou (2021) . Package: r-cran-whoa Architecture: arm64 Version: 0.0.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 641 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 467042 MD5sum: 2cfdd69d42df066dcd12695e9e889e69 SHA1: 0726f74019955078405595fb8b9bb83b2343ab47 SHA256: 0610d063d4a2426c192f290eb0a1ba245a57f4a8caf7cc4e35c401e92f77501e SHA512: 0bc99ccfcdd6ec69cea6ea3f8c6528525afedc47e720539afb21a4eed230bfc7524aa9c8f387022c8ebc7658d20eff1633e9aeee74108f3fccd9f11f37a8b51e Homepage: https://cran.r-project.org/package=whoa Description: CRAN Package 'whoa' (Evaluation of Genotyping Error in Genotype-by-Sequencing Data) This is a small, lightweight package that lets users investigate the distribution of genotypes in genotype-by-sequencing (GBS) data where they expect (by and large) Hardy-Weinberg equilibrium, in order to assess rates of genotyping errors and the dependence of those rates on read depth. It implements a Markov chain Monte Carlo (MCMC) sampler using 'Rcpp' to compute a Bayesian estimate of what we call the heterozygote miscall rate for restriction-associated digest (RAD) sequencing data and other types of reduced representation GBS data. It also provides functions to generate plots of expected and observed genotype frequencies. Some background on these topics can be found in a recent paper "Recent advances in conservation and population genomics data analysis" by Hendricks et al. (2018) , and another paper describing the MCMC approach is in preparation with Gordon Luikart and Thierry Gosselin. Package: r-cran-widals Architecture: arm64 Version: 0.6.2-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 610 Depends: libc6 (>= 2.17), 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_arm64.deb Size: 460164 MD5sum: 3aaad3374495ad9b88e85c97ca4ae487 SHA1: 5d0552316caf428fe81edd9f80bea3670273727c SHA256: 6cc9fdfdb361c2a25e7b22b651ab95c0809846012756d49551c131f0c752eace SHA512: 2f78328f582a330e7f8e1619578d639e8802f2e75cbd9dcba6abcbb6ab0d76b22a20842e46c2d55558c8fae22793b5d35b8c004e46b225954532925ed050db4c Homepage: https://cran.r-project.org/package=widals Description: CRAN Package 'widals' (Weighting by Inverse Distance with Adaptive Least Squares) Computationally easy modeling, interpolation, forecasting of massive temporal-spacial data. Package: r-cran-wienr Architecture: arm64 Version: 0.3-15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 603 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_arm64.deb Size: 349446 MD5sum: e98814d78a1a8e605114adabd3114ad1 SHA1: be6c17b10b0ee333fffbc61d2d4ce17a1d6632f4 SHA256: 8dbff88ed51dae458f5f4394445fd0c854ab8a40882741d0349c17b957d0b0cf SHA512: ec094be358889b732b450f3c62a64d877883ef3c1132a1ce6ebc8d066ca36cb52778588689c0d80e23863c5eea6cc3b94b278bc47d72fcdae8150840cdfde823 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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We utilize L1 penalties to also reduce the complexity of the model space. This package employs the methods as described in Dunipace, Eric and Lorenzo Trippa (2020) . 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This estimation method is consist of composite likelihood method by Pakel et al. (2014) and (Non-)linear shrinkage estimation of covariance matrices by Ledoit and Wolf (2004,2015,2016). (, , ). 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Package: r-cran-yatchewtest Architecture: arm64 Version: 1.1.1-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 202 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 Suggests: r-cran-testthat Filename: pool/dists/resolute/main/r-cran-yatchewtest_1.1.1-1.ca2604.1_arm64.deb Size: 64480 MD5sum: c69df49abe8251461bcca0c81168a497 SHA1: 9763a7678a606a717123dde84f81d5c70dbae434 SHA256: d8c2d8f1c5a4b5d34e7f3476be96f44d4adc1c3138af100d19a00e95acb57ba8 SHA512: d6a9cde4857c82430984373212105e67fe756d51f3d302afbcc791ab046e1ca428e05d3ef7a888178f84c67f2ef3d74689c546b1449b98364d79a00e4421c4bf Homepage: https://cran.r-project.org/package=YatchewTest Description: CRAN Package 'YatchewTest' (Yatchew (1997), De Chaisemartin & D'Haultfoeuille (2024)Linearity Test) Test of linearity originally proposed by Yatchew (1997) and improved by de Chaisemartin & D'Haultfoeuille (2024) to be robust under heteroskedasticity. 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Koo, B., La Vecchia, D., & Linton, O. B. (2021) describe an application of this package using the Center for Research in Security Prices (CRSP) Bond Data and document its implementation. Package: r-cran-ymd Architecture: arm64 Version: 0.1.5-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1795 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-ymd_0.1.5-1.ca2604.1_arm64.deb Size: 603050 MD5sum: e490208fd30c8761d4374e76acebb80d SHA1: 83a8b2cde1aa1aabdd0ae1f2483d7a61f2f1399a SHA256: 4f78f6a8936560fa95e338fcb20822501651ea79fd4c9a3711ce739162c6bc99 SHA512: 061d823a50f9a5cdbc23d4ad54a374161b77986dd183870c8414309191295ad3de8b3f334c911ece0d95696f695da77da7802b179e328f3413615239c4666c6a Homepage: https://cran.r-project.org/package=ymd Description: CRAN Package 'ymd' (Parse 'YMD' Format Number or String to Date) Convert 'YMD' format number or string to Date efficiently, using Rust's standard library. It also provides helper functions to handle Date, e.g., quick finding the beginning or end of the given period, adding months to Date, etc. Package: r-cran-ypbp Architecture: arm64 Version: 0.0.1-1.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-cran-rcppparallel (>= 5.1.11-2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-survival, r-cran-formula, r-cran-mass, r-cran-rcpp, r-cran-rstan, r-cran-rstantools, r-cran-bh, r-cran-rcppeigen, r-cran-stanheaders Suggests: r-cran-knitr, r-cran-testthat Filename: pool/dists/resolute/main/r-cran-ypbp_0.0.1-1.ca2604.1_arm64.deb Size: 694620 MD5sum: 4baa67874b08adb9dfb6f0b0dae5e77c SHA1: 53e6fb1c461209959aab70e1fae8b32a7c9d1a84 SHA256: a612abec0dc8e076ecc24ee9e5a155e9a45e8cefd5229a507f18fc89916a2502 SHA512: 06e27ad409b6e80dbe646338ea4713f4794d69063f6e0078aa0cc509a616446e7c9591a6886f55bf3efdeca79e09783d816ac08fca206b1b79a7d2969db45381 Homepage: https://cran.r-project.org/package=YPBP Description: CRAN Package 'YPBP' (Yang and Prentice Model with Baseline Distribution Modeled byBernstein Polynomials) Semiparametric modeling of lifetime data with crossing survival curves via Yang and Prentice model with baseline hazard/odds modeled with Bernstein polynomials. Details about the model can be found in Demarqui et al. (2019) . Model fitting can be carried out via both maximum likelihood and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times. Package: r-cran-ypinterimtesting Architecture: arm64 Version: 1.0.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 231 Depends: libc6 (>= 2.17), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-rcpp, r-cran-mass Filename: pool/dists/resolute/main/r-cran-ypinterimtesting_1.0.3-1.ca2604.1_arm64.deb Size: 87600 MD5sum: 8c82d7fc6c126ed04a36483dc137d9c0 SHA1: bfc6c95698ed66569dcc7a9babb2adf1ae36248f SHA256: b75ea6cadafac1209c1f20aaeb15d4260a451c8c305e6d18afcacd6a88f5753c SHA512: b2b7214bc6ee34ca5959bd01e7cc39d327a3843694d95c8e6ae02d60bea2b36b1cb38e3f5d74e281bde02ebef7b77cd23b6d49a50d8b75b1df9317eb8c06c346 Homepage: https://cran.r-project.org/package=YPInterimTesting Description: CRAN Package 'YPInterimTesting' (Interim Monitoring Using Adaptively Weighted Log-Rank Test inClinical Trials) For any spending function specified by the user, this package provides corresponding boundaries for interim testing using the adaptively weighted log-rank test developed by Yang and Prentice (2010 ). 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Details about the model can be found in Demarqui and Mayrink (2019) . Model fitting carried out via likelihood-based and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times. 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Package: r-cran-zoo Architecture: arm64 Version: 1.8-15-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1390 Depends: libc6 (>= 2.17), r-base-core (>= 4.5.0), r-api-4.0, r-cran-lattice Suggests: r-cran-aer, r-cran-coda, r-cran-chron, r-cran-ggplot2, r-cran-mondate, r-cran-scales, r-cran-stinepack, r-cran-strucchange, r-cran-timedate, r-cran-timeseries, r-cran-tinyplot, r-cran-tis, r-cran-tseries, r-cran-xts Filename: pool/dists/resolute/main/r-cran-zoo_1.8-15-1.ca2604.1_arm64.deb Size: 1024660 MD5sum: 4b960571d32dbde0cb80e1a019b8b9b1 SHA1: 22fc25d65f7db60a21ee0d3506a895d31328bd19 SHA256: 05bc35e0d8153de46841f2f0306e399c15d8700dc9f5cbdc86f9517bb6ad41ea SHA512: 84de8450faa2643d30d1aab2e2c4f14e4a57bcc92fa118139dc15aa5c40b6506f72b05f9d1abc0c5a53cb400aeb78d6b63a275985ab94219f54ebd4bec222e5b Homepage: https://cran.r-project.org/package=zoo Description: CRAN Package 'zoo' (S3 Infrastructure for Regular and Irregular Time Series (Z'sOrdered Observations)) An S3 class with methods for totally ordered indexed observations. It is particularly aimed at irregular time series of numeric vectors/matrices and factors. zoo's key design goals are independence of a particular index/date/time class and consistency with ts and base R by providing methods to extend standard generics. Package: r-cran-zoomerjoin Architecture: arm64 Version: 0.2.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 2415 Depends: libc6 (>= 2.39), libgcc-s1 (>= 4.2), r-base-core (>= 4.5.0), r-api-4.0, r-cran-collapse, r-cran-dplyr, r-cran-tibble, r-cran-tidyr, r-cran-rlang Suggests: r-cran-babynames, r-cran-fuzzyjoin, r-cran-covr, r-cran-igraph, r-cran-knitr, r-cran-microbenchmark, r-cran-profmem, r-cran-purrr, r-cran-rmarkdown, r-cran-stringdist, r-cran-testthat, r-cran-tidyverse, r-cran-vdiffr Filename: pool/dists/resolute/main/r-cran-zoomerjoin_0.2.3-1.ca2604.1_arm64.deb Size: 839424 MD5sum: 68247ad859aef252777067c07a5fb26d SHA1: 17905da3a3bcf35b9c8e802ed5ed04cb64114fb5 SHA256: 6d9c6c3e17abaee77f11f1562dd09a91e93dba9dbbb19bd6e45e4089e7081767 SHA512: 063fc83bf22ebdd6128f1feb90c01216564a5a9d6adaca4f177241e5a2806af16c0f2359ffeeeb5c747e7947fa5a7cb8a922785e86ddf9783c7d431d0a8405a8 Homepage: https://cran.r-project.org/package=zoomerjoin Description: CRAN Package 'zoomerjoin' (Superlatively Fast Fuzzy Joins) Empowers users to fuzzily-merge data frames with millions or tens of millions of rows in minutes with low memory usage. The package uses the locality sensitive hashing algorithms developed by Datar, Immorlica, Indyk and Mirrokni (2004) , and Broder (1998) to avoid having to compare every pair of records in each dataset, resulting in fuzzy-merges that finish in linear time. Package: r-cran-ztpln Architecture: arm64 Version: 0.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 510 Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libstdc++6 (>= 14), r-base-core (>= 4.5.0), r-api-4.0, r-cran-distributionutils, r-cran-rcpp, r-cran-mixtools, r-cran-rcppeigen, r-cran-rcppnumerical Suggests: r-cran-knitr, r-cran-dplyr, r-cran-ggplot2, r-cran-rmarkdown, r-cran-testthat, r-cran-tidyr Filename: pool/dists/resolute/main/r-cran-ztpln_0.1.3-1.ca2604.1_arm64.deb Size: 210348 MD5sum: 4457eef2301f94c686f9ab8ff53f8bf7 SHA1: c31eddcaf26d72690bb47199486066da7e743a66 SHA256: 7672069ea5c933bb823283143e9edf3ca2e8794f5267ca2c74e651749fc0ae89 SHA512: 3ce1a624ffbbb475c81d78d76f01ba1b92065243436151054ea40ac38a8a1ccdc829a133981b21b204e9cbeb8e0c55a9eafa17415665c4353e4722bfb658e307 Homepage: https://cran.r-project.org/package=ztpln Description: CRAN Package 'ztpln' (Zero-Truncated Poisson Lognormal Distribution) Functions for obtaining the density, random variates and maximum likelihood estimates of the Zero-truncated Poisson lognormal distribution and their mixture distribution. Package: r-cran-zvcv Architecture: arm64 Version: 2.1.3-1.ca2604.1 Priority: optional Section: gnu-r Maintainer: Dirk Eddelbuettel Installed-Size: 1038 Depends: libblas3 | libblas.so.3, libc6 (>= 2.29), libgcc-s1 (>= 4.5), 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-abind, r-cran-mvtnorm, r-cran-rlinsolve, r-cran-magrittr, r-cran-dplyr, r-cran-rcpparmadillo, r-cran-bh Suggests: r-cran-partitions, r-cran-ggplot2, r-cran-ggthemes Filename: pool/dists/resolute/main/r-cran-zvcv_2.1.3-1.ca2604.1_arm64.deb Size: 504320 MD5sum: 49f1d71d81edae4aeae6e4d19c20b919 SHA1: 510f6f9332beac5c535235af21d8f09086595ef3 SHA256: 6dcd0cad1f90faac969bbc015c89022c19a5b582dc1bbc392d04c292afc0230a SHA512: 944ca06c4d08855ece62bb7a73d0e90b32be979491723978c896c2d2bc18a249b51efb008e0a8ce6f77c10006415ca7fad849d401afbb8a75611886b23d1c7d3 Homepage: https://cran.r-project.org/package=ZVCV Description: CRAN Package 'ZVCV' (Zero-Variance Control Variates) Stein control variates can be used to improve Monte Carlo estimates of expectations when the derivatives of the log target are available. This package implements a variety of such methods, including zero-variance control variates (ZV-CV, Mira et al. (2013) ), regularised ZV-CV (South et al., 2023 ), control functionals (CF, Oates et al. (2017) ) and semi-exact control functionals (SECF, South et al., 2022 ). ZV-CV is a parametric approach that is exact for (low order) polynomial integrands with Gaussian targets. CF is a non-parametric alternative that offers better than the standard Monte Carlo convergence rates. SECF has both a parametric and a non-parametric component and it offers the advantages of both for an additional computational cost. Functions for applying ZV-CV and CF to two estimators for the normalising constant of the posterior distribution in Bayesian statistics are also supplied in this package. The basic requirements for using the package are a set of samples, derivatives and function evaluations.